text:
A major focus of our lab is understanding the effects of genetic
variation on molecular phenotypes and human disease. We develop
methods for integrating diverse functional genomic datasets of
transcription, chromatin modifications, regulator binding, and their
changes across multiple conditions to interpret genetic associations,
identify causal variants, and predict the effects of genetic
Li, Kellis
Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.
Wang, Tucker, Rizki, Mills, Krijger, de Wit, Subramanian, Bartell, Nguyen, Ye, Leyton-Mange, Dolmatova, van der Harst, de Laat, Ellinor, Newton-Cheh, Milan, Kellis, Boyer
Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.
Smith, Felix, Morrison, Kalogeropoulos, Trompet, Wilk, Gidlof, Wang, Morley, Mendelson, Joehanes, Ligthart, Shan, Bis, Wang, Sjogren, Ngwa, Brandimarto, Stott, Aguilar, Rice, Sesso, Demissie, Buckley, Taylor, Ford, Yao, Liu C; CHARGE-SCD consortium; EchoGen consortium; QT-IGC consortium; CHARGE-QRS, Sotoodehnia, van der Harst, Stricker, Kritchevsky, Liu, Gaziano, Hofman, Moravec, Uitterlinden, Kellis, van Meurs, Margulies, Dehghan, Levy, Olde, Psaty, Cupples, Jukema, Djousse, Franco, Boerwinkle, Boyer, Newton-Cheh, Butler, Vasan, Cappola, Smith
Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure
Barrera, Vedenko, Kurland, Rogers, Gisselbrecht, Rossin, Woodard, Mariani, Kock, Inukai, Siggers, Shokri, Gordân, Sahni, Cotsapas, Hao, Yi, Kellis, Daly, Vidal, Hill, Bulyk
Sequencing of exomes and genomes has revealed abundant genetic variation affecting the coding sequences of human transcription factors (TFs), but the consequences of such variation remain largely unexplored. We developed a computational, structure-based approach to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activity across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases. We found 77 variants in 28 genes that affect DNA binding affinity or specificity and identified thousands of rare alleles likely to alter the DNA binding activity of human sequence-specific TFs. Our results suggest that most individuals have unique repertoires of TF DNA binding activities, which may contribute to phenotypic variation.
Marbach, Lamparter, Quon, Kellis, Kutalik, Bergmann.
Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants-including variants that do not reach genome-wide significance-often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).
Bekelis, Kerley-Hamilton, Teegarden, Tomlinson, Kuintzle, Simmons, Singer, Roberts, Kellis, Hendrix.
The molecular mechanisms behind cerebral aneurysm formation and rupture remain poorly understood. In the past decade, microRNAs (miRNAs) have been shown to be key regulators in a host of biological processes. They are noncoding RNA molecules, approximately 21 nucleotides long, that posttranscriptionally inhibit mRNAs by attenuating protein translation and promoting mRNA degradation. The miRNA and mRNA interactions and expression levels in cerebral aneurysm tissue from human subjects were profiled. A prospective case-control study was performed on human subjects to characterize the differential expression of mRNA and miRNA in unruptured cerebral aneurysms in comparison with control tissue (healthy superficial temporal arteries [STA]). Ion Torrent was used for deep RNA sequencing. Affymetrix miRNA microarrays were used to analyze miRNA expression, whereas NanoString nCounter technology was used for validation of the identified targets. Overall, 7 unruptured cerebral aneurysm and 10 STA specimens were collected. Several differentially expressed genes were identified in aneurysm tissue, with MMP-13 (fold change 7.21) and various collagen genes (COL1A1, COL5A1, COL5A2) being among the most upregulated. In addition, multiple miRNAs were significantly differentially expressed, with miR-21 (fold change 16.97) being the most upregulated, and miR-143-5p (fold change -11.14) being the most downregulated. From these, miR-21, miR-143, and miR-145 had several significantly anticorrelated target genes in the cohort that are associated with smooth muscle cell function, extracellular matrix remodeling, inflammation signaling, and lipid accumulation. All these processes are crucial to the pathophysiology of cerebral aneurysms. This analysis identified differentially expressed genes and miRNAs in unruptured human cerebral aneurysms, suggesting the possibility of a role for miRNAs in aneurysm formation. Further investigation for their importance as therapeutic targets is needed
Ward, Kellis
More than 90% of common variants associated with complex traits do not affect proteins directly, but instead the circuits that control gene expression. This has increased the urgency of understanding the regulatory genome as a key component for translating genetic results into mechanistic insights and ultimately therapeutics. To address this challenge, we developed HaploReg (http://compbio.mit.edu/HaploReg) to aid the functional dissection of genome-wide association study (GWAS) results, the prediction of putative causal variants in haplotype blocks, the prediction of likely cell types of action, and the prediction of candidate target genes by systematic mining of comparative, epigenomic and regulatory annotations. Since first launching the website in 2011, we have greatly expanded HaploReg, increasing the number of chromatin state maps to 127 reference epigenomes from ENCODE 2012 and Roadmap Epigenomics, incorporating regulator binding data, expanding regulatory motif disruption annotations, and integrating expression quantitative trait locus (eQTL) variants and their tissue-specific target genes from GTEx, Geuvadis, and other recent studies. We present these updates as HaploReg v4, and illustrate a use case of HaploReg for attention deficit hyperactivity disorder (ADHD)-associated SNPs with putative brain regulatory mechanisms.
Yang, Huang, Petralia, Long, Zhang, Argmann, Zhao, Mobbs, Schadt, Zhu, Tu; GTEx Consortium.
Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger "co-aging" than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.
Claussnitzer, Dankel, Kim, Quon, Meuleman, Haugen, Glunk, Sousa, Beaudry, Puviindran, Abdennur, Liu, Svensson, Hsu, Drucker, Mellgren, Hui, Hauner, Kellis
Genome-wide association studies can be used to identify disease-relevant genomic regions, but interpretation of the data is challenging. The FTO region harbors the strongest genetic association with obesity, yet the mechanistic basis of this association remains elusive. We examined epigenomic data, allelic activity, motif conservation, regulator expression, and gene coexpression patterns, with the aim of dissecting the regulatory circuitry and mechanistic basis of the association between the FTO region and obesity. We validated our predictions with the use of directed perturbations in samples from patients and from mice and with endogenous CRISPR-Cas9 genome editing in samples from patients. Our data indicate that the FTO allele associated with obesity represses mitochondrial thermogenesis in adipocyte precursor cells in a tissue-autonomous manner. The rs1421085 T-to-C single-nucleotide variant disrupts a conserved motif for the ARID5B repressor, which leads to derepression of a potent preadipocyte enhancer and a doubling of IRX3 and IRX5 expression during early adipocyte differentiation. This results in a cell-autonomous developmental shift from energy-dissipating beige (brite) adipocytes to energy-storing white adipocytes, with a reduction in mitochondrial thermogenesis by a factor of 5, as well as an increase in lipid storage. Inhibition of Irx3 in adipose tissue in mice reduced body weight and increased energy dissipation without a change in physical activity or appetite. Knockdown of IRX3 or IRX5 in primary adipocytes from participants with the risk allele restored thermogenesis, increasing it by a factor of 7, and overexpression of these genes had the opposite effect in adipocytes from nonrisk-allele carriers. Repair of the ARID5B motif by CRISPR-Cas9 editing of rs1421085 in primary adipocytes from a patient with the risk allele restored IRX3 and IRX5 repression, activated browning expression programs, and restored thermogenesis, increasing it by a factor of 7. Our results point to a pathway for adipocyte thermogenesis regulation involving ARID5B, rs1421085, IRX3, and IRX5, which, when manipulated, had pronounced pro-obesity and anti-obesity effects
Chibnik, Yu, Eaton, Srivastava, Schneider, Kellis, Bennett, De Jager
We explore the role of DNA methylation in Alzheimer's disease (AD). To elucidate where DNA methylation falls along the causal pathway linking risk factors to disease, we examine causal models to assess its role in the pathology of AD. DNA methylation profiles were generated in 740 brain samples using the Illumina HumanMet450K beadset. We focused our analysis on CpG sites from 11 AD susceptibility gene regions. The primary outcome was a quantitative measure of neuritic amyloid plaque (NP), a key early element of AD pathology. We tested four causal models: (1) independent associations, (2) CpG mediating the association of a variant, (3) reverse causality, and (4) genetic variant by CpG interaction. Six genes regions (17 CpGs) showed evidence of CpG associations with NP, independent of genetic variation - BIN1 (5), CLU (5), MS4A6A (3), ABCA7 (2), CD2AP (1), and APOE (1). Together they explained 16.8% of the variability in NP. An interaction effect was seen in the CR1 region for two CpGs, cg10021878 (P = 0.01) and cg05922028 (P = 0.001), in relation to NP. In both cases, subjects with the risk allele rs6656401(AT) (/) (AA) display more methylation being associated with more NP burden, whereas subjects with the rs6656401(TT) protective genotype have an inverse association with more methylation being associated with less NP. These observations suggest that, within known AD susceptibility loci, methylation is related to pathologic processes of AD and may play a largely independent role by influencing gene expression in AD susceptibility loci.
GTEx Consortium; Ardlie, Deluca, Segrč, Sullivan, Young, Gelfand, Trowbridge, Maller, Tukiainen, Lek, Ward, Kheradpour, Iriarte, Meng, Palmer, Esko, Winckler, Hirschhorn, Kellis, MacArthur, Getz, Shabalin, Li, Zhou, Nobel, Rusyn, Wright, Lappalainen, Ferreira, Ongen, Rivas, Battle, Mostafavi, Monlong, Sammeth, Melé, Reverter, Goldmann, Koller, Guigó, McCarthy, Dermitzakis, Gamazon, Im, Konkashbaev, Nicolae, Cox, Flutre, Wen, Stephens, Pritchard, Tu, Zhang, Huang, Long, Lin, Yang, Zhu, Liu, Brown, Mestichelli, Tidwell, Lo, Salvatore, Shad, Thomas, Lonsdale, Moser, Gillard, Karasik, Ramsey, Choi, Foster, Syron, Fleming, Magazine, Hasz, Walters, Bridge, Miklos, Sullivan, Barker, Traino, Mosavel, Siminoff, Valley, Rohrer, Jewell, Branton, Sobin, Barcus, Qi, McLean, Hariharan, Um, Wu, Tabor, Shive, Smith, Buia, Undale, Robinson, Roche, Valentino, Britton, Burges, Bradbury, Hambright, Seleski, Korzeniewski, Erickson, Marcus, Tejada, Taherian, Lu, Basile, Mash, Volpi, Struewing, Temple, Boyer, Colantuoni, Little, Koester, Carithers, Moore, Guan, Compton, Sawyer, Demchok, Vaught, Rabiner, Lockhart, Ardlie, Getz, Wright, Kellis, Volpi, Dermitzakis
Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues
Onengut-Gumuscu, Chen, Burren, Cooper, Quinlan, Mychaleckyj, Farber, Bonnie, Szpak, Schofield, Achuthan, Guo, Fortune, Stevens, Walker, Ward, Kundaje, Kellis, Daly, Barrett, Cooper, Deloukas, Todd, Wallace, Concannon, Rich
Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions, finding major pathways contributing to risk, with some loci shared across immune disorders. To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new T1D-associated regions (P < 5 × 10(-8)). A comparative analysis with 15 immune diseases showed that T1D is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new T1D risk loci. Using a Bayesian approach, we defined credible sets for the T1D-associated SNPs. The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34(+) stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal
Roadmap Epigenomics Consortium, Kundaje, Meuleman, Ernst, Bilenky, Yen, Heravi-Moussavi, Kheradpour, Zhang, Wang, Ziller, Amin, Whitaker, Schultz, Ward, Sarkar, Quon, Sandstrom, Eaton, Wu, Pfenning, Wang, Claussnitzer, Liu, Coarfa, Harris, Shoresh, Epstein, Gjoneska, Leung, Xie, Hawkins, Lister, Hong, Gascard, Mungall, Moore, Chuah, Tam, Canfield, Hansen, Kaul, Sabo, Bansal, Carles, Dixon, Farh, Feizi, Karlic, Kim, Kulkarni, Li, Lowdon, Elliott, Mercer, Neph, Onuchic, Polak, Rajagopal, Ray, Sallari, Siebenthall, Sinnott-Armstrong, Stevens, Thurman, Wu, Zhang, Zhou, Beaudet, Boyer, De Jager, Farnham, Fisher, Haussler, Jones, Li, Marra, McManus, Sunyaev, Thomson, Tlsty, Tsai, Wang, Waterland, Zhang, Chadwick, Bernstein, Costello, Ecker, Hirst, Meissner, Milosavljevic, Ren, Stamatoyannopoulos, Wang, Kellis
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease
Gjoneska, Pfenning, Mathys, Quon, Kundaje, Tsai, Kellis
Alzheimer's disease (AD) is a severe age-related neurodegenerative disorder characterized by accumulation of amyloid-beta plaques and neurofibrillary tangles, synaptic and neuronal loss, and cognitive decline. Several genes have been implicated in AD, but chromatin state alterations during neurodegeneration remain uncharacterized. Here we profile transcriptional and chromatin state dynamics across early and late pathology in the hippocampus of an inducible mouse model of AD-like neurodegeneration. We find a coordinated downregulation of synaptic plasticity genes and regulatory regions, and upregulation of immune response genes and regulatory regions, which are targeted by factors that belong to the ETS family of transcriptional regulators, including PU.1. Human regions orthologous to increasing-level enhancers show immune-cell-specific enhancer signatures as well as immune cell expression quantitative trait loci, while decreasing-level enhancer orthologues show fetal-brain-specific enhancer activity. Notably, AD-associated genetic variants are specifically enriched in increasing-level enhancer orthologues, implicating immune processes in AD predisposition. Indeed, increasing enhancers overlap known AD loci lacking protein-altering variants, and implicate additional loci that do not reach genome-wide significance. Our results reveal new insights into the mechanisms of neurodegeneration and establish the mouse as a useful model for functional studies of AD regulatory regions
Sugathan, Biagioli, Golzio, Erdin, Blumenthal, Manavalan, Ragavendran, Brand, Lucente, Miles, Sheridan, Stortchevoi, Kellis, Haggarty, Katsanis, Gusella, Talkowski
Truncating mutations of chromodomain helicase DNA-binding protein 8 (CHD8), and of many other genes with diverse functions, are strong-effect risk factors for autism spectrum disorder (ASD), suggesting multiple mechanisms of pathogenesis. We explored the transcriptional networks that CHD8 regulates in neural progenitor cells (NPCs) by reducing its expression and then integrating transcriptome sequencing (RNA sequencing) with genome-wide CHD8 binding (ChIP sequencing). Suppressing CHD8 to levels comparable with the loss of a single allele caused altered expression of 1,756 genes, 64.9% of which were up-regulated. CHD8 showed widespread binding to chromatin, with 7,324 replicated sites that marked 5,658 genes. Integration of these data suggests that a limited array of direct regulatory effects of CHD8 produced a much larger network of secondary expression changes. Genes indirectly down-regulated (i.e., without CHD8-binding sites) reflect pathways involved in brain development, including synapse formation, neuron differentiation, cell adhesion, and axon guidance, whereas CHD8-bound genes are strongly associated with chromatin modification and transcriptional regulation. Genes associated with ASD were strongly enriched among indirectly down-regulated loci (P < 10(-8)) and CHD8-bound genes (P = 0.0043), which align with previously identified coexpression modules during fetal development. We also find an intriguing enrichment of cancer-related gene sets among CHD8-bound genes (P < 10(-10)). In vivo suppression of chd8 in zebrafish produced macrocephaly comparable to that of humans with inactivating mutations. These data indicate that heterozygous disruption of CHD8 precipitates a network of gene-expression changes involved in neurodevelopmental pathways in which many ASD-associated genes may converge on shared mechanisms of pathogenesis
De Jager, Srivastava, Lunnon, Burgess, Schalkwyk, Yu, Eaton, Keenan, Ernst, McCabe, Tang, Raj, Replogle, Brodeur, Gabriel, Chai, Younkin, Younkin, Zou, Szyf, Epstein, Schneider, Bernstein, Meissner, Ertekin-Taner, Chibnik, Kellis, Mill, Bennett
We used a collection of 708 prospectively collected autopsied brains to assess the methylation state of the brain's DNA in relation to Alzheimer's disease (AD). We found that the level of methylation at 71 of the 415,848 interrogated CpGs was significantly associated with the burden of AD pathology, including CpGs in the ABCA7 and BIN1 regions, which harbor known AD susceptibility variants. We validated 11 of the differentially methylated regions in an independent set of 117 subjects. Furthermore, we functionally validated these CpG associations and identified the nearby genes whose RNA expression was altered in AD: ANK1, CDH23, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2. Our analyses suggest that these DNA methylation changes may have a role in the onset of AD given that we observed them in presymptomatic subjects and that six of the validated genes connect to a known AD susceptibility gene network
Arking, Pulit, Crotti, vanderHarst, Munroe, Koopmann, Sotoodehnia, Rossin, Morley, Wang, Johnson, Lundby, Gudbjartsson, Noseworthy, Eijgelsheim, Bradford, Tarasov, Dörr, Müller-Nurasyid, Lahtinen, Nolte, Smith, Bis, Isaacs, Newhouse, Evans, Post, Waggott, Lyytikainen, Hicks, Eisele, Ellinghaus, Hayward, Navarro, Ulivi, Tanaka, Tester, Chatel, Gustafsson, Kumari, Morris, Naluai, Padmanabhan, Kluttig, Strohmer, Panayiotou, Torres, Knoflach, Hubacek, Slowikowski, Raychaudhuri, Kumar, Harris, Launer, Shuldiner, Alonso, Bader, Ehret, Huang, Kao, Strait, Macfarlane, Brown, Caulfield, Samani, Kronenberg, Willeit, Smith, Greiser, Meyer, Werdan, Carella, Zelante, Heckbert, Psaty, Rotter, Kolcic, Polasek, Wright, Griffin, Daly, Arnar, Hólm, Thorsteinsdottir, Denny, Roden, Zuvich, Emilsson, Plump, Larson, O'Donnell, Yin, Bobbo, D'Adamo, Iorio, Sinagra, Carracedo, Cummings, Nalls, Jula, Kontula, Marjamaa, Oikarinen, Perola, Porthan, Erbel, Hoffmann, Jöckel, Kalsch, Nöthen, den, Loos, Thelle, Gieger, Meitinger, Perz, Peters, Prucha, Sinner, Waldenberger, de, Franke, van, Beckmann, Martens, Bardai, Hofman, Wilde, Behr, Dalageorgou, Giudicessi, Medeiros-Domingo, Barc, Kyndt, Probst, Ghidoni, Insolia, Hamilton, Scherer, Brandimarto, Margulies, Moravec, Fabiola, Fuchsberger, O'Connell, Lee, Watt, Campbell, Wild, ElMokhtari, Frey, Asselbergs, Leach, Navis, vandenBerg, vanVeldhuisen, Kellis, Krijthe, Franco, Hofman, Kors, Uitterlinden, Witteman, Kedenko, Lamina, Oostra, Abecasis, Lakatta, Mulas, Orrú, Schlessinger, Uda, Markus, Völker, Snieder, Spector, Arnlöv, Lind, Sundström, Syvanen, Kivimaki, Kahönen, Mononen, Raitakari, Viikari, Adamkova, Kiechl, Brion, Nicolaides, Paulweber, Haerting, Dominiczak, Nyberg, Whincup, Hingorani, Schott, Bezzina, Ingelsson, Ferrucci, Gasparini, Wilson, Rudan, Franke, Mühleisen, Pramstaller, Lehtimaki, Paterson, Parsa, Liu, vanDuijn, Siscovick, Gudnason, Jamshidi, Salomaa, Felix, Sanna, Ritchie, Stricker, Stefansson, Boyer, Cappola, Olsen, Lage, Schwartz, Kaab, Chakravarti, Ackerman, Pfeufer, deBakker, Newton-Cheh
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ~8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
Lee, Ye, Villani, Raj, Li, Eisenhaure, Imboywa, Chipendo, Ran, Slowikowski, Ward, Raddassi, McCabe, Lee, Frohlich, Hafler, Kellis, Raychaudhuri, Zhang, Stranger, Benoist, De Jager, Regev, Hacohen
Little is known about how human genetic variation affects the responses to environmental stimuli in the context of complex diseases. Experimental and computational approaches were applied to determine the effects of genetic variation on the induction of pathogen-responsive genes in human dendritic cells. We identified 121 common genetic variants associated in cis with variation in expression responses to Escherichia coli lipopolysaccharide, influenza, or interferon-beta (IFN-beta). We localized and validated causal variants to binding sites of pathogen-activated STAT (signal transducer and activator of transcription) and IRF (IFN-regulatory factor) transcription factors. We also identified a common variant in IRF7 that is associated in trans with type I IFN induction in response to influenza infection. Our results reveal common alleles that explain interindividual variation in pathogen sensing and provide functional annotation for genetic variants that alter susceptibility to inflammatory diseases.
Kasowski, Kyriazopoulou-Panagiotopoulou, Grubert, Zaugg, Kundaje, Liu, Boyle, Zhang, Zakharia, Spacek, Li, Xie, Olarerin-George, Steinmetz, Hogenesch, Kellis, Batzoglou, Snyder
The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.
The GTEx Consortium
Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority
of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not
correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to
many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a
resource database and associated tissue bank for the scientific community to study the relationship between genetic
variation and gene expression in human tissues
Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and molecular quantitative trait loci all provide complementary information about the function of noncoding sequences. These functional maps can help with prioritizing variants on risk haplotypes, filtering mutations encountered in the clinic and performing systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable data-set integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis and treatment
Although only 5% of the human genome is conserved across mammals, a substantially larger portion is biochemically active, raising the question of whether the additional elements evolve neutrally or confer a lineage-specific fitness advantage. To address this question, we integrate human variation information from the 1000 Genomes Project and activity data from the ENCODE Project. A broad range of transcribed and regulatory nonconserved elements show decreased human diversity, suggesting lineage-specific purifying selection. Conversely, conserved elements lacking activity show increased human diversity, suggesting that some recently became nonfunctional. Regulatory elements under human constraint in nonconserved regions were found near color vision and nerve-growth genes, consistent with purifying selection for recently evolved functions. Our results suggest continued turnover in regulatory regions, with at least an additional 4% of the human genome subject to lineage-specific constraint.
Wiggs, Yaspan, Hauser, Kang, Allingham, Olson, Abdrabou, Fan, Wang, Brodeur, Budenz, Caprioli, Crenshaw, Crooks, Delbono, Doheny, Friedman, Gaasterland, Gaasterland, Laurie, Lee, Lichter, Loomis, Liu, Medeiros, McCarty, Mirel, Moroi, Musch, Realini, Rozsa, Schuman, Scott, Singh, Stein, Trager, Vanveldhuisen, Vollrath, Wollstein, Yoneyama, Zhang, Weinreb, Ernst, Kellis, Masuda, Zack, Richards, Pericak-Vance, Pasquale, Haines
Optic nerve degeneration caused by glaucoma is a leading cause of blindness worldwide. Patients affected by the normal-pressure form of glaucoma are more likely to harbor risk alleles for glaucoma-related optic nerve disease. We have performed a meta-analysis of two independent genome-wide association studies for primary open angle glaucoma (POAG) followed by a normal-pressure glaucoma (NPG, defined by intraocular pressure (IOP) less than 22 mmHg) subgroup analysis. The single-nucleotide polymorphisms that showed the most significant associations were tested for association with a second form of glaucoma, exfoliation-syndrome glaucoma. The overall meta-analysis of the GLAUGEN and NEIGHBOR dataset results (3,146 cases and 3,487 controls) identified significant associations between two loci and POAG: the CDKN2BAS region on 9p21 (rs2157719 [G], OR=0.69 [95%CI 0.63-0.75], p=1.86x10(-18)), and the SIX1/SIX6 region on chromosome 14q23 (rs10483727 [A], OR=1.32 [95%CI 1.21-1.43], p=3.87x10(-11)). In sub-group analysis two loci were significantly associated with NPG: 9p21 containing the CDKN2BAS gene (rs2157719 [G], OR=0.58 [95% CI 0.50-0.67], p=1.17x10(-12)) and a probable regulatory region on 8q22 (rs284489 [G], OR=0.62 [95% CI 0.53-0.72], p=8.88x10(-10)). Both NPG loci were also nominally associated with a second type of glaucoma, exfoliation syndrome glaucoma (rs2157719 [G], OR=0.59 [95% CI 0.41-0.87], p=0.004 and rs284489 [G], OR=0.76 [95% CI 0.54-1.06], p=0.021), suggesting that these loci might contribute more generally to optic nerve degeneration in glaucoma. Because both loci influence transforming growth factor beta (TGF-beta) signaling, we performed a genomic pathway analysis that showed an association between the TGF-beta pathway and NPG (permuted p=0.009). These results suggest that neuro-protective therapies targeting TGF-beta signaling could be effective for multiple forms of glaucoma.
The resolution of genome-wide association studies (GWAS) is limited by the linkage disequilibrium (LD) structure of the population being studied. Selecting the most likely causal variants within an LD block is relatively straightforward within coding sequence, but is more difficult when all variants are intergenic. Predicting functional non-coding sequence has been recently facilitated by the availability of conservation and epigenomic information. We present HaploReg, a tool for exploring annotations of the non-coding genome among the results of published GWAS or novel sets of variants. Using LD information from the 1000 Genomes Project, linked SNPs and small indels can be visualized along with their predicted chromatin state in nine cell types, conservation across mammals and their effect on regulatory motifs. Sets of SNPs, such as those resulting from GWAS, are analyzed for an enrichment of cell type-specific enhancers. HaploReg will be useful to researchers developing mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation. The HaploReg database is available at http://compbio.mit.edu/HaploReg/
Lindblad-Toh, Garber, Zuk, Lin, Parker, Washietl, Kheradpour, Ernst, Jordan, Mauceli, Ward, Lowe, Holloway, Clamp, Gnerre, Alfoldi, Beal, Chang, Clawson, Palma, Fitzgerald, Flicek, Guttman, Hubisz, Jaffe, Jungreis, Kostka, Lara, Martins, Massingham, Moltke, Raney, Rasmussen, Stark, Vilella, Wen, Xie, Zody, Worley, Kovar, Muzny, Gibbs, Warren, Mardis, Weinstock, Wilson, Birney, Margulies, Herrero, Green, Haussler, Siepel, Goldman, Pollard, Pedersen, Lander, Kellis
The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering 4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for 60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease.
Ernst, Kheradpour, Mikkelsen, Shoresh, Ward, Epstein, Zhang, Wang, Issner, Coyne, Ku, Durham, Kellis*, Bernstein*
Chromatin profiling has emerged as a powerful means for annotating genomic elements and detecting regulatory activity. Here we generate and analyze a compendium of epigenomic maps for nine chromatin marks across nine cell types, in order to systematically characterize cis-regulatory elements, their cell type-specificities, and their functional interactions. We first identify recurrent combinations of histone modifications and use them to annotate diverse regulatory elements including promoters, enhancers, transcripts and insulators in each cell type. We next characterize the dynamics of these elements, revealing meaningful patterns of activity for promoter states and exquisite cell type-selectivity for enhancer states. We define multi-cell activity profiles that reflect the patterns of enhancer state activity across cell types, as well as analogous profiles for gene expression, regulatory motif enrichments, and expression of the corresponding regulators. We use correlations between these profiles to link candidate enhancers to putative target genes, to infer cell type-specific activators and repressors, and to predict and validate functional regulator binding motifs in specific chromatin states. These functional annotations and regulatory predictions enable us to revisit intergenic single-nucleotide polymorphisms (SNPs) associated with human disease in genome-wide association studies (GWAS). We find that for several diseases, topscoring SNPs are precisely positioned within enhancer elements specifically active in relevant cell types. In several cases a disease variant affects a motif instance for one of the predicted causal regulators, thus providing a potential mechanistic explanation for the disease association. Our study presents a general framework for applying multi-cell chromatin state analysis to decipher cis-regulatory connections and their role in health and disease.
To recognize the molecular basis of human biology and disease, we need a comprehensive understanding of the human genome. This requires computational methods for genome interpretation which can systematically intepret the functional elements encoded in the 4-letter DNA code. For this purpose, we have developed methods for the comprehensive annotation of proteins, RNAs and regulatory control elements encoded in the human genome. While most of these genomic elements lack distinguishing sequence features for their de novo recognition in a single genome, comparison of multiple related species provides a new evolutionary lens for genome interpretation. Exploiting genome-wide comparative genomics datasets, we recognize specific patterns of evolutionary change, or 'evolutionary signatures,' associated with each class of functional elements, and dictated by the specific constraints unique to each type of function. We have used these signatures in the human, fly, and yeast genomes to recognize protein-coding genes and exons, RNA genes and structures, microRNAs and their targets, and several types of regulatory elements, revealing thousands of new elements, and many novel biological insights.
Chen, Sturgill, Qu, Jiang, Park, Boley, Suzuki, Fletcher, Plachetzki, FitzGerald, Artieri, Atallah, Barmina, Brown, Blankenburg, Clough, Dasgupta, Gubbala, Han, Jayaseelan, Kalra, Kim, Kovar, Lee, Li, Malley, Malone, Mathew, Mattiuzzo, Munidasa, Muzny, Ongeri, Perales, Przytycka, Pu, Robinson, Thornton, Saada, Scherer, Smith, Vinson, Warner, Worley, Wu, Zou, Cherbas, Kellis, Eisen, Piano, Kionte, Fitch, Sternberg, Cutter, Duff, Hoskins, Graveley, Gibbs, Bickel, Kopp, Carninci, Celniker, Oliver, Richards.
Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community
Kellis, Wold, Snyder, Bernstein, Kundaje, Marinov, Ward, Birney, Crawford, Dekker, Dunham, Elnitski, Farnham, Feingold, Gerstein, Giddings, Gilbert, Gingeras, Green, Guigo, Hubbard, Kent, Lieb, Myers, Pazin, Ren, Stamatoyannopoulos, Weng, White, Hardison
With the completion of the human genome sequence, attention turned to identifying and annotating its functional DNA elements. As a complement to genetic and comparative genomics approaches, the Encyclopedia of DNA Elements Project was launched to contribute maps of RNA transcripts, transcriptional regulator binding sites, and chromatin states in many cell types. The resulting genome-wide data reveal sites of biochemical activity with high positional resolution and cell type specificity that facilitate studies of gene regulation and interpretation of noncoding variants associated with human disease. However, the biochemically active regions cover a much larger fraction of the genome than do evolutionarily conserved regions, raising the question of whether nonconserved but biochemically active regions are truly functional. Here, we review the strengths and limitations of biochemical, evolutionary, and genetic approaches for defining functional DNA segments, potential sources for the observed differences in estimated genomic coverage, and the biological implications of these discrepancies. We also analyze the relationship between signal intensity, genomic coverage, and evolutionary conservation. Our results reinforce the principle that each approach provides complementary information and that we need to use combinations of all three to elucidate genome function in human biology and disease.
ENCODE Project Consortium
The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.
The modENCODE Consortium, Roy, Ernst, Kharchenko, Kheradpour, Nčgre, Eaton, Landolin, Bristow, Ma, Lin, Washietl, Arshinoff, Ay, Meyer, Robine, Washington, Di Stefano, Berezikov, Brown, Brown, Candeias, Carlson, Carr, Jungreis, Marbach, Sealfon, Tolstorukov, Alekseyenko, Artieri, Boley, Booth, Brooks, Dai, Davis, Duff, Feng, Gorchakov, Gu, Henikoff, Kapranov, Li, Li, MacAlpine, Malone, Minoda, Nordman, Okamura, Perry, Powell, Riddle, Sakai, Samsonova, Sandler, Schwartz, Sher, Spokony, Sturgill, van Baren, Will, Wan, Yang, Yu, Feingold, Good, Guyer, Lowdon, Ahmad, Andrews, Berger, Bickel, Brenner, Brent, Cherbas, Elgin, Gingeras, Grossman, Hoskins, Kaufman, Kent, Kuroda, Orr-Weaver, Perrimon, Pirrotta, Posakony, Ren, Russell, Cherbas, Graveley, Lewis, Micklem, Oliver, Park, Celniker, Henikoff, Karpen, Lai, MacAlpine, Stein, White, Kellis
Several years after the initial sequencing of the genomes from human and other organisms, the vast majority of each genome remains unannotated, and it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. To address this question, the Drosophila modENCODE project has undertaken a large-scale effort to comprehensively map transcription, regulator binding, chromatin state, replication, and nucleosome properties across a developmental time-course and in multiple cell lines. Here, we report our initial integrative analysis of the first phase of the project, encompassing more than 1000 datasets generated over four years across six production centers. Our integrated annotation enabled the discovery of new proteincoding, non-coding, RNA regulatory, replication, and chromatin elements that more than triple the annotated portion of the genome. We study correlated activity patterns of these elements to infer a functional regulatory network, which we use to predict putative functions for new genes, reveal stage-specific and tissue-specific regulators, and infer predictive models of gene expression. Our results provide a reference annotation that can inform directed experimental and computational studies in Drosophila and related species, and provide a model for systematic data integration towards the comprehensive genomic and functional annotation of any genome, including the human.
ENCODE Project Consortium, Myers, Stamatoyannopoulos, Snyder, Dunham, Hardison, Bernstein, Gingeras, Kent, Birney, Wold, Crawford
The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.
Celniker, Dillon, Gerstein, Gunsalus, Henikoff, Karpen, Kellis, Lai, Lieb, MacAlpine, Micklem, Piano, Snyder, Stein, White, Waterston
Despite the successes of genomics, little is known about how genetic information produces complex organisms. A look at the crucial functional elements of fly and worm genomes could change that.
Stark, Lin, Kheradpour, Pedersen, Parts, Carlson, Crosby, Rasmussen, Roy, Deoras, Ruby, Brennecke, Hodges, Hinrichs, Caspi, Paten, Park, Han, Maeder, Polansky, Robson, Aerts, van, Hassan, Gilbert, Eastman, Rice, Weir, Hahn, Park, Dewey, Pachter, Kent, Haussler, Lai, Bartel, Hannon, Kaufman, Eisen, Clark, Smith, Celniker, Gelbart, Kellis
Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or 'evolutionary signatures', dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre- and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies.
Lindblad-Toh, Wade, Mikkelsen, Karlsson, Jaffe, Kamal, Clamp, Chang, Kulbokas, Zody, Mauceli, Xie, Breen, Wayne, Ostrander, Ponting, Galibert, Smith, DeJong, Kirkness, Alvarez, Biagi, Brockman, Butler, Chin, Cook, Cuff, Daly, DeCaprio, Gnerre, Grabherr, Kellis, Kleber, Bardeleben, Goodstadt, Heger, Hitte, Kim, Koepfli, Parker, Pollinger, Searle, Sutter, Thomas, Webber, Baldwin, Abebe, Abouelleil, Aftuck, Ait-Zahra, Aldredge, Allen, An, Anderson, Antoine, Arachchi, Aslam, Ayotte, Bachantsang, Barry, Bayul, Benamara, Berlin, Bessette, Blitshteyn, Bloom, Blye, Boguslavskiy, Bonnet, Boukhgalter, Brown, Cahill, Calixte, Camarata, Cheshatsang, Chu, Citroen, Collymore, Cooke, Dawoe, Daza, Decktor, DeGray, Dhargay, Dooley, Dooley, Dorje, Dorjee, Dorris, Duffey, Dupes, Egbiremolen, Elong, Falk, Farina, Faro, Ferguson, Ferreira, Fisher, FitzGerald, Foley, Foley, Franke, Friedrich, Gage, Garber, Gearin, Giannoukos, Goode, Goyette, Graham, Grandbois, Gyaltsen, Hafez, Hagopian, Hagos, Hall, Healy, Hegarty, Honan, Horn, Houde, Hughes, Hunnicutt, Husby, Jester, Jones, Kamat, Kanga, Kells, Khazanovich, Kieu, Kisner, Kumar, Lance, Landers, Lara, Lee, Leger, Lennon, Leuper, LeVine, Liu, Liu, Lokyitsang, Lokyitsang, Lui, Macdonald, Major, Marabella, Maru, Matthews, McDonough, Mehta, Meldrim, Melnikov, Meneus, Mihalev, Mihova, Miller, Mittelman, Mlenga, Mulrain, Munson, Navidi, Naylor, Nguyen, Nguyen, Nguyen, Nguyen, Nicol, Norbu, Norbu, Novod, Nyima, Olandt, O'Neill, O'Neill, Osman, Oyono, Patti, Perrin, Phunkhang, Pierre, Priest, Rachupka, Raghuraman, Rameau, Ray, Raymond, Rege, Rise, Rogers, Rogov, Sahalie, Settipalli, Sharpe, Shea, Sheehan, Sherpa, Shi, Shih, Sloan, Smith, Sparrow, Stalker, Stange-Thomann, Stavropoulos, Stone, Stone, Sykes, Tchuinga, Tenzing, Tesfaye, Thoulutsang, Thoulutsang, Topham, Topping, Tsamla, Vassiliev, Venkataraman, Vo, Wangchuk, Wangdi, Weiand, Wilkinson, Wilson, Yadav, Yang, Yang, Young, Yu, Zainoun, Zembek, Zimmer, Lander
Here we report a high-quality draft genome sequence of the domestic dog (Canis familiaris), together with a dense map of single nucleotide polymorphisms (SNPs) across breeds. The dog is of particular interest because it provides important evolutionary information and because existing breeds show great phenotypic diversity for morphological, physiological and behavioural traits. We use sequence comparison with the primate and rodent lineages to shed light on the structure and evolution of genomes and genes. Notably, the majority of the most highly conserved non-coding sequences in mammalian genomes are clustered near a small subset of genes with important roles in development. Analysis of SNPs reveals long-range haplotypes across the entire dog genome, and defines the nature of genetic diversity within and across breeds. The current SNP map now makes it possible for genome-wide association studies to identify genes responsible for diseases and traits, with important consequences for human and companion animal health.
Kellis, Patterson, Endrizzi, Birren, Lander
Identifying the functional elements encoded in a genome is one of the principal challenges in modern biology. Comparative genomics should offer a powerful, general approach. Here, we present a comparative analysis of the yeast Saccharomyces cerevisiae based on high-quality draft sequences of three related species (S. paradoxus, S. mikatae and S. bayanus). We first aligned the genomes and characterized their evolution, defining the regions and mechanisms of change. We then developed methods for direct identification of genes and regulatory motifs. The gene analysis yielded a major revision to the yeast gene catalogue, affecting approximately 15% of all genes and reducing the total count by about 500 genes. The motif analysis automatically identified 72 genome-wide elements, including most known regulatory motifs and numerous new motifs. We inferred a putative function for most of these motifs, and provided insights into their combinatorial interactions. The results have implications for genome analysis of diverse organisms, including the human.
Galagan, Calvo, Borkovich, Selker, Read, Jaffe, FitzHugh, Ma, Smirnov, Purcell, Rehman, Elkins, Engels, Wang, Nielsen, Butler, Endrizzi, Qui, Ianakiev, Bell-Pedersen, Nelson, Werner-Washburne, Selitrennikoff, Kinsey, Braun, Zelter, Schulte, Kothe, Jedd, Mewes, Staben, Marcotte, Greenberg, Roy, Foley, Naylor, Stange-Thomann, Barrett, Gnerre, Kamal, Kamvysselis, Mauceli, Bielke, Rudd, Frishman, Krystofova, Rasmussen, Metzenberg, Perkins, Kroken, Cogoni, Macino, Catcheside, Li, Pratt, Osmani, DeSouza, Glass, Orbach, Berglund, Voelker, Yarden, Plamann, Seiler, Dunlap, Radford, Aramayo, Natvig, Alex, Mannhaupt, Ebbole, Freitag, Paulsen, Sachs, Lander, Nusbaum, Birren
Neurospora crassa is a central organism in the history of twentieth-century genetics, biochemistry and molecular biology. Here, we report a high-quality draft sequence of the N. crassa genome. The approximately 40-megabase genome encodes about 10,000 protein-coding genes--more than twice as many as in the fission yeast Schizosaccharomyces pombe and only about 25% fewer than in the fruitfly Drosophila melanogaster. Analysis of the gene set yields insights into unexpected aspects of Neurospora biology including the identification of genes potentially associated with red light photobiology, genes implicated in secondary metabolism, and important differences in Ca2+ signalling as compared with plants and animals. Neurospora possesses the widest array of genome defence mechanisms known for any eukaryotic organism, including a process unique to fungi called repeat-induced point mutation (RIP). Genome analysis suggests that RIP has had a profound impact on genome evolution, greatly slowing the creation of new genes through genomic duplication and resulting in a genome with an unusually low proportion of closely related genes.
The recent realization that many long transcripts in the human genome do not encode proteins has opened up a whole new field for the study of long non-coding RNAs (lncRNAs). Two of our genomic signatures have facilitated their discovery and characterization, namely the chromatin signatures associated with promoters and transcribed regions, and the evolutionary signatures associated with protein-coding selection. This has enabled us to participate in numerous collaborations seeking the discovery, annotation, and functional characterization of long non-coding RNAs. We have also developed computational methods for studying the structural properties of non-coding RNAs, based on their evolutionary signatures, biophysical folding properties, and recent types of experimental evidence that distinguished paired vs. unpaired positions and can thus constrain the folding algorithms.
Washietl, Kellis*, Garber*
Long intergenic noncoding RNAs (lincRNAs) play diverse regulatory roles in human development and disease, but little is known about their evolutionary history and constraint. Here, we characterize human lincRNA expression patterns in nine tissues across six mammalian species and multiple individuals. Of the 1898 human lincRNAs expressed in these tissues, we find orthologous transcripts for 80% in chimpanzee, 63% in rhesus, 39% in cow, 38% in mouse, and 35% in rat. Mammalian-expressed lincRNAs show remarkably strong conservation of tissue specificity, suggesting that it is selectively maintained. In contrast, abundant splice-site turnover suggests that exact splice sites are not critical. Relative to evolutionarily young lincRNAs, mammalian-expressed lincRNAs show higher primary sequence conservation in their promoters and exons, increased proximity to protein-coding genes enriched for tissue-specific functions, fewer repeat elements, and more frequent single-exon transcripts. Remarkably, we find that ~20% of human lincRNAs are not expressed beyond chimpanzee and are undetectable even in rhesus. These hominid-specific lincRNAs are more tissue specific, enriched for testis, and faster evolving within the human lineage.
Washietl, Bernhart, Kellis
Many biologically important RNA structures are conserved in evolution leading to
characteristic mutational patterns. RNAalifold is a widely used program to
predict consensus secondary structures in multiple alignments by combining
evolutionary information with traditional energy-based RNA folding algorithms.
Here we describe the theory and applications of the RNAalifold algorithm.
Consensus secondary structure prediction not only leads to significantly more
accurate structure models, but it also allows to study structural conservation of
functional RNAs.
Sauvageau, Goff, Lodato, Bonev, Groff, Gerhardinger, Sanchez-Gomez, Hacisuleyman, Li, Spence, Liapis, Mallard, Morse, Swerdel, D'Ecclessis, Moore, Lai, Gong, Yancopoulos, Frendewey, Kellis, Hart, Valenzuela, Arlotta, Rinn
Many studies are uncovering functional roles for long noncoding RNAs (lncRNAs), yet few have been tested for in vivo relevance through genetic ablation in animal models. To investigate the functional relevance of lncRNAs in various physiological conditions, we have developed a collection of 18 lncRNA knockout strains in which the locus is maintained transcriptionally active. Initial characterization revealed peri- and postnatal lethal phenotypes in three mutant strains (Fendrr, Peril, and Mdgt), the latter two exhibiting incomplete penetrance and growth defects in survivors. We also report growth defects for two additional mutant strains (linc-Brn1b and linc-Pint). Further analysis revealed defects in lung, gastrointestinal tract, and heart in Fendrr(-/-) neonates, whereas linc-Brn1b(-/-) mutants displayed distinct abnormalities in the generation of upper layer II-IV neurons in the neocortex. This study demonstrates that lncRNAs play critical roles in vivo and provides a framework and impetus for future larger-scale functional investigation into the roles of lncRNA molecules
Rouskin, Zubradt, Washietl, Kellis, Weissman
RNA has a dual role as an informational molecule and a direct effector of biological tasks. The latter function is enabled by RNA's ability to adopt complex secondary and tertiary folds and thus has motivated extensive computational1, 2 and experimental3, 4, 5, 6, 7, 8 efforts for determining RNA structures. Existing approaches for evaluating RNA structure have been largely limited to in vitro systems, yet the thermodynamic forces which drive RNA folding in vitro may not be sufficient to predict stable RNA structures in vivo5. Indeed, the presence of RNA-binding proteins and ATP-dependent helicases can influence which structures are present inside cells. Here we present an approach for globally monitoring RNA structure in native conditions in vivo with single-nucleotide precision. This method is based on in vivo modification with dimethyl sulphate (DMS), which reacts with unpaired adenine and cytosine residues9, followed by deep sequencing to monitor modifications. Our data from yeast and mammalian cells are in excellent agreement with known messenger RNA structures and with the high-resolution crystal structure of the Saccharomyces cerevisiae ribosome10. Comparison between in vivo and in vitro data reveals that in rapidly dividing cells there are vastly fewer structured mRNA regions in vivo than in vitro. Even thermostable RNA structures are often denatured in cells, highlighting the importance of cellular processes in regulating RNA structure. Indeed, analysis of mRNA structure under ATP-depleted conditions in yeast shows that energy-dependent processes strongly contribute to the predominantly unfolded state of mRNAs inside cells. Our studies broadly enable the functional analysis of physiological RNA structures and reveal that, in contrast to the Anfinsen view of protein folding whereby the structure formed is the most thermodynamically favourable, thermodynamics have an incomplete role in determining mRNA structure in vivo.
Sun, Goff, Trapnell, Alexander, Lo, Hacisuleyman, Sauvageau, Tazon-Vega, Kelley, Hendrickson, Yuan, Kellis, Lodish, Rinn
The prevalence of obesity has led to a surge of interest in understanding the detailed mechanisms underlying adipocyte development. Many protein-coding genes, mRNAs, and microRNAs have been implicated in adipocyte development, but the global expression patterns and functional contributions of long noncoding RNA (lncRNA) during adipogenesis have not been explored. Here we profiled the transcriptome of primary brown and white adipocytes, preadipocytes, and cultured adipocytes and identified 175 lncRNAs that are specifically regulated during adipogenesis. Many lncRNAs are adipose-enriched, strongly induced during adipogenesis, and bound at their promoters by key transcription factors such as peroxisome proliferator-activated receptor gamma (PPAR-gamma) and CCAAT/enhancer-binding protein alpha (CEBP-alpha). RNAi-mediated loss of function screens identified functional lncRNAs with varying impact on adipogenesis. Collectively, we have identified numerous lncRNAs that are functionally required for proper adipogenesis
Grote, Wittler, Hendrix, Koch, Wahrisch, Beisaw, Macura, Blass, Kellis, Werber, Herrmann
The histone-modifying complexes PRC2 and TrxG/MLL play pivotal roles in determining the activation state of genes controlling pluripotency, lineage commitment, and cell differentiation. Long noncoding RNAs (lncRNAs) can bind to either complex, and some have been shown to act as modulators of PRC2 or TrxG/MLL activity. Here we show that the lateral mesoderm-specific lncRNA Fendrr is essential for proper heart and body wall development in the mouse. Embryos lacking Fendrr displayed upregulation of several transcription factors controlling lateral plate or cardiac mesoderm differentiation, accompanied by a drastic reduction in PRC2 occupancy along with decreased H3K27 trimethylation and/or an increase in H3K4 trimethylation at their promoters. Fendrr binds to both the PRC2 and TrxG/MLL complexes, suggesting that it acts as modulator of chromatin signatures that define gene activity. Thus, we identified an lncRNA that plays an essential role in the regulatory networks controlling the fate of lateral mesoderm derivatives
Talkowski, Maussion, Crapper, Rosenfeld, Blumenthal, Hanscom, Chiang, Lindgren, Pereira, Ruderfer, Diallo, Lopez, Turecki, Chen, Gigek, Harris, Lip, An, Biagioli, Macdonald, Lin, Haggarty, Sklar, Purcell, Kellis, Schwartz, Shaffer, Natowicz, Shen, Morton, Gusella, Ernst
Large intergenic noncoding (linc) RNAs represent a newly described class of ribonucleic acid whose importance in human disease remains undefined. We identified a severely developmentally delayed 16-year-old female with karyotype 46,XX,t(2;11)(p25.1;p15.1)dn in the absence of clinically significant copy number variants (CNVs). DNA capture followed by next-generation sequencing of the translocation breakpoints revealed disruption of a single noncoding gene on chromosome 2, LINC00299, whose RNA product is expressed in all tissues measured, but most abundantly in brain. Among a series of additional, unrelated subjects referred for clinical diagnostic testing who showed CNV affecting this locus, we identified four with exon-crossing deletions in association with neurodevelopmental abnormalities. No disruption of the LINC00299 coding sequence was seen in almost 14,000 control subjects. Together, these subjects with disruption of LINC00299 implicate this particular noncoding RNA in brain development and raise the possibility that, as a class, abnormalities of lincRNAs may play a significant role in human developmental disorders
Washietl, Will, Hendrix, Goff, Rinn, Berger, Kellis.
Noncoding RNAs have emerged as important key players in the cell. Understanding their surprisingly diverse range of functions is challenging for experimental and computational biology. Here, we review computational methods to analyze noncoding RNAs. The topics covered include basic and advanced techniques to predict RNA structures, annotation of noncoding RNAs in genomic data, mining RNA-seq data for novel transcripts and prediction of transcript structures, computational aspects of microRNAs, and database resources. These authors contributed equally WIREs RNA 2012. doi: 10.1002/wrna.1134 For further resources related to this article, please visit the WIREs website.
Washietl, Hofacker, Stadler, Kellis
Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using 'pseudo-energies' to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with 'soft' constraints of different strength. We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect RNA folding such as chemical nucleotide modifications and protein binding.
Parker, Moltke, Roth, Washietl, Wen, Kellis, Breaker, Pedersen
Regulatory RNA structures are often members of families with multiple paralogous instances across the genome. Family members share functional and structural properties, which allow them to be studied as a whole, facilitating both bioinformatic and experimental characterization. We have developed a comparative method, EvoFam, for genome-wide identification of families of regulatory RNA structures, based on primary sequence and secondary structure similarity. We apply EvoFam to a 41-way genomic vertebrate alignment. Genome-wide, we identify 220 human, high-confidence families outside protein-coding regions comprising 725 individual structures, including 48 families with known structural RNA elements. Known families identified include both noncoding RNAs, e.g., miRNAs and the recently identified MALAT1/MEN b lincRNA family; and cis-regulatory structures, e.g., iron-responsive elements. We also identify tens of new families supported by strong evolutionary evidence and other statistical evidence, such as GO term enrichments. For some of these, detailed analysis has led to the formulation of specific functional hypotheses. Examples include two hypothesized auto-regulatory feedback mechanisms: one involving six long hairpins in the 39-UTR of MAT2A, a key metabolic gene that produces the primary human methyl donor S-adenosylmethionine; the other involving a tRNA-like structure in the intron of the tRNA maturation gene POP1. We experimentally validate the predicted MAT2A structures. Finally, we identify potential new regulatory networks, including large families of short hairpins enriched in immunity-related genes, e.g., TNF, FOS, and CTLA4, which include known transcript destabilizing elements. Our findings exemplify the diversity of posttranscriptional regulation and provide a resource for further characterization of new regulatory mechanisms and families of noncoding RNAs.
Hung, Wang, Lin, Koegel, Kotake, Grant, Horlings, Shah, Umbricht, Wang, Wang, Kong, Langerod, Břrresen-Dale, Kim, Vijver, Sukumar, Whitfield, Kellis, Xiong, Wong, Chang
Transcription of long noncoding RNAs (lncRNAs) within gene regulatory elements can modulate gene activity in response to external stimuli, but the scope and functions of such activity are not known. Here we use an ultrahigh-density array that tiles the promoters of 56 cell-cycle genes to interrogate 108 samples representing diverse perturbations. We identify 216 transcribed regions that encode putative lncRNAs, many with RT-PCR-validated periodic expression during the cell cycle, show altered expression in human cancers and are regulated in expression by specific oncogenic stimuli, stem cell differentiation or DNA damage. DNA damage induces five lncRNAs from the CDKN1A promoter, and one such lncRNA, named PANDA, is induced in a p53-dependent manner. PANDA interacts with the transcription factor NF-YA to limit expression of pro-apoptotic genes; PANDA depletion markedly sensitized human fibroblasts to apoptosis by doxorubicin. These findings suggest potentially widespread roles for promoter lncRNAs in cell-growth control.
Guttman, Amit, Garber, French, Lin, Feldser, Huarte, Zuk, Carey, Cassady, Cabili, Jaenisch, Mikkelsen, Jacks, Hacohen, Bernstein, Kellis, Regev, Rinn, Lander
There is growing recognition that mammalian cells produce many thousands of large intergenic transcripts. However, the functional significance of these transcripts has been particularly controversial. Although there are some well-characterized examples, most (>95%) show little evidence of evolutionary conservation and have been suggested to represent transcriptional noise. Here we report a new approach to identifying large non-coding RNAs using chromatin-state maps to discover discrete transcriptional units intervening known protein-coding loci. Our approach identified approximately 1,600 large multi-exonic RNAs across four mouse cell types. In sharp contrast to previous collections, these large intervening non-coding RNAs (lincRNAs) show strong purifying selection in their genomic loci, exonic sequences and promoter regions, with greater than 95% showing clear evolutionary conservation. We also developed a functional genomics approach that assigns putative functions to each lincRNA, demonstrating a diverse range of roles for lincRNAs in processes from embryonic stem cell pluripotency to cell proliferation. We obtained independent functional validation for the predictions for over 100 lincRNAs, using cell-based assays. In particular, we demonstrate that specific lincRNAs are transcriptionally regulated by key transcription factors in these processes such as p53, NFkappaB, Sox2, Oct4 (also known as Pou5f1) and Nanog. Together, these results define a unique collection of functional lincRNAs that are highly conserved and implicated in diverse biological processes.
While gene regulation has been traditionally thought as mostly pre-transcriptional, it has been recently recognized that small regulatory RNAs can mediate the level of gene expression and translation post-transcriptionally, by directly targeting the messenger RNA of a protein-coding transcript. The most well-studied of these small regulatory RNAs are microRNAs (miRNAs), which recognize small 7-mer 'seed' motifs in the 3'untranslated regions (3'UTRs) of their targets. However, many additional classes of small regulatory RNAs have emerged, including piwiRNAs (piRNAs) encoded in a small number of master loci and specifically targeting and degrading transposable elements, and small-interfering RNAs (siRNAs) that can target both protein-coding and mobile elements and can arise from convergent transcripts or structured loci. Our group has developed methods for recognizing microRNAs based on their 3'UTR motif complementarity, their evolutionary signatures, and their sequencing signatures in RNA-Seq datasets. We have also developed methods for microRNA target prediction using evolutionary conservation. We have also shown that a single microRNA locus can result in two functional gene products by transcription of both strands into distinct and functional microRNAs. We have also participated in the discovery of piwiRNAs and endogenous siRNAs in Drosophila.
Barsacchi, Novoa, Kellis, Bechini
Riboswitches are cis-regulatory elements in mRNA, mostly found in Bacteria, which exhibit two main secondary structure conformations. While one of them prevents the gene from being expressed, the other conformation allows its expression, and this switching process is typically driven by the presence of a specific ligand. Although there are a handful of known riboswitches, our knowledge in this field has been greatly limited due to our inability to identify them based on its sequence. Indeed, current methods are not able to predict the presence of the two functionally distinct conformations just from the knowledge of the plain RNA nucleotide sequence. Whether this would be possible, for which cases, and what prediction accuracy can be achieved, are currently open questions. Here we show that the two alternate secondary structures of riboswitches can be accurately predicted once the "switching sequence" of the riboswitch has been properly identified. The proposed SwiSpot approach is capable of identifying the switching sequence inside a putative, complete riboswitch sequence, on the basis of pairing behaviors, which are evaluated on proper sets of configurations. Moreover, it is able to model the switching behavior of riboswitches whose generated ensemble covers both alternate configurations. Beyond structural predictions, the approach can also be paired to homology-based riboswitch searches.
Stark, Bushati, Jan, Kheradpour, Hodges, Brennecke, Bartel, Cohen, Kellis
MicroRNAs (miRNAs) are approximately 22-nucleotide RNAs that are processed from characteristic precursor hairpins and pair to sites in messages of protein-coding genes to direct post-transcriptional repression. Here, we report that the miRNA iab-4 locus in the Drosophila Hox cluster is transcribed convergently from both DNA strands, giving rise to two distinct functional miRNAs. Both sense and antisense miRNA products target neighboring Hox genes via highly conserved sites, leading to homeotic transformations when ectopically expressed. We also report sense/antisense miRNAs in mouse and find antisense transcripts close to many miRNAs in both flies and mammals, suggesting that additional sense/antisense pairs exist.
Stark, Kheradpour, Parts, Brennecke, Hodges, Hannon, Kellis
MicroRNAs (miRNAs) are short regulatory RNAs that inhibit target genes by complementary binding in 3' untranslated regions (3' UTRs). They are one of the most abundant classes of regulators, targeting a large fraction of all genes, making their comprehensive study a requirement for understanding regulation and development. Here we use 12 Drosophila genomes to define structural and evolutionary signatures of miRNA hairpins, which we use for their de novo discovery. We predict >41 novel miRNA genes, which encompass many unique families, and 28 of which are validated experimentally. We also define signals for the precise start position of mature miRNAs, which suggest corrections of previously known miRNAs, often leading to drastic changes in their predicted target spectrum. We show that miRNA discovery power scales with the number and divergence of species compared, suggesting that such approaches can be successful in human as dozens of mammalian genomes become available. Interestingly, for some miRNAs sense and anti-sense hairpins score highly and mature miRNAs from both strands can indeed be found in vivo. Similarly, miRNAs with weak 5' end predictions show increased in vivo processing of multiple alternate 5' ends and have fewer predicted targets. Lastly, we show that several miRNA star sequences score highly and are likely functional. For mir-10 in particular, both arms show abundant processing, and both show highly conserved target sites in Hox genes, suggesting a possible cooperation of the two arms, and their role as a master Hox regulator.
Xie, Lu, Kulbokas, Golub, Mootha, Lindblad-Toh, Lander, Kellis
Comprehensive identification of all functional elements encoded in the human genome is a fundamental need in biomedical research. Here, we present a comparative analysis of the human, mouse, rat and dog genomes to create a systematic catalogue of common regulatory motifs in promoters and 3' untranslated regions (3' UTRs). The promoter analysis yields 174 candidate motifs, including most previously known transcription-factor binding sites and 105 new motifs. The 3'-UTR analysis yields 106 motifs likely to be involved in post-transcriptional regulation. Nearly one-half are associated with microRNAs (miRNAs), leading to the discovery of many new miRNA genes and their likely target genes. Our results suggest that previous estimates of the number of human miRNA genes were low, and that miRNAs regulate at least 20% of human genes. The overall results provide a systematic view of gene regulation in the human, which will be refined as additional mammalian genomes become available.
Czech, Malone, Zhou, Stark, Schlingeheyde, Dus, Perrimon, Kellis, Wohlschlegel, Sachidanandam, Hannon, Brennecke
Drosophila endogenous small RNAs are categorized according to their mechanisms of biogenesis and the Argonaute protein to which they bind. MicroRNAs are a class of ubiquitously expressed RNAs of approximately 22 nucleotides in length, which arise from structured precursors through the action of Drosha-Pasha and Dicer-1-Loquacious complexes. These join Argonaute-1 to regulate gene expression. A second endogenous small RNA class, the Piwi-interacting RNAs, bind Piwi proteins and suppress transposons. Piwi-interacting RNAs are restricted to the gonad, and at least a subset of these arises by Piwi-catalysed cleavage of single-stranded RNAs. Here we show that Drosophila generates a third small RNA class, endogenous small interfering RNAs, in both gonadal and somatic tissues. Production of these RNAs requires Dicer-2, but a subset depends preferentially on Loquacious rather than the canonical Dicer-2 partner, R2D2 (ref. 14). Endogenous small interfering RNAs arise both from convergent transcription units and from structured genomic loci in a tissue-specific fashion. They predominantly join Argonaute-2 and have the capacity, as a class, to target both protein-coding genes and mobile elements. These observations expand the repertoire of small RNAs in Drosophila, adding a class that blurs distinctions based on known biogenesis mechanisms and functional roles.
Ruby, Stark, Johnston, Kellis, Bartel, Lai
MicroRNA (miRNA) genes give rise to small regulatory RNAs in a wide variety of organisms. We used computational methods to predict miRNAs conserved among Drosophila species and large-scale sequencing of small RNAs from Drosophila melanogaster to experimentally confirm and complement these predictions. In addition to validating 20 of our top 45 predictions for novel miRNA loci, the large-scale sequencing identified many miRNAs that had not been predicted. In total, 59 novel genes were identified, increasing our tally of confirmed fly miRNAs to 148. The large-scale sequencing also refined the identities of previously known miRNAs and provided insights into their biogenesis and expression. Many miRNAs were expressed in particular developmental contexts, with a large cohort of miRNAs expressed primarily in imaginal discs. Conserved miRNAs typically were expressed more broadly and robustly than were nonconserved miRNAs, and those conserved miRNAs with more restricted expression tended to have fewer predicted targets than those expressed more broadly. Predicted targets for the expanded set of microRNAs substantially increased and revised the miRNA-target relationships that appear conserved among the fly species. Insights were also provided into miRNA gene evolution, including evidence for emergent regulatory function deriving from the opposite arm of the miRNA hairpin, exemplified by mir-10, and even the opposite strand of the DNA, exemplified by mir-iab-4.
Brennecke, Aravin, Stark, Dus, Kellis, Sachidanandam, Hannon
Drosophila Piwi-family proteins have been implicated in transposon control. Here, we examine piwi-interacting RNAs (piRNAs) associated with each Drosophila Piwi protein and find that Piwi and Aubergine bind RNAs that are predominantly antisense to transposons, whereas Ago3 complexes contain predominantly sense piRNAs. As in mammals, the majority of Drosophila piRNAs are derived from discrete genomic loci. These loci comprise mainly defective transposon sequences, and some have previously been identified as master regulators of transposon activity. Our data suggest that heterochromatic piRNA loci interact with potentially active, euchromatic transposons to form an adaptive system for transposon control. Complementary relationships between sense and antisense piRNA populations suggest an amplification loop wherein each piRNA-directed cleavage event generates the 5' end of a new piRNA. Thus, sense piRNAs, formed following cleavage of transposon mRNAs may enhance production of antisense piRNAs, complementary to active elements, by directing cleavage of transcripts from master control loci.
Murchison, Kheradpour, Sachidanandam, Smith, Hodges, Xuan, Kellis, Grützner, Stark, Hannon
Small RNA pathways play evolutionarily conserved roles in gene regulation and defense from parasitic nucleic acids. The character and expression patterns of small RNAs show conservation throughout animal lineages, but specific animal clades also show variations on these recurring themes, including species-specific small RNAs. The monotremes, with only platypus and four species of echidna as extant members, represent the basal branch of the mammalian lineage. Here, we examine the small RNA pathways of monotremes by deep sequencing of six platypus and echidna tissues. We find that highly conserved microRNA species display their signature tissue-specific expression patterns. In addition, we find a large rapidly evolving cluster of microRNAs on platypus chromosome X1, which is unique to monotremes. Platypus and echidna testes contain a robust Piwi-interacting (piRNA) system, which appears to be participating in ongoing transposon defense.
Murchison, Tovar, Hsu, Bender, Kheradpour, Rebbeck, Obendorf, Conlan, Bahlo, Blizzard, Pyecroft, Kreiss, Kellis, Stark, Harkins, Marshall, Woods, Hannon, Papenfuss
The Tasmanian devil, a marsupial carnivore, is endangered because of the emergence of a transmissible cancer known as devil facial tumor disease (DFTD). This fatal cancer is clonally derived and is an allograft transmitted between devils by biting. We performed a large-scale genetic analysis of DFTD with microsatellite genotyping, a mitochondrial genome analysis, and deep sequencing of the DFTD transcriptome and microRNAs. These studies confirm that DFTD is a monophyletic clonally transmissible tumor and suggest that the disease is of Schwann cell origin. On the basis of these results, we have generated a diagnostic marker for DFTD and identify a suite of genes relevant to DFTD pathology and transmission. We provide a genomic data set for the Tasmanian devil that is applicable to cancer diagnosis, disease evolution, and conservation biology.
Protein-coding genes constitute the workhorse of the cell, and play
almost every imaginable cellular role, from structural components to
enzymes, regulators, signaling molecules, transporters. As such,
human polymorphisms that disrupt protein-coding genes have been linked
to countless diseases. Such studies rely on a systematic understanding
of all the protein-coding genes encoded in the human genome. This has
proven to be a daunting task, given the very short size of exons (only
50 nucleotides on average), the very long intergenic and intronic
spaces (reaching hundreds of thousands of nucleotides), and the very
complex patterns of alternative splicing that genes can undergo.
To address this challenge, we have developed discriminative metrics
for gene finding, based on comparative genomics evidence. We found
several evolutionary signatures associated with protein-coding exons,
including reading frame conservation (RFC) and codon substitution
frequencies (CSF). We showed that these evolutionary signatures
significantly outperform single-species and pairwise metrics, and
applied them to revisit the yeast, fly, and human genomes, leading to
new genes, new exons, and new alternative splice forms, revealing
spurious annotations, refining gene boundaries and reading frame, and
adding new alternative splice forms. We worked closely with curators
and experimental labs, to validate most of these predictions, revising
genome databases.
These evolutionary signatures have also enabled us to reveal
several types of unusual genes, that defy the traditional rules of
translation. These include many polycistronic transcripts, genes with
regulated stop codon readthrough events, and apparent programmed
translational frameshifts, many of which correlate with mRNA secondary
structures. Most of these results would have been inaccessible with
traditional gene finding methods. We have also recently recognized
that protein-coding exons can encode within them additional functional
elements that exploit the degenerate nature of the genetic code.
Ma, Diedrich, Jungreis, Donaldson, Vaughan, Kellis, Yates, Saghatelian
Computational, genomic, and proteomic approaches have been used to discover nonannotated protein-coding small open reading frames (smORFs). Some novel smORFs have crucial biological roles in cells and organisms, which motivates the search for additional smORFs. Proteomic smORF discovery methods are advantageous because they detect smORF-encoded polypeptides (SEPs) to validate smORF translation and SEP stability. Because SEPs are shorter and less abundant than average proteins, SEP detection using proteomics faces unique challenges. Here, we optimize several steps in the SEP discovery workflow to improve SEP isolation and identification. These changes have led to the detection of several new human SEPs (novel human genes), improved confidence in the SEP assignments, and enabled quantification of SEPs under different cellular conditions. These improvements will allow faster detection and characterization of new SEPs and smORFs.
Rivas, Pirinen, Conrad, Lek, Tsang, Karczewski, Maller, Kukurba, DeLuca, Fromer, Ferreira, Smith, Zhang, Zhao, Banks, Poplin, Ruderfer, Purcell, Tukiainen, Minikel, Stenson, Cooper, Huang, Sullivan, Nedzel, Bustamante, Li, Daly, Guigo, Donnelly, Ardlie, Sammeth, Dermitzakis, McCarthy, Montgomery, Lappalainen, MacArthur
Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants
Sealfon, Lin, Jungreis, Wolf, Kellis, Sabeti
The increasing availability of sequence data for many viruses provides power to detect regions under unusual evolutionary constraint at a high resolution. One approach leverages the synonymous substitution rate as a signature to pinpoint genic regions encoding overlapping or embedded functional elements. Protein-coding regions in viral genomes often contain overlapping RNA structural elements, reading frames, regulatory elements, microRNAs, and packaging signals. Synonymous substitutions in these regions would be selectively disfavored and thus these regions are characterized by excess synonymous constraint. Codon choice can also modulate transcriptional efficiency, translational accuracy, and protein folding. We developed a phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates. We demonstrated the high specificity of our approach on simulated data and applied our framework to the protein-coding regions of approximately 30 distinct species of viruses with diverse genome architectures. FRESCo recovers known multifunctional regions in well-characterized viruses such as hepatitis B virus, poliovirus, and West Nile virus, often at a single-codon resolution, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses. In a number of viruses, the synonymously constrained regions that we identified also display conserved, stable predicted RNA structures, including putative novel elements in multiple viral species
Loughran, Chou, Ivanov, Jungries, Kellis, Kiran, Baranov, Atkins.
Stop codon readthrough is used extensively by viruses to expand their gene expression. Until recent discoveries in Drosophila, only a very limited number of readthrough cases in chromosomal genes had been reported. Analysis of conserved protein coding signatures that extend beyond annotated stop codons identified potential stop codon readthrough of four mammalian genes. Here we use a modified targeted bioinformatic approach to identify a further three mammalian readthrough candidates. All seven genes were tested experimentally using reporter constructs transfected into HEK-293T cells. Four displayed efficient stop codon readthrough, and these have UGA immediately followed by CUAG. Comparative genomic analysis revealed that in the four readthrough candidates containing UGA-CUAG, this motif is conserved not only in mammals but throughout vertebrates with the first six of the seven nucleotides being universally conserved. The importance of the CUAG motif was confirmed using a systematic mutagenesis approach. One gene, OPRL1, encoding an opiate receptor, displayed extremely efficient levels of readthrough (~31%) in HEK-293T cells. Signals both 5' and 3' of the OPRL1 stop codon contribute to this high level of readthrough. The sequence UGA-CUA alone can support 1.5% readthrough, underlying its importance.
Ma, Ward, Jungreis, Slavoff, Schwaid, Neveu, Budnik, Kellis, Saghatelian
The existence of nonannotated protein-coding human short open reading frames (sORFs) has been revealed through the direct detection of their sORF-encoded polypeptide (SEP) products. The discovery of novel SEPs increases the size of the genome and the proteome and provides insights into the molecular biology of mammalian cells, such as the prevalent usage of non-AUG start codons. Through modifications of the existing SEP-discovery workflow, we discover an additional 195 SEPs in K562 cells and extend this methodology to identify novel human SEPs in additional cell lines and human tissue for a final tally of 237 new SEPs. These results continue to expand the human genome and proteome and demonstrate that SEPs are a ubiquitous class of nonannotated polypeptides that require further investigation.
Harrow, Frankish, Gonzalez, Tapanari, Diekhans, Kokocinski, Aken, Barrell, Zadissa, Searle, Barnes, Bignell, Boychenko, Hunt, Kay, Mukherjee, Rajan, Despacio-Reyes, Saunders, Steward, Harte, Lin, Howald, Tanzer, Derrien, Chrast, Walters, Balasubramanian, Pei, Tress, Rodriguez, Ezkurdia, van Baren, Brent, Haussler, Kellis, Valencia, Reymond, Gerstein, Guigo, Hubbard
The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.
Jungreis, Lin, Spokony, Chan, Negre, Victorsen, White, Kellis
While translational stop codon readthrough is often utilized by viral genomes it has been observed for only a handful of eukaryotic genes. We previously used comparative genomics evidence to recognize protein-coding regions in 12 species of Drosophila and showed that for 149 genes the open reading frame following the stop codon has a protein-coding conservation signature, hinting that stop codon readthrough might be common in Drosophila. We return to this observation armed with deep RNA sequence data from the modENCODE project, an improved higher-resolution comparative genomics metric for detecting protein-coding regions, comparative sequence information from additional species, and directed experimental evidence. We report an expanded set of 283 readthrough candidates, including 16 double readthrough candidates; these were manually curated to rule out alternatives such as A-to-I editing, alternative splicing, dicistronic translation, and selenocysteine incorporation. We report experimental evidence of translation using GFP tagging and mass spectrometry for several readthrough regions. We find that the set of readthrough candidates differs from other genes in length, composition, conservation, stop codon context, and in some cases, conserved stem loops, providing clues about readthrough regulation and potential mechanisms. Lastly, we expand our studies beyond Drosophila and find evidence of abundant readthrough in several other insect species and one crustacean, and several readthrough candidates in nematode and human, suggesting that functionally-important translational stop codon readthrough is significantly more prevalent in Metazoa than previously recognized.
Lin, Washietl, Kheradpour, Parker, Pedersen, Kellis
The degeneracy of the genetic code allows protein-coding DNA and RNA sequences to simultaneously encode additional, overlapping functional elements. A sequence in which both protein-coding and additional overlapping functions have evolved under purifying selection should show increased evolutionary conservation compared to typical protein-coding genes - especially at synonymous sites. In this study, we use genome alignments of 29 placental mammals to systematically locate short regions within human ORFs that show conspicuously low estimated rates of synonymous substitution across these species. The 29-species alignment provides statistical power to locate more than 10,000 such regions with resolution down to nine-codon windows, which are found within more than a quarter of all human protein-coding genes and contain ~2% of their synonymous sites. We collect numerous lines of evidence that the observed synonymous constraint in these regions reflects selection on overlapping functional elements including splicing regulatory elements, dual-coding genes, RNA secondary structures, microRNA target sites, and developmental enhancers. Our results show that overlapping functional elements are common in mammalian genes, despite the vast genomic landscape.
Lin, Jungreis, Kellis
As high-throughput transcriptome sequencing provides evidence for novel transcripts in many species, there is a renewed need for accurate methods to classify small genomic regions as protein coding or non-coding. We present PhyloCSF, a novel comparative genomics method that analyzes a multispecies nucleotide sequence alignment to determine whether it is likely to represent a conserved protein-coding region, based on a formal statistical comparison of phylogenetic codon models. We show that PhyloCSF's classification performance in 12-species Drosophila genome alignments exceeds all other methods we compared in a previous study. We anticipate that this method will be widely applicable as the transcriptomes of many additional species, tissues and subcellular compartments are sequenced, particularly in the context of ENCODE and modENCODE, and as interest grows in long non-coding RNAs, often initially recognized by their lack of protein coding potential rather than conserved RNA secondary structures. The Objective Caml source code and executables for GNU/Linux and Mac OS X are freely available at http://compbio.mit.edu/PhyloCSF
Müller, Kohajda, Findeiß, Stadler, Washietl, Kellis, vonBergen, Kalkhof
Proteins with molecular weights of <25 kDa are involved in major biological processes such as ribosome formation, stress adaption (e.g., temperature reduction) and cell cycle control. Despite their importance, the coverage of smaller proteins in standard proteome studies is rather sparse. Here we investigated biochemical and mass spectrometric parameters that influence coverage and validity of identification. The underrepresentation of low molecular weight (LMW) proteins may be attributed to the low numbers of proteolytic peptides formed by tryptic digestion as well as their tendency to be lost in protein separation and concentration/desalting procedures. In a systematic investigation of the LMW proteome of Escherichia coli, a total of 455 LMW proteins (27% of the 1672 listed in the SwissProt protein database) were identified, corresponding to a coverage of 62% of the known cytosolic LMW proteins. Of these proteins, 93 had not yet been functionally classified, and five had not previously been confirmed at the protein level. In this study, the influences of protein extraction (either urea or TFA), proteolytic digestion (solely, and the combined usage of trypsin and AspN as endoproteases) and protein separation (gel- or non-gel-based) were investigated. Compared to the standard procedure based solely on the use of urea lysis buffer, in-gel separation and tryptic digestion, the complementary use of TFA for extraction or endoprotease AspN for proteolysis permits the identification of an extra 72 (32%) and 51 proteins (23%), respectively. Regarding mass spectrometry analysis with an LTQ Orbitrap mass spectrometer, collision-induced fragmentation (CID and HCD) and electron transfer dissociation using the linear ion trap (IT) or the Orbitrap as the analyzer were compared. IT-CID was found to yield the best identification rate, whereas IT-ETD provided almost comparable results in terms of LMW proteome coverage. The high overlap between the proteins identified with IT-CID and IT-ETD allowed the validation of 75% of the identified proteins using this orthogonal fragmentation technique. Furthermore, a new approach to evaluating and improving the completeness of protein databases that utilizes the program RNAcode was introduced and examined.
Pruitt, Harrow, Harte, Wallin, Diekhans, Maglott, Searle, Farrell, Loveland, Ruef, Hart, Suner, Landrum, Aken, Ayling, Baertsch, Fernandez-Banet, Cherry, Curwen, Dicuccio, Kellis, Lee, Lin, Schuster, Shkeda, Amid, Brown, Dukhanina, Frankish, Hart, Maidak, Mudge, Murphy, Murphy, Rajan, Rajput, Riddick, Snow, Steward, Webb, Weber, Wilming, Wu, Birney, Haussler, Hubbard, Ostell, Durbin, Lipman
Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.
Lin, Deoras, Rasmussen, Kellis
Comparative genomics of multiple related species is a powerful methodology for the discovery of functional genomic elements, and its power should increase with the number of species compared. Here, we use 12 Drosophila genomes to study the power of comparative genomics metrics to distinguish between protein-coding and non-coding regions. First, we study the relative power of different comparative metrics and their relationship to single-species metrics. We find that even relatively simple multi-species metrics robustly outperform advanced single-species metrics, especially for shorter exons (< or =240 nt), which are common in animal genomes. Moreover, the two capture largely independent features of protein-coding genes, with different sensitivity/specificity trade-offs, such that their combinations lead to even greater discriminatory power. In addition, we study how discovery power scales with the number and phylogenetic distance of the genomes compared. We find that species at a broad range of distances are comparably effective informants for pairwise comparative gene identification, but that these are surpassed by multi-species comparisons at similar evolutionary divergence. In particular, while pairwise discovery power plateaued at larger distances and never outperformed the most advanced single-species metrics, multi-species comparisons continued to benefit even from the most distant species with no apparent saturation. Last, we find that genes in functional categories typically considered fast-evolving can nonetheless be recovered at very high rates using comparative methods. Our results have implications for comparative genomics analyses in any species, including the human.
Clamp, Fry, Kamal, Xie, Cuff, Lin, Kellis, Lindblad-Toh, Lander
Although the Human Genome Project was completed 4 years ago, the catalog of human protein-coding genes remains a matter of controversy. Current catalogs list a total of approximately 24,500 putative protein-coding genes. It is broadly suspected that a large fraction of these entries are functionally meaningless ORFs present by chance in RNA transcripts, because they show no evidence of evolutionary conservation with mouse or dog. However, there is currently no scientific justification for excluding ORFs simply because they fail to show evolutionary conservation: the alternative hypothesis is that most of these ORFs are actually valid human genes that reflect gene innovation in the primate lineage or gene loss in the other lineages. Here, we reject this hypothesis by carefully analyzing the nonconserved ORFs-specifically, their properties in other primates. We show that the vast majority of these ORFs are random occurrences. The analysis yields, as a by-product, a major revision of the current human catalogs, cutting the number of protein-coding genes to approximately 20,500. Specifically, it suggests that nonconserved ORFs should be added to the human gene catalog only if there is clear evidence of an encoded protein. It also provides a principled methodology for evaluating future proposed additions to the human gene catalog. Finally, the results indicate that there has been relatively little true innovation in mammalian protein-coding genes.
Lin, Carlson, Crosby, Matthews, Yu, Park, Wan, Schroeder, Gramates, St, Roark, Wiley, Kulathinal, Zhang, Myrick, Antone, Celniker, Gelbart, Kellis
The availability of sequenced genomes from 12 Drosophila species has enabled the use of comparative genomics for the systematic discovery of functional elements conserved within this genus. We have developed quantitative metrics for the evolutionary signatures specific to protein-coding regions and applied them genome-wide, resulting in 1193 candidate new protein-coding exons in the D. melanogaster genome. We have reviewed these predictions by manual curation and validated a subset by directed cDNA screening and sequencing, revealing both new genes and new alternative splice forms of known genes. We also used these evolutionary signatures to evaluate existing gene annotations, resulting in the validation of 87% of genes lacking descriptive names and identifying 414 poorly conserved genes that are likely to be spurious predictions, noncoding, or species-specific genes. Furthermore, our methods suggest a variety of refinements to hundreds of existing gene models, such as modifications to translation start codons and exon splice boundaries. Finally, we performed directed genome-wide searches for unusual protein-coding structures, discovering 149 possible examples of stop codon readthrough, 125 new candidate ORFs of polycistronic mRNAs, and several candidate translational frameshifts. These results affect >10% of annotated fly genes and demonstrate the power of comparative genomics to enhance our understanding of genome organization, even in a model organism as intensively studied as Drosophila melanogaster.
Beyond the primary sequence of the genome, it has recently been recognized that a wide variety of post-translational modifications in the tails of histone proteins (around which DNA is wrapped into chromatin) play key roles in genome function, cellular differentiation, and human disease. With several dozen such 'chromatin marks' already described, a wide array of chromatin regulators that can read and write them, and an astronomical number of mark combinations, discovering biologically meaningful sets of jointly acting marks and characterizing their specific function are daunting new challenges in genomics. We have developed new methods for addressing these challenges, using genome-wide maps of many histone marks to discover recurrent and spatially-coherent combinations of marks, or 'chromatin states'. We also developed data integration methods to systematically characterize chromatin state function, revealing diverse classes of enhancers, promoters, and insulators, which we used to discover new functional elements, to study chromatin dynamics across cell types and development, and to reveal motifs and regulators governing epigenetic changes in development, differentiation and disease.
Das, Hendrix, Apostolou, Buchner, Canver, Beyaz, Ljuboja, Kuintzle, Kim, Karnik, Shao, Xie, Xu, De Los Angeles, Zhang, Choe, Jun, Shen, Gregory, Daley, Meissner, Kellis, Hochedlinger, Kim, Orkin.
Polycomb Repressive Complex 2 (PRC2) function and DNA methylation (DNAme) are typically correlated with gene repression. Here, we show that PRC2 is required to maintain expression of maternal microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) from the Gtl2-Rian-Mirg locus, which is essential for full pluripotency of iPSCs. In the absence of PRC2, the entire locus becomes transcriptionally repressed due to gain of DNAme at the intergenic differentially methylated regions (IG-DMRs). Furthermore, we demonstrate that the IG-DMR serves as an enhancer of the maternal Gtl2-Rian-Mirg locus. Further analysis reveals that PRC2 interacts physically with Dnmt3 methyltransferases and reduces recruitment to and subsequent DNAme at the IG-DMR, thereby allowing for proper expression of the maternal Gtl2-Rian-Mirg locus. Our observations are consistent with a mechanism through which PRC2 counteracts the action of Dnmt3 methyltransferases at an imprinted locus required for full pluripotency.
Yen, Kellis
Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.
Madabhushi, Gao, Pfenning, Pan, Yamakawa, Seo, Rueda, Phan, Yamakawa, Pao, Stott, Gjoneska, Nott, Cho, Kellis, Tsai.
Neuronal activity causes the rapid expression of immediate early genes that are crucial for experience-driven changes to synapses, learning, and memory. Here, using both molecular and genome-wide next-generation sequencing methods, we report that neuronal activity stimulation triggers the formation of DNA double strand breaks (DSBs) in the promoters of a subset of early-response genes, including Fos, Npas4, and Egr1. Generation of targeted DNA DSBs within Fos and Npas4 promoters is sufficient to induce their expression even in the absence of an external stimulus. Activity-dependent DSB formation is likely mediated by the type II topoisomerase, Topoisomerase II-beta (Topo II-beta), and knockdown of Topo II-beta attenuates both DSB formation and early-response gene expression following neuronal stimulation. Our results suggest that DSB formation is a physiological event that rapidly resolves topological constraints to early-response gene expression in neurons
Hatchi, Skourti-Stathaki, Ventz, Pinello, Yen, Kamieniarz-Gdula, Dimitrov, Pathania, McKinney, Eaton, Kellis, Hill, Parmigiani, Proudfoot, Livingston
The mechanisms contributing to transcription-associated genomic instability are both complex and incompletely understood. Although R-loops are normal transcriptional intermediates, they are also associated with genomic instability. Here, we show that BRCA1 is recruited to R-loops that form normally over a subset of transcription termination regions. There it mediates the recruitment of a specific, physiological binding partner, senataxin (SETX). Disruption of this complex led to R-loop-driven DNA damage at those loci as reflected by adjacent gamma-H2AX accumulation and ssDNA breaks within the untranscribed strand of relevant R-loop structures. Genome-wide analysis revealed widespread BRCA1 binding enrichment at R-loop-rich termination regions (TRs) of actively transcribed genes. Strikingly, within some of these genes in BRCA1 null breast tumors, there are specific insertion/deletion mutations located close to R-loop-mediated BRCA1 binding sites within TRs. Thus, BRCA1/SETX complexes support a DNA repair mechanism that addresses R-loop-based DNA damage at transcriptional pause sites
Elliott, Hong, Xing, Zhou, Li, Coarfa, Bell, Maire, Ligon, Sigaroudinia, Gascard, Tlsty, Harris, Schalkwyk, Bilenky, Mill, Farnham, Kellis, Marra, Milosavljevic, Hirst, Stormo, Wang, Costello
The role of intermediate methylation states in DNA is unclear. Here, to comprehensively identify regions of intermediate methylation and their quantitative relationship with gene activity, we apply integrative and comparative epigenomics to 25 human primary cell and tissue samples. We report 18,452 intermediate methylation regions located near 36% of genes and enriched at enhancers, exons and DNase I hypersensitivity sites. Intermediate methylation regions average 57% methylation, are predominantly allele-independent and are conserved across individuals and between mouse and human, suggesting a conserved function. These regions have an intermediate level of active chromatin marks and their associated genes have intermediate transcriptional activity. Exonic intermediate methylation correlates with exon inclusion at a level between that of fully methylated and unmethylated exons, highlighting gene context-dependent functions. We conclude that intermediate DNA methylation is a conserved signature of gene regulation and exon usage
Ernst, Kellis
With hundreds of epigenomic maps, the opportunity arises to exploit the correlated nature of epigenetic signals, across both marks and samples, for large-scale prediction of additional datasets. Here, we undertake epigenome imputation by leveraging such correlations through an ensemble of regression trees. We impute 4,315 high-resolution signal maps, of which 26% are also experimentally observed. Imputed signal tracks show overall similarity to observed signals and surpass experimental datasets in consistency, recovery of gene annotations and enrichment for disease-associated variants. We use the imputed data to detect low-quality experimental datasets, to find genomic sites with unexpected epigenomic signals, to define high-priority marks for new experiments and to delineate chromatin states in 127 reference epigenomes spanning diverse tissues and cell types. Our imputed datasets provide the most comprehensive human regulatory region annotation to date, and our approach and the ChromImpute software constitute a useful complement to large-scale experimental mapping of epigenomic information.
Ho, Jung, Liu, Alver, Lee, Ikegami, Sohn, Minoda, Tolstorukov, Appert, Parker, Gu, Kundaje, Riddle, Bishop, Egelhofer, Hu, Alekseyenko, Rechtsteiner, Asker, Belsky, Bowman, Chen, Chen, Day, Dong, Dose, Duan, Epstein, Ercan, Feingold, Ferrari, Garrigues, Gehlenborg, Good, Haseley, He, Herrmann, Hoffman, Jeffers, Kharchenko, Kolasinska-Zwierz, Kotwaliwale, Kumar, Langley, Larschan, Latorre, Libbrecht, Lin, Park, Pazin, Pham, Plachetka, Qin, Schwartz, Shoresh, Stempor, Vielle, Wang, Whittle, Xue, Kingston, Kim, Bernstein, Dernburg, Pirrotta, Kuroda, Noble, Tullius, Kellis, MacAlpine, Strome, Elgin, Liu, Lieb, Ahringer, Karpen, Park
Genome function is dynamically regulated in part by chromatin, which consists of the histones, non-histone proteins and RNA molecules that package DNA. Studies in Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular mechanisms of genome function in humans, and have revealed conservation of chromatin components and mechanisms. Nevertheless, the three organisms have markedly different genome sizes, chromosome architecture and gene organization. On human and fly chromosomes, for example, pericentric heterochromatin flanks single centromeres, whereas worm chromosomes have dispersed heterochromatin-like regions enriched in the distal chromosomal 'arms', and centromeres distributed along their lengths. To systematically investigate chromatin organization and associated gene regulation across species, we generated and analysed a large collection of genome-wide chromatin data sets from cell lines and developmental stages in worm, fly and human. Here we present over 800 new data sets from our ENCODE and modENCODE consortia, bringing the total to over 1,400. Comparison of combinatorial patterns of histone modifications, nuclear lamina-associated domains, organization of large-scale topological domains, chromatin environment at promoters and enhancers, nucleosome positioning, and DNA replication patterns reveals many conserved features of chromatin organization among the three organisms. We also find notable differences in the composition and locations of repressive chromatin. These data sets and analyses provide a rich resource for comparative and species-specific investigations of chromatin composition, organization and function
Hammonds, Bristow, Fisher, Weiszmann, Wu, Hartenstein, Kellis, Yu, Frise, Celniker
Site-specific transcription factors (TFs) bind DNA regulatory elements to control expression of target genes, forming the core of gene regulatory networks. Despite decades of research, most studies focus on only a small number of TFs and the roles of many remain unknown. We present a systematic characterization of spatiotemporal gene expression patterns for all known or predicted Drosophila TFs throughout embryogenesis, the first such comprehensive study for any metazoan animal. We generated RNA expression patterns for all 708 TFs by in situ hybridization, annotated the patterns using an anatomical controlled vocabulary, and analyzed TF expression in the context of organ system development. Nearly all TFs are expressed during embryogenesis and more than half are specifically expressed in the central nervous system. Compared to other genes, TFs are enriched early in the development of most organ systems, and throughout the development of the nervous system. Of the 535 TFs with spatially restricted expression, 79% are dynamically expressed in multiple organ systems while 21% show single-organ specificity. Of those expressed in multiple organ systems, 77 TFs are restricted to a single organ system either early or late in development. Expression patterns for 354 TFs are characterized for the first time in this study. We produced a reference TF dataset for the investigation of gene regulatory networks in embryogenesis, and gained insight into the expression dynamics of the full complement of TFs controlling the development of each organ system.
Mortazavi, Pepke, Jansen, Marinov, Ernst, Kellis, Hardison, Myers, Wold.
We tested whether Self-Organizing Maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq datasets from six biologically diverse cell lines studied by the ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional datasets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.
The regions bound by sequence-specific transcription factors can be highly variable across different cell types, despite the static nature of the underlying genome sequence. This has been partly attributed to changes in chromatin accessibility, but a systematic picture has been hindered by the lack of large-scale datasets. Here, we use 456 binding experiments for 119 regulators and 84 chromatin maps generated by ENCODE in six human cell types and relate those to a global map of regulatory motif instances for these factors. We find specific and robust chromatin state preferences for each regulator beyond the previously-reported open-chromatin association, suggesting a much richer chromatin landscape beyond simple accessibility. The preferentially-bound chromatin states of regulators were enriched for sequence motifs of regulators relative to all states, suggesting that these preferences are at least partly encoded by the genomic sequence. Relative to all regions bound by a regulator however, regulatory motifs were surprisingly depleted in the regulator's preferentially-bound states, suggesting additional non-sequence-specific binding beyond the level predicted by the regulatory motifs. Such permissive binding was largely restricted to open-chromatin regions showing histone modification marks characteristic of active enhancer and promoter regions, whereas open-chromatin regions lacking such marks did not show permissive binding. Lastly, the vast majority of co-binding of regulator pairs is predicted by the chromatin state preferences of individual regulators. Overall, our results suggest a joint role of sequence motifs and specific chromatin states beyond mere accessibility in mediating regulator binding dynamics across different cell types.
Rajagopal, Xie, Li, Wagner, Wang, Stamatoyannopoulos, Ernst, Kellis, Ren
Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer identification from Chromatin States) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.
Hoffman*, Ernst*, Wilder, Kundaje, Harris, Libbrecht, Giardine, Ellenbogen, Bilmes, Birney, Hardison, Dunham, Kellis*, Noble*
The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape
Jin, Ernst, Tiedemann, Xu, Sureshchandra, Kellis, Dalton, Liu, Choi, Robertson
DNA methylation, mediated by the combined action of three DNA methyltransferases (DNMT1, DNMT3A, and DNMT3B), is essential for mammalian development and is a major contributor to cellular transformation. To elucidate how DNA methylation is targeted, we mapped the genome-wide localization of all DNMTs and methylation, and examined the relationships among these markers, histone modifications, and nucleosome structure in a pluripotent human tumor cell line in its undifferentiated and differentiated states. Our findings reveal a strong link between DNMTs and transcribed loci, and that DNA methylation is not a simple sum of DNMT localization patterns. A comparison of the epigenomes of normal and cancerous stem cells, and pluripotent and differentiated states shows that the presence of at least two DNMTs is strongly associated with loci targeted for DNA hypermethylation. Taken together, these results shed important light on the determinants of DNA methylation and how it may become disrupted in cancer cells
Meuleman, Peric-Hupkes, Kind, Beaudry, Pagie, Kellis, Reinders, Wessels, van Steensel
In metazoans, the nuclear lamina is thought to play an important role in the spatial organization of interphase chromosomes, by providing anchoring sites for large genomic segments named lamina-associated domains (LADs). Some of these LADs are cell-type specific, while many others appear constitutively associated with the lamina. Constitutive LADs (cLADs) may contribute to a basal chromosome architecture. By comparison of mouse and human lamina interaction maps we find that the sizes and genomic positions of cLADs are strongly conserved. Moreover, cLADs are depleted of synteny breakpoints, pointing to evolutionary selective pressure to keep cLADs intact. Paradoxically, the overall sequence conservation is low for cLADs. Instead, cLADs are universally characterized by long stretches of DNA of high A/T content. Cell-type specific LADs also tend to adhere to this "A/T-rule" in embryonic stem cells, but not in differentiated cells. This suggests that the A/T-rule represents a default positioning mechanism that is locally overruled during lineage commitment. Analysis of paralogs suggests that during evolution changes in A/T-content have driven the relocation of genes to and from the nuclear lamina, in tight association with changes in expression level. Taken together, these results reveal that the spatial organization of mammalian genomes is highly conserved and tightly linked to local nucleotide composition.
Landt, Marinov, Kundaje, Kheradpour, Pauli, Batzoglou, Bernstein, Bickel, Brown, Cayting, Chen, Desalvo, Epstein, Fisher-Aylor, Euskirchen, Gerstein, Gertz, Hartemink, Hoffman, Iyer, Jung, Karmakar, Kellis, Kharchenko, Li, Liu, Liu, Ma, Milosavljevic, Myers, Park, Pazin, Perry, Raha, Reddy, Rozowsky, Shoresh, Sidow, Slattery, Stamatoyannopoulos, Tolstorukov, White, Xi, Farnham, Lieb, Wold, Snyder
Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals
Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type-specific activity patterns and for interpreting disease-association studies. However, the computational challenge of learning chromatin-state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise. To address this challenge, we developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets and visualizing the resulting genome-wide maps of chromatin-state annotations.
Ram, Goren, Amit, Shoresh, Yosef, Ernst, Kellis, Gymrek, Issner, Coyne, Durham, Zhang, Donaghey, Epstein, Regev, Bernstein
Hundreds of chromatin regulators (CRs) control chromatin structure and function by catalyzing and binding histone modifications, yet the rules governing these key processes remain obscure. Here, we present a systematic approach to infer CR function. We developed ChIP-string, a meso-scale assay that combines chromatin immunoprecipitation with a signature readout of 487 representative loci. We applied ChIP-string to screen 145 antibodies, thereby identifying effective reagents, which we used to map the genome-wide binding of 29 CRs in two cell types. We found that specific combinations of CRs colocalize in characteristic patterns at distinct chromatin environments, at genes of coherent functions, and at distal regulatory elements. When comparing between cell types, CRs redistribute to different loci but maintain their modular and combinatorial associations. Our work provides a multiplex method that substantially enhances the ability to monitor CR binding, presents a large resource of CR maps, and reveals common principles for combinatorial CR function.
Wu, Cheng, Keller, Ernst, Kumar, Mishra, Morrissey, Dorman, Chen, Drautz, Giardine, Shibata, Song, Pimkin, Crawford, Furey, Kellis, Miller, Taylor, Schuster, Zhang, Chiaromonte, Blobel, Weiss, Hardison.
Interplays among lineage-specific nuclear proteins, chromatin modifying enzymes, and the basal transcription machinery govern cellular differentiation, but their dynamics of action and coordination with transcriptional control are not fully understood. Alterations in chromatin structure appear to establish a permissive state for gene activation at some loci, but they play an integral role in activation at other loci. To determine the predominant roles of chromatin states and factor occupancy in directing gene regulation during differentiation, we mapped chromatin accessibility, histone modifications, and nuclear factor occupancy genome-wide during mouse erythroid differentiation dependent on the master regulatory transcription factor GATA1. Notably, despite extensive changes in gene expression, the chromatin state profiles (proportions of a gene in a chromatin state dominated by activating or repressive histone modifications) and accessibility remain largely unchanged during GATA1-induced erythroid differentiation. In contrast, gene induction and repression are strongly associated with changes in patterns of transcription factor occupancy. Our results indicate that during erythroid differentiation, the broad features of chromatin states are established at the stage of lineage commitment, largely independently of GATA1. These determine permissiveness for expression, with subsequent induction or repression mediated by distinctive combinations of transcription factors.
Magklara, Yen, Colquitt, Clowney, Allen, Markenscoff-Papadimitriou, Evans, Kheradpour, Mountoufaris, Carey, Barnea, Kellis, Lomvardas
Constitutive heterochromatin is traditionally viewed as the static form of heterochromatin that silences pericentromeric and telomeric repeats in a cell cycle- and differentiation-independent manner. Here, we show that, in the mouse olfactory epithelium, olfactory receptor (OR) genes are marked in a highly dynamic fashion with the molecular hallmarks of constitutive heterochromatin, H3K9me3 and H4K20me3. The cell type and developmentally dependent deposition of these marks along the OR clusters are, most likely, reversed during the process of OR choice to allow for monogenic and monoallelic OR expression. In contrast to the current view of OR choice, our data suggest that OR silencing takes place before OR expression, indicating that it is not the product of an OR-elicited feedback signal. Our findings suggest that chromatin-mediated silencing lays a molecular foundation upon which singular and stochastic selection for gene expression can be applied.
Kharchenko, Alekseyenko, Schwartz, Minoda, Riddle, Ernst, Sabo, Larschan, Gorchakov, Gu, Linder-Basso, Plachetka, Shanower, Tolstorukov, Bishop, Canfield, Sandstrom, Thurman, Stamatoyannopoulos, Kellis, Elgin, Kuroda, Pirotta, Karpen,
We present a genome-wide map of the chromatin landscape for Drosophila melanogaster, based on the distributions of 18 histone modifications and 9 combinatorial patterns identified by computational analysis. Integrative analysis with other genome-wide mapping data (non-histone chromatin proteins, DNaseI hypersensitive sites, GRO-seq, short/long RNA expression) reveals distinct properties of chromosomes, genes, regulatory elements and other functional domains. In addition to highlighting the special identities of the male X and the 4th chromosomes, this analysis identifies distinct chromatin signatures among active genes that are correlated with differences in gene length, exonic structure, regulatory function, and genomic context. It also reveals a diversity of chromatin signatures among Polycomb targets, including a subset with paused RNA polymerase. This systematic profiling and integrative analysis of chromatin signatures provides important insights into the differential packaging of functional elements, and will serve as a valuable resource for future experimental investigations of genome structure and function.
Bernstein, Stamatoyannopoulos, Costello, Ren, Milosavljevic, Meissner, Kellis, Marra, Beaudet, Ecker, Farnham, Hirst, Lander, Mikkelsen, Thomson,
The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease. The maps will detail the genomewide landscapes of DNA methylation, histone modifications and related chromatin features. They are intended to provide a reference for studies of the genetic and epigenetic events that underlie human development, diversity and disease. Here we describe the organizational structure, goals and anticipated deliverables of the consortium.
A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in human T cells, based on recurrent and spatially coherent combinations of chromatin marks. We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.
Nčgre, Brown, Shah, Kheradpour, Morrison, Henikoff, Feng, Ahmad, Russell, White, Stein, Henikoff, Kellis, White
Insulators are DNA sequences that control the interactions among genomic regulatory elements and act as chromatin boundaries. A thorough understanding of their location and function is necessary to address the complexities of metazoan gene regulation. We studied by ChIP-chip the genome-wide binding sites of 6 insulator-associated proteins-dCTCF, CP190, BEAF-32, Su(Hw), Mod(mdg4), and GAF-to obtain the first comprehensive map of insulator elements in Drosophila embryos. We identify over 14,000 putative insulators, including all classically defined insulators. We find two major classes of insulators defined by dCTCF/CP190/BEAF-32 and Su(Hw), respectively. Distributional analyses of insulators revealed that particular sub-classes of insulator elements are excluded between cis-regulatory elements and their target promoters; divide differentially expressed, alternative, and divergent promoters; act as chromatin boundaries; are associated with chromosomal breakpoints among species; and are embedded within active chromatin domains. Together, these results provide a map demarcating the boundaries of gene regulatory units and a framework for understanding insulator function during the development and evolution of Drosophila.
Heintzman, Hon, Hawkins, Kheradpour, Stark, Harp, Ye, Lee, Stuart, Ching, Ching, Antosiewicz-Bourget, Liu, Zhang, Green, Lobanenkov, Stewart, Thomson, Crawford, Kellis, Ren
The human body is composed of diverse cell types with distinct functions. Although it is known that lineage specification depends on cell-specific gene expression, which in turn is driven by promoters, enhancers, insulators and other cis-regulatory DNA sequences for each gene, the relative roles of these regulatory elements in this process are not clear. We have previously developed a chromatin-immunoprecipitation-based microarray method (ChIP-chip) to locate promoters, enhancers and insulators in the human genome. Here we use the same approach to identify these elements in multiple cell types and investigate their roles in cell-type-specific gene expression. We observed that the chromatin state at promoters and CTCF-binding at insulators is largely invariant across diverse cell types. In contrast, enhancers are marked with highly cell-type-specific histone modification patterns, strongly correlate to cell-type-specific gene expression programs on a global scale, and are functionally active in a cell-type-specific manner. Our results define over 55,000 potential transcriptional enhancers in the human genome, significantly expanding the current catalogue of human enhancers and highlighting the role of these elements in cell-type-specific gene expression.
Zeitlinger, Stark, Kellis, Hong, Nechaev, Adelman, Levine, Young
It is widely assumed that the key rate-limiting step in gene activation is the recruitment of RNA polymerase II (Pol II) to the core promoter. Although there are well-documented examples in which Pol II is recruited to a gene but stalls, a general role for Pol II stalling in development has not been established. We have carried out comprehensive Pol II chromatin immunoprecipitation microarray (ChIP-chip) assays in Drosophila embryos and identified three distinct Pol II binding behaviors: active (uniform binding across the entire transcription unit), no binding, and stalled (binding at the transcription start site). The notable feature of the approximately 10% genes that are stalled is that they are highly enriched for developmental control genes, which are either repressed or poised for activation during later stages of embryogenesis. We propose that Pol II stalling facilitates rapid temporal and spatial changes in gene activity during development.
Zeitlinger, Zinzen, Stark, Kellis, Zhang, Young, Levine
Genetic studies have identified numerous sequence-specific transcription factors that control development, yet little is known about their in vivo distribution across animal genomes. We determined the genome-wide occupancy of the dorsoventral (DV) determinants Dorsal, Twist, and Snail in the Drosophila embryo using chromatin immunoprecipitation coupled with microarray analysis (ChIP-chip). The in vivo binding of these proteins correlate tightly with the limits of known enhancers. Our analysis predicts substantially more target genes than previous estimates, and includes Dpp signaling components and anteroposterior (AP) segmentation determinants. Thus, the ChIP-chip data uncover a much larger than expected regulatory network, which integrates diverse patterning processes during development.
Harbison, Gordon, Lee, Rinaldi, Macisaac, Danford, Hannett, Tagne, Reynolds, Yoo, Jennings, Zeitlinger, Pokholok, Kellis, Rolfe, Takusagawa, Lander, Gifford, Fraenkel, Young
DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.
Beyond a comprehensive encyclopedia of genomic elements we need to understand how these elements are put together in cellular circuits and to infer predictive models of cellular response, which can serve as foundations for modern medicine, rational drug design, and personalized treatments. Cellular regulatory networks that control gene expression are dictated by the binding of DNA and RNA regulators to sequence motifs typically only 6-8 nucleotides long. These 'regulatory motifs' have previously remained elusive to systematic discovery due to their short lengths and varying distances at which they can act. However, by evaluating their genome-wide conservation patterns, their positional and strand biases, and their association with functional genomic datasets, our work has pioneered new methods for their systematic discovery, the characterization of their functional roles, and the inference of the regulatory networks they define. We also mined time-course and multi-cell type datasets to reveal the stage- and tissue-specific action of the regulators that recognize these motifs, and to build computational models that predict gene expression from the combined action of multiple regulators for each gene.
Park, Kellis
A fundamental unit of gene-regulatory control is the contact between a regulatory protein and its target DNA or RNA molecule. Biophysical models that directly predict these interactions are incomplete and confined to specific types of structures, but computational analysis of large-scale experimental datasets allows regulatory motifs to be identified by their over- representation in target sequences. In this issue, Alipanahi et al describe the use of a deep learning strategy to calculate protein-nucleic acid interactions from diverse experimental data sets. They show that their algorithm, called DeepBind, is broadly applicable and results in increased predictive power compared to traditional single-domain methods, and they use its predictions to discover regulatory motifs, to predict RNA editing and alternative splicing, and to interpret genetic variants. Looking beyond regulatory motifs, the current results illustrate the power of deep learning for biological data analysis in general. The approach can increase predictive power for specific tasks, integrate diverse datasets across data types, and provide greater generalization given the focus on representation learning and not simply classification accuracy. Systematic visualization and exploration of internal representations at each layer can yield mechanistic insights and guide new experiments and research directions. More broadly, deep learning can serve as a guiding principle to organize both hypothesis-driven research and exploratory investigation. For this potential to be realized, statistical and biological tasks must be integrated at all levels, including study design, experiment planning, model building and refinement, and data interpretation.
Rogers, Barrera, Reyon, Sander, Kellis, Joung, Bulyk
Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE-DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to 5,000-20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE-DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.
Zhao, Barrera, Ersing, Willox, Schmidt, Greenfeld, Zhou, Mollo, Shi, Takasaki, Jiang, Cahir-McFarland, Kellis, Bulyk, Kieff, Gewurz
The nuclear factor kB (NF-kB) subunits RelA, RelB, cRel, p50, and p52 are each critical for B cell development and function. To systematically characterize their responses to canonical and noncanonical NF-kB pathway activity, we performed chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq) analysis in lymphoblastoid B cell lines (LCLs). We found a complex NF-kB-binding landscape, which did not readily reflect the two NF-kB pathway paradigms. Instead, 10 subunit-binding patterns were observed at promoters and 11 at enhancers. Nearly one-third of NF-kB-binding sites lacked kB motifs and were instead enriched for alternative motifs. The oncogenic forkhead box protein FOXM1 co-occupied nearly half of NF-kB-binding sites and was identified in protein complexes with NF-kB on DNA. FOXM1 knockdown decreased NF-kB target gene expression and ultimately induced apoptosis, highlighting FOXM1 as a synthetic lethal target in B cell malignancy. These studies provide a resource for understanding mechanisms that underlie NF-kB nuclear activity and highlight opportunities for selective NF-kB blockade
Slattery, Ma, Spokony, Arthur, Kheradpour, Kundaje, Nčgre, Crofts, Ptashkin, Zieba, Ostapenko, Suchy, Victorsen, Jameel, Grundstad, Gao, Moran, Rehm, Grossman, Kellis, White.
Annotation of regulatory elements and identification of the transcription-related factors (TRFs) targeting these elements are key steps in understanding how cells interpret their genetic blueprint and their environment during development, and how that process goes awry in the case of disease. One goal of the modENCODE (model organism ENCyclopedia of DNA Elements) Project is to survey a diverse sampling of TRFs, both DNA-binding and non-DNA-binding factors, to provide a framework for the subsequent study of the mechanisms by which transcriptional regulators target the genome. Here we provide an updated map of the Drosophila melanogaster regulatory genome based on the location of 84 TRFs at various stages of development. This regulatory map reveals a variety of genomic targeting patterns, including factors with strong preferences toward proximal promoter binding, factors that target intergenic and intronic DNA, and factors with distinct chromatin state preferences. The data also highlight the stringency of the Polycomb regulatory network, and show association of the Trithorax-like (Trl) protein with hotspots of DNA binding throughout development. Furthermore, the data identify more than 5800 instances in which TRFs target DNA regions with demonstrated enhancer activity. Regions of high TRF co-occupancy are more likely to be associated with open enhancers used across cell types, while lower TRF occupancy regions are associated with complex enhancers that are also regulated at the epigenetic level. Together these data serve as a resource for the research community in the continued effort to dissect transcriptional regulatory mechanisms directing Drosophila development.
Kheradpour, Kellis
Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species
Kheradpour, Ernst, Melnikov, Rogov, Wang, Zhang, Alston, Mikkelsen, Kellis
Genome-wide chromatin maps have permitted the systematic mapping of putative regulatory elements across multiple human cell types, revealing tens of thousands of candidate distal enhancer regions. However, until recently, their experimental dissection by directed regulatory motif disruption has remained unfeasible at the genome scale, due to the technological lag in large-scale DNA synthesis. Here, we employ a massively parallel reporter assay (MPRA) to measure the transcriptional levels induced by 145bp DNA segments centered on evolutionarily-conserved regulatory motif instances and found in enhancer chromatin states. We select five predicted activators (HNF1, HNF4, FOXA, GATA, NFE2L2) and two predicted repressors (GFI1, ZFP161) and measure reporter expression in erythroleukemia (K562) and liver carcinoma (HepG2) cell lines. We test 2,104 wild-type sequences and an additional 3,314 engineered enhancer variants containing targeted motif disruptions, each using 10 barcode tags in two cell lines and 2 replicates. The resulting data strongly confirm the enhancer activity and cell type specificity of enhancer chromatin states, the ability of 145bp segments to recapitulate both, the necessary role of regulatory motifs in enhancer function, and the complementary roles of activator and repressor motifs. We find statistically robust evidence that (1) scrambling, removing, or disrupting the predicted activator motifs abolishes enhancer function, while silent or motif-improving changes maintain enhancer activity; (2) evolutionary conservation, nucleosome exclusion, binding of other factors, and strength of the motif match are all associated with wild-type enhancer activity; (3) scrambling repressor motifs leads to aberrant reporter expression in cell lines where the enhancers are usually not active. Our results suggest a general strategy for deciphering cis-regulatory elements by systematic large-scale experimental manipulation, and provide quantitative enhancer activity measurements across thousands of constructs that can be mined to generate and test predictive models of gene expression
Spivakov, Akhtar, Kheradpour, Beal, Girardot, Koscielny, Herrero, Kellis, Furlong, Birney
Advances in sequencing technology have boosted population genomics and made it possible to map the positions of transcription factor binding sites (TFBSs) with high precision. Here we investigate TFBS variability by combining transcription factor binding maps generated by ENCODE, modENCODE, our previously published data and other sources with genomic variation data for human individuals and Drosophila isogenic lines. We introduce a metric of TFBS variability that takes into account changes in motif match associated with mutation and makes it possible to investigate TFBS functional constraints instance-by-instance as well as in sets that share common biological properties. We also take advantage of the emerging per-individual transcription factor binding data to show evidence that TFBS mutations, particularly at evolutionarily conserved sites, can be efficiently buffered to ensure coherent levels of transcription factor binding. Our analyses provide insights into the relationship between individual and interspecies variation and show evidence for the functional buffering of TFBS mutations in both humans and flies. In a broad perspective, these results demonstrate the potential of combining functional genomics and population genetics approaches for understanding gene regulation.
Melnikov, Murugan, Zhang, Tesileanu, Wang, Rogov, Feizi, Gnirke, Callan, Kinney, Kellis, Lander, Mikkelsen
Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare >27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-beta enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.
Negre, Brown, Ma, Bristow, Miller, Kheradpour, Loriaux, Sealfon, Li, Ishii, Spokony, Chen, Hwang, Wagner, Auburn, Domanus, Shah, Morrison, Zieba, Suchy, Senderowicz, Victorsen, Bild, Grundstad, Hanley, Mannervik, Venken, Bellen, White, Russell, Grossman, Ren, Posakony, Kellis, White
Following the sequencing of human and model organism genomes, genome-wide annotation of regulatory information has emerged as a major challenge. Here we describe an initial map of the Drosophila melanogaster regulatory genome based on the developmental dynamics of chromatin modifications and chromatin modifying enzymes, on polymerase occupancy of promoters, on the dynamic binding of enhancer-associated proteins such as the transcriptional co-factor CBP, and on the localization of forty-one site-specific transcription factors at different stages of development. The entire dataset provides protein modification and binding annotations across 94% of the genome along with prediction and validation of 4 classes of regulatory elements: insulators, promoters, silencers and enhancers. This regulatory map reveals several newly discovered properties of genome regulation, including the lack of epigenetic marks at promoters of transiently expressed genes, the association of specific Histone Deacetylases (HDACs) with Polycomb Response Elements, the early role of CBP as a marker of enhancers and the occurence of high-occupancy transcription factor binding sites that correlate with gene expression. Using these data we also generated a combinatorial analysis of transcription factors and DNA sequence motifs that are associated with different sets of developmentally co-expressed genes, providing a database for discovering the sets of regulatory inputs that control regulatory element function. Together, these cis-regulatory annotations serve as a foundation for further detailed analyses of the genomic regulatory code in Drosophila.
Xie, Mikkelsen, Gnirke, Lindblad-Toh, Kellis, Lander
Conserved noncoding elements (CNEs) constitute the majority of sequences under purifying selection in the human genome, yet their function remains largely unknown. Experimental evidence suggests that many of these elements play regulatory roles, but little is known about regulatory motifs contained within them. Here we describe a systematic approach to discover and characterize regulatory motifs within mammalian CNEs by searching for long motifs (12-22 nt) with significant enrichment in CNEs and studying their biochemical and genomic properties. Our analysis identifies 233 long motifs (LMs), matching a total of approximately 60,000 conserved instances across the human genome. These motifs include 16 previously known regulatory elements, such as the histone 3'-UTR motif and the neuron-restrictive silencer element, as well as striking examples of novel functional elements. The most highly enriched motif (LM1) corresponds to the X-box motif known from yeast and nematode. We show that it is bound by the RFX1 protein and identify thousands of conserved motif instances, suggesting a broad role for the RFX family in gene regulation. A second group of motifs (LM2*) does not match any previously known motif. We demonstrate by biochemical and computational methods that it defines a binding site for the CTCF protein, which is involved in insulator function to limit the spread of gene activation. We identify nearly 15,000 conserved sites that likely serve as insulators, and we show that nearby genes separated by predicted CTCF sites show markedly reduced correlation in gene expression. These sites may thus partition the human genome into domains of expression.
Chiang, Moses, Kellis, Lander, Eisen
Transcriptional regulation in eukaryotes is often multifactorial, involving multiple transcription factors binding to the same transcription control region (e.g., upstream activating sequences and enhancers), and to understand the regulatory content of eukaryotic genomes it is necessary to consider the cooccurrence and spatial relationships of individual binding sites. The identification of sequences conserved among related species (often known as phylogenetic footprinting) has been successfully used to identify individual transcription factor binding sites. Here, we extend this concept of functional conservation to higher-order features of transcription control regions involved in the multifactorial control of gene expression. We used the genome sequences of four yeast species of the genus Saccharomyces to identify sequences potentially involved in multifactorial control of gene expression. We found 1,117 potential regulatory "templates": pairs of hexameric sequences that are jointly conserved in transcription regulatory regions and also exhibit non-random relative spacing. Many of the individual sequences in these templates correspond to known transcription factor binding sites, and the sets of genes containing a particular template in their transcription control regions tend to be differentially expressed in conditions where the corresponding transcription factors are known to be active. The incorporation of both joint conservation and spacing constraints of sequence pairs predicts groups of target genes that were specific for common patterns of gene expression. Our work suggests that positional information, especially the relative spacing between transcription factor binding sites, may represent a common organizing principle of transcription control regions.
Moses, Chiang, Kellis, Lander, Eisen
The binding sites of sequence specific transcription factors are an important and relatively well-understood class of functional non-coding DNAs. Although a wide variety of experimental and computational methods have been developed to characterize transcription factor binding sites, they remain difficult to identify. Comparison of non-coding DNA from related species has shown considerable promise in identifying these functional non-coding sequences, even though relatively little is known about their evolution. Here we analyse the genome sequences of the budding yeasts Saccharomyces cerevisiae, S. bayanus, S. paradoxus and S. mikatae to study the evolution of transcription factor binding sites. As expected, we find that both experimentally characterized and computationally predicted binding sites evolve slower than surrounding sequence, consistent with the hypothesis that they are under purifying selection. We also observe position-specific variation in the rate of evolution within binding sites. We find that the position-specific rate of evolution is positively correlated with degeneracy among binding sites within S. cerevisiae. We test theoretical predictions for the rate of evolution at positions where the base frequencies deviate from background due to purifying selection and find reasonable agreement with the observed rates of evolution. Finally, we show how the evolutionary characteristics of real binding motifs can be used to distinguish them from artefacts of computational motif finding algorithms. CONCLUSION: As has been observed for protein sequences, the rate of evolution in transcription factor binding sites varies with position, suggesting that some regions are under stronger functional constraint than others. This variation likely reflects the varying importance of different positions in the formation of the protein-DNA complex. The characterization of the pattern of evolution in known binding sites will likely contribute to the effective use of comparative sequence data in the identification of transcription factor binding sites and is an important step toward understanding the evolution of functional non-coding DNA.
Syed, Stultz, Kellis, Indyk, Guttag
In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to efficiently search for such patterns in large datasets. This involves first transforming continuous physiological signals from patients into symbolic sequences, and then searching for patterns in these reduced representations that are strongly associated with an outcome.Our strategy of identifying conserved activity that is unlikely to have occurred purely by chance in symbolic data is analogous to the discovery of regulatory motifs in genomic datasets. We build upon existing work in this area, generalizing the notion of a regulatory motif and enhancing current techniques to operate robustly on non-genomic data. We also address two significant considerations associated with motif discovery in general: computational efficiency and robustness in the presence of degeneracy and noise. To deal with these issues, we introduce the concept of active regions and new subset-based techniques such as a two-layer Gibbs sampling algorithm. These extensions allow for a framework for information inference, where precursors are identified as approximately conserved activity of arbitrary complexity preceding multiple occurrences of an event.We evaluated our solution on a population of patients who experienced sudden cardiac death and attempted to discover electrocardiographic activity that may be associated with the endpoint of death. To assess the predictive patterns discovered, we compared likelihood scores for motifs in the sudden death population against control populations of normal individuals and those with non-fatal supraventricular arrhythmias. Our results suggest that predictive motif discovery may be able to identify clinically relevant information even in the absence of significant prior knowledge.
While the genome is static and its code one-dimentional, the binding of regulators to target genes, which can themselves be regulators of other targets, defines complex circuits of information flow, or 'regulatory networks'. However, defining these networks can be a major challenge given the complex and sometimes subtle relationships between regulators and their targets. To address these challenges, we developed methods for de novo discovery of regulator targets, using a phylogenetic framework for motif conservation, and rigorous statistics of motif confidence. We showed that computationally-derived regulator targets are of comparable quality and abundance as ChIP-derived binding sites (showing stronger overlap with target genes than ChIP, even outside ChIP-bound regions), and that miRNA targets are of even higher confidence. We presented the first regulatory network for a complete animal genome and studied its structural and functional properties. Combining evolutionarily conserved motif instances with chromatin binding and expression analysis, we have defined functional regulatory networks for yeast under multiple stress conditions, and fly across developmental stages. We have also developed graph algorithms for discovering recurrent patterns of network connectivity known as 'network motifs' in both directed and undirected graphs, revealing combinatorial connectivity patterns in the transcriptional machinery.
Pierson, Koller, Battle, Mostafavi, Ardlie, Getz, Wright, Kellis, Volpi, Dermitzakis
To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner
Boyle, Araya, Brdlik, Cayting, Cheng, Cheng, Gardner, Hillier, Janette, Jiang, Kasper, Kawli, Kheradpour, Kundaje, Li, Ma, Niu, Rehm, Rozowsky, Slattery, Spokony, Terrell, Vafeados, Wang, Weisdepp, Wu, Xie, Yan, Feingold, Good, Pazin, Huang, Bickel, Brenner, Reinke, Waterston, Gerstein, White, Kellis, Snyder
Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease
Feizi, Marbach, Medard, Kellis
Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines
Marbach, Costello, Kuffner, Vega, Prill, Camacho, Allison, DREAM5 Consortium, Kellis, Collins, Stolovitzky
Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
Marbach, Roy, Ay, Meyer, Candeias, Kahveci, Bristow, Kellis
Gaining insights on gene regulation from large-scale functional datasets is a grand challenge in systems biology. In this paper, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics datasets, and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily-conserved sequence motifs, gene expression, and chromatin modification datasets as input features. Applying these methods to Drosophila melanogaster, we predict ~300k regulatory edges in a network of ~600 TFs and 12k target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously-uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Lastly, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns), and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.
Kheradpour, Stark, Roy, Kellis
Gene expression is regulated pre- and post-transcriptionally via cis-regulatory DNA and RNA motifs. Identification of individual functional instances of such motifs in genome sequences is a major goal for inferring regulatory networks yet has been hampered due to the motifs' short lengths that lead to many chance matches and poor signal-to-noise ratios. In this paper, we develop a general methodology for the comparative identification of functional motif instances across many related species, using a phylogenetic framework that accounts for the evolutionary relationships between species, allows for motif movements, and is robust against missing data due to artifacts in sequencing, assembly, or alignment. We also provide a robust statistical framework for evaluating motif confidence, which enables us to translate evolutionary conservation into a confidence measure for each motif instance, correcting for varying motif length, composition, and background conservation of the target regions. We predict targets of fly transcription factors and miRNAs in alignments of 12 recently sequenced Drosophila species. When compared to extensive genome-wide experimental data, predicted targets are of high quality, matching and surpassing ChIP-chip microarrays and recovering miRNA targets with high sensitivity. The resulting regulatory network suggests significant redundancy between pre- and post-transcriptional regulation of gene expression.
Grochow, Kellis
The study of biological networks and network motifs can yield significant new insights into systems biology. Previous methods of discovering network motifs - network-centric subgraph enumeration and sampling - have been limited to motifs of 6 to 8 nodes, revealing only the smallest network components. New methods are necessary to identify larger network sub-structures and functional motifs. Here we present a novel algorithm for discovering large network motifs that achieves these goals, based on a novel symmetry-breaking technique, which eliminates repeated isomorphism testing, leading to an exponential speed-up over previous methods. This technique is made possible by reversing the traditional network-based search at the heart of the algorithm to a motif-based search, which also eliminates the need to store all motifs of a given size and enables parallelization and scaling. Additionally, our method enables us to study the clustering properties of discovered motifs, revealing even larger network elements. We apply this algorithm to the protein-protein interaction network and transcription regulatory network of S. cerevisiae, and discover several large network motifs, which were previously inaccessible to existing methods, including a 29-node cluster of 15-node motifs corresponding to the key transcription machinery of S. cerevisiae.
Presser, Elowitz, Kellis, Kishony
Gene duplication is an important mechanism in the evolution of protein interaction networks. Duplications are followed by the gain and loss of interactions, rewiring the network at some unknown rate. Because rewiring is likely to change the distribution of network motifs within the duplicated interaction set, it should be possible to study network rewiring by tracking the evolution of these motifs. We have developed a mathematical framework that, together with duplication data from comparative genomic and proteomic studies, allows us to infer the connectivity of the preduplication network and the changes in connectivity over time. We focused on the whole-genome duplication (WGD) event in Saccharomyces cerevisiae. The model allowed us to predict the frequency of intergene interaction before WGD and the post duplication probabilities of interaction gain and loss. We find that the predicted frequency of self-interactions in the preduplication network is significantly higher than that observed in today's network. This could suggest a structural difference between the modern and ancestral networks, preferential addition or retention of interactions between ohnologs, or selective pressure to preserve duplicates of self-interacting proteins.
Ay, Kellis, Kahveciy,
We consider the problem of aligning two metabolic pathways. Unlike traditional approaches, we do not restrict the alignment to one-to-one mappings between the molecules (nodes) of the input pathways (graphs). We follow the observation that in nature different organisms can perform the same or similar functions through different sets of reactions and molecules. The number and the topology of the molecules in these alternative sets often vary from one organism to another. With the motivation that an accurate biological alignment should be able to reveal these functionally similar molecule sets across different species, we develop an algorithm that first measures the similarities between different nodes using a mixture of homology and topological similarity. We combine the two metrics by employing an eigenvalue formulation. We then search for an alignment between the two input pathways that maximizes a similarity score, evaluated as the sum of the similarities of the mapped subsets of size at most a given integer k, and also does not contain any conflicting mappings. Here we prove that this maximization is NP-hard by a reduction from Maximum Weight Independent Set (MWIS) problem. We then convert our problem into an instance of MWIS and use an efficient vertex-selection strategy to extract the mappings that constitute our alignment. We name our algorithm SubMAP (Subnetwork Mappings in Alignment of Pathways). We evaluate its accuracy and performance on real datasets. Our empirical results demonstrate that SubMAP can identify biologically-relevant mappings that are missed by traditional alignment methods and, that it is scalable for metabolic pathways of arbitrary topology, including searching for a query pathway of size 70 against the complete KEGG database of 1,842 pathways.
A unique and defining feature of biological systems is their ability to adapt to changing environments and evolve new functions and pathways, while constantly subjected to the forces of mutation and natural selection. Understanding the mechanisms associated with evolutionary innovation can teach us how to better interpret evolutionary signatures and comparative genomics evidence, yield new insights about the function of diverse genomic elements from their shared ancestry, and in the longer term reveal insights on how to engineer more evolvable and noise-tolerant systems. With the availability of many complete genomes, we have taken a new approach to studying gene evolution at the whole-genome level, which has resulted in numerous new insights that wouldn't be visible at the single-gene level. This revealed a unique genomic signature for whole-genome duplication which we demonstrated in yeast and fish, and has since been used to resolve the ancestry of many species, including our early vertebrate ancestors.
Neafsey, Waterhouse, Abai, Aganezov, Alekseyev, Allen, Amon, Arcŕ, Arensburger, Artemov, Assour, Basseri, Berlin, Birren, Blandin, Brockman, Burkot, Burt, Chan, Chauve, Chiu, Christensen, Costantini, Davidson, Deligianni, Dottorini, Dritsou, Gabriel, Guelbeogo, Hall, Han, Hlaing, Hughes, Jenkins, Jiang, Jungreis, Kakani, Kamali, Kemppainen, Kennedy, Kirmitzoglou, Koekemoer, Laban, Langridge, Lawniczak, Lirakis, Lobo, Lowy, MacCallum, Mao, Maslen, Mbogo, McCarthy, Michel, Mitchell, Moore, Murphy, Naumenko, Nolan, Novoa, O'Loughlin, Oringanje, Oshaghi, Pakpour, Papathanos, Peery, Povelones, Prakash, Price, Rajaraman, Reimer, Rinker, Rokas, Russell, Sagnon, Sharakhova, Shea, Simao, Simard, Slotman, Somboon, Stegniy, Struchiner, Thomas, Tojo, Topalis, Tubio, Unger, Vontas, Walton, Wilding, Willis, Wu, Yan, Zdobnov, Zhou, Catteruccia, Christophides, Collins, Cornman, Crisanti, Donnelly, Emrich, Fontaine, Gelbart, Hahn, Hansen, Howell, Kafatos, Kellis, Lawson, Louis, Luckhart, Muskavitch, Ribeiro, Riehle, Sharakhov, Tu, Zwiebel, Besansky
Variation in vectorial capacity for human malaria among Anopheles mosquito species is determined by many factors, including behavior, immunity, and life history. To investigate the genomic basis of vectorial capacity and explore new avenues for vector control, we sequenced the genomes of 16 anopheline mosquito species from diverse locations spanning ~100 million years of evolution. Comparative analyses show faster rates of gene gain and loss, elevated gene shuffling on the X chromosome, and more intron losses, relative to Drosophila. Some determinants of vectorial capacity, such as chemosensory genes, do not show elevated turnover but instead diversify through protein-sequence changes. This dynamism of anopheline genes and genomes may contribute to their flexible capacity to take advantage of new ecological niches, including adapting to humans as primary hosts
Whitney*, Pfenning*, Howard, Blatti, Liu, Ward, Wang, Audet, Kellis, Mukherjee, Sinha, Hartemink, West, Jarvis
Songbirds represent an important model organism for elucidating molecular mechanisms that link genes with complex behaviors, in part because they have discrete vocal learning circuits that have parallels with those that mediate human speech. We found that ~10% of the genes in the avian genome were regulated by singing, and we found a striking regional diversity of both basal and singing-induced programs in the four key song nuclei of the zebra finch, a vocal learning songbird. The region-enriched patterns were a result of distinct combinations of region-enriched transcription factors (TFs), their binding motifs, and presinging acetylation of histone 3 at lysine 27 (H3K27ac) enhancer activity in the regulatory regions of the associated genes. RNA interference manipulations validated the role of the calcium-response transcription factor (CaRF) in regulating genes preferentially expressed in specific song nuclei in response to singing. Thus, differential combinatorial binding of a small group of activity-regulated TFs and predefined epigenetic enhancer activity influences the anatomical diversity of behaviorally regulated gene networks
Cheng, Ma, Kim, Wu, Cayting, Boyle, Sundaram, Xing, Dogan, Li, Euskirchen, Lin, Lin, Visel, Kawli, Yang, Patacsil, Keller, Giardine, Mouse ENCODE Consortium, Kundaje, Wang, Pennacchio, Weng, Hardison, Snyder
To broaden our understanding of the evolution of gene regulation mechanisms, we generated occupancy profiles for 34 orthologous transcription factors (TFs) in human-mouse erythroid progenitor, lymphoblast and embryonic stem-cell lines. By combining the genome-wide transcription factor occupancy repertoires, associated epigenetic signals, and co-association patterns, here we deduce several evolutionary principles of gene regulatory features operating since the mouse and human lineages diverged. The genomic distribution profiles, primary binding motifs, chromatin states, and DNA methylation preferences are well conserved for TF-occupied sequences. However, the extent to which orthologous DNA segments are bound by orthologous TFs varies both among TFs and with genomic location: binding at promoters is more highly conserved than binding at distal elements. Notably, occupancy-conserved TF-occupied sequences tend to be pleiotropic; they function in several tissues and also co-associate with many TFs. Single nucleotide variants at sites with potential regulatory functions are enriched in occupancy-conserved TF-occupied sequences
Yue, Cheng, Breschi, Vierstra, Wu, Ryba, Sandstrom, Ma, Davis, Pope, Shen, Pervouchine, Djebali, Thurman, Kaul, Rynes, Kirilusha, Marinov, Williams, Trout, Amrhein, Fisher-Aylor, Antoshechkin, DeSalvo, See, Fastuca, Drenkow, Zaleski, Dobin, Prieto, Lagarde, Bussotti, Tanzer, Denas, Li, Bender, Zhang, Byron, Groudine, McCleary, Pham, Ye, Kuan, Edsall, Wu, Rasmussen, Bansal, Kellis, Keller, Morrissey, Mishra, Jain, Dogan, Harris, Cayting, Kawli, Boyle, Euskirchen, Kundaje, Lin, Lin, Jansen, Malladi, Cline, Erickson, Kirkup, Learned, Sloan, Rosenbloom, Lacerda de Sousa, Beal, Pignatelli, Flicek, Lian, Kahveci, Lee, Kent, Ramalho Santos, Herrero, Notredame, Johnson, Vong, Lee, Bates, Neri, Diegel, Canfield, Sabo, Wilken, Reh, Giste, Shafer, Kutyavin, Haugen, Dunn, Reynolds, Neph, Humbert, Hansen, De Bruijn, Selleri, Rudensky, Josefowicz, Samstein, Eichler, Orkin, Levasseur, Papayannopoulou, Chang, Skoultchi, Gosh, Disteche, Treuting, Wang, Weiss, Blobel, Cao, Zhong, Wang, Good, Lowdon, Adams, Zhou, Pazin, Feingold, Wold, Taylor, Mortazavi, Weissman, Stamatoyannopoulos, Snyder, Guigo, Gingeras, Gilbert, Hardison, Beer, Ren; Mouse ENCODE Consortium
The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases
Flot, Hespeels, Li, Noel, Arkhipova, Danchin, Hejnol, Henrissat, Koszul, Aury, Barbe, Barthélémy, Bast, Bazykin, Chabrol, Couloux, Da Rocha, Da Silva, Gladyshev, Gouret, Hallatschek, Hecox-Lea, Labadie, Lejeune, Piskurek, Poulain, Rodriguez, Ryan, Vakhrusheva, Wajnberg, Wirth, Yushenova, Kellis, Kondrashov, Mark Welch, Pontarotti, Weissenbach, Wincker, Jaillon, Van Doninck
Loss of sexual reproduction is considered an evolutionary dead end for metazoans, but bdelloid rotifers challenge this view as they appear to have persisted asexually for millions of years. Neither male sex organs nor meiosis have ever been observed in these microscopic animals: oocytes are formed through mitotic divisions, with no reduction of chromosome number and no indication of chromosome pairing. However, current evidence does not exclude that they may engage in sex on rare, cryptic occasions. Here we report the genome of a bdelloid rotifer, Adineta vaga (Davis, 1873), and show that its structure is incompatible with conventional meiosis. At gene scale, the genome of A. vaga is tetraploid and comprises both anciently duplicated segments and less divergent allelic regions. However, in contrast to sexual species, the allelic regions are rearranged and sometimes even found on the same chromosome. Such structure does not allow meiotic pairing; instead, we find abundant evidence of gene conversion, which may limit the accumulation of deleterious mutations in the absence of meiosis. Gene families involved in resistance to oxidation, carbohydrate metabolism and defence against transposons are significantly expanded, which may explain why transposable elements cover only 3% of the assembled sequence. Furthermore, 8% of the genes are likely to be of non-metazoan origin and were probably acquired horizontally. This apparent convergence between bdelloids and prokaryotes sheds new light on the evolutionary significance of sex
Sealfon, Gire, Ellis, Calderwood, Qadri, Hensley, Kellis, Ryan, Larocque, Harris, Sabeti
Whole-genome sequencing is an important tool for understanding microbial evolution and identifying the emergence of functionally important variants over the course of epidemics. In October 2010, a severe cholera epidemic began in Haiti, with additional cases identified in the neighboring Dominican Republic. We used whole-genome approaches to sequence four Vibrio cholerae isolates from Haiti and the Dominican Republic and three additional V. cholerae isolates to a high depth of coverage (>2000x); four of the seven isolates were previously sequenced. Using these sequence data, we examined the effect of depth of coverage and sequencing platform on genome assembly and identification of sequence variants. We found that 50x coverage is sufficient to construct a whole-genome assembly and to accurately call most variants from 100 base pair paired-end sequencing reads. Phylogenetic analysis between the newly sequenced and thirty-three previously sequenced V. cholerae isolates indicates that the Haitian and Dominican Republic isolates are closest to strains from South Asia. The Haitian and Dominican Republic isolates form a tight cluster, with only four variants unique to individual isolates. These variants are located in the CTX region, the SXT region, and the core genome. Of the 126 mutations identified that separate the Haiti-Dominican Republic cluster from the V. cholerae reference strain (N16961), 73 are non-synonymous changes, and a number of these changes cluster in specific genes and pathways. Sequence variant analyses of V. cholerae isolates, including multiple isolates from the Haitian outbreak, identify coverage-specific and technology-specific effects on variant detection, and provide insight into genomic change and functional evolution during an epidemic.
Lowe, Kellis, Siepel, Raney, Clamp, Salama, Kingsley, Lindblad-Toh, Haussler
The gain, loss, and modification of gene regulatory elements may underlie a substantial proportion of phenotypic changes on animal lineages. To investigate the gain of regulatory elements throughout vertebrate evolution, we identified genome-wide sets of putative regulatory regions for five vertebrates, including humans. These putative regulatory regions are conserved nonexonic elements (CNEEs), which are evolutionarily conserved yet do not overlap any coding or noncoding mature transcript. We then inferred the branch on which each CNEE came under selective constraint. Our analysis identified three extended periods in the evolution of gene regulatory elements. Early vertebrate evolution was characterized by regulatory gains near transcription factors and developmental genes, but this trend was replaced by innovations near extracellular signaling genes, and then innovations near posttranslational protein modifiers.
Rhind, Chen, Yassour, Thompson, Haas, Habib, Wapinski, Roy, Lin, Heiman, Young, Furuya, Guo, Pidoux, Chen, Robbertse, Goldberg, Aoki, Bayne, Berlin, Desjardins, Dobbs, Dukaj, Fan, Fitzgerald, French, Gujja, Hansen, Keifenheim, Levin, Mosher, Müller, Pfiffner, Priest, Russ, Smialowska, Swoboda, Sykes, Vaughn, Vengrova, Yoder, Zeng, Allshire, Baulcombe, Birren, Brown, Ekwall, Kellis, Leatherwood, Levin, Margalit, Martienssen, Nieduszynski, Spatafora, Friedman, Dalgaard, Baumann, Niki, Regev, Nusbaum
The fission yeast clade-comprising Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus-occupies the basal branch of Ascomycete fungi and is an important model of eukaryote biology. A comparative annotation of these genomes identified a near extinction of transposons and the associated innovation of transposon-free centromeres. Expression analysis established that meiotic genes are subject to antisense transcription during vegetative growth, which suggests a mechanism for their tight regulation. In addition, trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response. Differences in gene content and regulation also explain why, unlike the Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source. These analyses elucidate the genome structure and gene regulation of fission yeast and provide tools for investigation across the Schizosaccharomyces clade.
Kellis, Birren, Lander
Whole-genome duplication followed by massive gene loss and specialization has long been postulated as a powerful mechanism of evolutionary innovation. Recently, it has become possible to test this notion by searching complete genome sequence for signs of ancient duplication. Here, we show that the yeast Saccharomyces cerevisiae arose from ancient whole-genome duplication, by sequencing and analysing Kluyveromyces waltii, a related yeast species that diverged before the duplication. The two genomes are related by a 1:2 mapping, with each region of K. waltii corresponding to two regions of S. cerevisiae, as expected for whole-genome duplication. This resolves the long-standing controversy on the ancestry of the yeast genome, and makes it possible to study the fate of duplicated genes directly. Strikingly, 95% of cases of accelerated evolution involve only one member of a gene pair, providing strong support for a specific model of evolution, and allowing us to distinguish ancestral and derived functions.
Butler, Rasmussen, Lin, Santos, Sakthikumar, Munro, Rheinbay, Grabherr, Forche, Reedy, Agrafioti, Arnaud, Bates, Brown, Brunke, Costanzo, Fitzpatrick, de, Harris, Hoyer, Hube, Klis, Kodira, Lennard, Logue, Martin, Neiman, Nikolaou, Quail, Quinn, Santos, Schmitzberger, Sherlock, Shah, Silverstein, Skrzypek, Soll, Staggs, Stansfield, Stumpf, Sudbery, Srikantha, Zeng, Berman, Berriman, Heitman, Gow, Lorenz, Birren, Kellis, Cuomo
Candida species are the most common cause of opportunistic fungal infection worldwide. Here we report the genome sequences of six Candida species and compare these and related pathogens and non-pathogens. There are significant expansions of cell wall, secreted and transporter gene families in pathogenic species, suggesting adaptations associated with virulence. Large genomic tracts are homozygous in three diploid species, possibly resulting from recent recombination events. Surprisingly, key components of the mating and meiosis pathways are missing from several species. These include major differences at the mating-type loci (MTL); Lodderomyces elongisporus lacks MTL, and components of the a1/2 cell identity determinant were lost in other species, raising questions about how mating and cell types are controlled. Analysis of the CUG leucine-to-serine genetic-code change reveals that 99% of ancestral CUG codons were erased and new ones arose elsewhere. Lastly, we revise the Candida albicans gene catalogue, identifying many new genes.
Jaillon, Aury, Brunet, Petit, Stange-Thomann, Mauceli, Bouneau, Fischer, Ozouf-Costaz, Bernot, Nicaud, Jaffe, Fisher, Lutfalla, Dossat, Segurens, Dasilva, Salanoubat, Levy, Boudet, Castellano, Anthouard, Jubin, Castelli, Katinka, Vacherie, Biémont, Skalli, Cattolico, Poulain, De, Cruaud, Duprat, Brottier, Coutanceau, Gouzy, Parra, Lardier, Chapple, McKernan, McEwan, Bosak, Kellis, Volff, Guigó, Zody, Mesirov, Lindblad-Toh, Birren, Nusbaum, Kahn, Robinson-Rechavi, Laudet, Schachter, Quétier, Saurin, Scarpelli, Wincker, Lander, Weissenbach, Roest
Tetraodon nigroviridis is a freshwater puffer fish with the smallest known vertebrate genome. Here, we report a draft genome sequence with long-range linkage and substantial anchoring to the 21 Tetraodon chromosomes. Genome analysis provides a greatly improved fish gene catalogue, including identifying key genes previously thought to be absent in fish. Comparison with other vertebrates and a urochordate indicates that fish proteins have diverged markedly faster than their mammalian homologues. Comparison with the human genome suggests approximately 900 previously unannotated human genes. Analysis of the Tetraodon and human genomes shows that whole-genome duplication occurred in the teleost fish lineage, subsequent to its divergence from mammals. The analysis also makes it possible to infer the basic structure of the ancestral bony vertebrate genome, which was composed of 12 chromosomes, and to reconstruct much of the evolutionary history of ancient and recent chromosome rearrangements leading to the modern human karyotype.
Drosophila, Clark, Eisen, Smith, Bergman, Oliver, Markow, Kaufman, Kellis, Gelbart, Iyer, Pollard, Sackton, Larracuente, Singh, Abad, Abt, Adryan, Aguade, Akashi, Anderson, Aquadro, Ardell, Arguello, Artieri, Barbash, Barker, Barsanti, Batterham, Batzoglou, Begun, Bhutkar, Blanco, Bosak, Bradley, Brand, Brent, Brooks, Brown, Butlin, Caggese, Calvi, Bernardo, Caspi, Castrezana, Celniker, Chang, Chapple, Chatterji, Chinwalla, Civetta, Clifton, Comeron, Costello, Coyne, Daub, David, Delcher, Delehaunty, Do, Ebling, Edwards, Eickbush, Evans, Filipski, Findeiss, Freyhult, Fulton, Fulton, Garcia, Gardiner, Garfield, Garvin, Gibson, Gilbert, Gnerre, Godfrey, Good, Gotea, Gravely, Greenberg, Griffiths-Jones, Gross, Guigo, Gustafson, Haerty, Hahn, Halligan, Halpern, Halter, Han, Heger, Hillier, Hinrichs, Holmes, Hoskins, Hubisz, Hultmark, Huntley, Jaffe, Jagadeeshan, Jeck, Johnson, Jones, Jordan, Karpen, Kataoka, Keightley, Kheradpour, Kirkness, Koerich, Kristiansen, Kudrna, Kulathinal, Kumar, Kwok, Lander, Langley, Lapoint, Lazzaro, Lee, Levesque, Li, Lin, Lin, Lindblad-Toh, Llopart, Long, Low, Lozovsky, Lu, Luo, Machado, Makalowski, Marzo, Matsuda, Matzkin, McAllister, McBride, McKernan, McKernan, Mendez-Lago, Minx, Mollenhauer, Montooth, Mount, Mu, Myers, Negre, Newfeld, Nielsen, Noor, O'Grady, Pachter, Papaceit, Parisi, Parisi, Parts, Pedersen, Pesole, Phillippy, Ponting, Pop, Porcelli, Powell, Prohaska, Pruitt, Puig, Quesneville, Ram, Rand, Rasmussen, Reed, Reenan, Reily, Remington, Rieger, Ritchie, Robin, Rogers, Rohde, Rozas, Rubenfield, Ruiz, Russo, Salzberg, Sanchez-Gracia, Saranga, Sato, Schaeffer, Schatz, Schlenke, Schwartz, Segarra, Singh, Sirot, Sirota, Sisneros, Smith, Smith, Spieth, Stage, Stark, Stephan, Strausberg, Strempel, Sturgill, Sutton, Sutton, Tao, Teichmann, Tobari, Tomimura, Tsolas, Valente, Venter, Venter, Vicario, Vieira, Vilella, Villasante, Walenz, Wang, Wasserman, Watts, Wilson, Wilson, Wing, Wolfner, Wong, Wong, Wu, Wu, Yamamoto, Yang, Yang, Yorke, Yoshida, Zdobnov, Zhang, Zhang, Zimin, Baldwin, Abdouelleil, Abdulkadir, Abebe, Abera, Abreu, Acer, Aftuck, Alexander, An, Anderson, Anderson, Arachi, Azer, Bachantsang, Barry, Bayul, Berlin, Bessette, Bloom, Blye, Boguslavskiy, Bonnet, Boukhgalter, Bourzgui, Brown, Cahill, Channer, Cheshatsang, Chuda, Citroen, Collymore, Cooke, Costello, D'Aco, Daza, De, DeGray, DeMaso, Dhargay, Dooley, Dooley, Doricent, Dorje, Dorjee, Dupes, Elong, Falk, Farina, Faro, Ferguson, Fisher, Foley, Franke, Friedrich, Gadbois, Gearin, Gearin, Giannoukos, Goode, Graham, Grandbois, Grewal, Gyaltsen, Hafez, Hagos, Hall, Henson, Hollinger, Honan, Huard, Hughes, Hurhula, Husby, Kamat, Kanga, Kashin, Khazanovich, Kisner, Lance, Lara, Lee, Lennon, Letendre, LeVine, Lipovsky, Liu, Liu, Liu, Lokyitsang, Lokyitsang, Lubonja, Lui, MacDonald, Magnisalis, Maru, Matthews, McCusker, McDonough, Mehta, Meldrim, Meneus, Mihai, Mihalev, Mihova, Mittelman, Mlenga, Montmayeur, Mulrain, Navidi, Naylor, Negash, Nguyen, Nguyen, Nicol, Norbu, Norbu, Novod, O'Neill, Osman, Markiewicz, Oyono, Patti, Phunkhang, Pierre, Priest, Raghuraman, Rege, Reyes, Rise, Rogov, Ross, Ryan, Settipalli, Shea, Sherpa, Shi, Shih, Sparrow, Spaulding, Stalker, Stange-Thomann, Stavropoulos, Stone, Strader, Tesfaye, Thomson, Thoulutsang, Thoulutsang, Topham, Topping, Tsamla, Vassiliev, Vo, Wangchuk, Wangdi, Weiand, Wilkinson, Wilson, Yadav, Young, Yu, Zembek, Zhong, Zimmer, Zwirko, Jaffe, Alvarez, Brockman, Butler, Chin, Gnerre, Grabherr, Kleber, Mauceli, MacCallum
Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.
Warren, Hillier, Marshall, Birney, Ponting, Grützner, Belov, Miller, Clarke, Chinwalla, Yang, Heger, Locke, Miethke, Waters, Veyrunes, Fulton, Fulton, Graves, Wallis, Puente, López-Otín, Ordóńez, Eichler, Chen, Cheng, Deakin, Alsop, Thompson, Kirby, Papenfuss, Wakefield, Olender, Lancet, Huttley, Smit, Pask, Temple-Smith, Batzer, Walker, Konkel, Harris, Whittington, Wong, Gemmell, Buschiazzo, Vargas, Merkel, Schmitz, Zemann, Churakov, Kriegs, Brosius, Murchison, Sachidanandam, Smith, Hannon, Tsend-Ayush, McMillan, Attenborough, Rens, Ferguson-Smith, Lefčvre, Sharp, Nicholas, Ray, Kube, Reinhardt, Pringle, Taylor, Jones, Nixon, Dacheux, Niwa, Sekita, Huang, Stark, Kheradpour, Kellis, Flicek, Chen, Webber, Hardison, Nelson, Hallsworth-Pepin, Delehaunty, Markovic, Minx, Feng, Kremitzki, Mitreva, Glasscock, Wylie, Wohldmann, Thiru, Nhan, Pohl, Smith, Hou, Nefedov, de, Renfree, Mardis, Wilson
We present a draft genome sequence of the platypus, Ornithorhynchus anatinus. This monotreme exhibits a fascinating combination of reptilian and mammalian characters. For example, platypuses have a coat of fur adapted to an aquatic lifestyle; platypus females lactate, yet lay eggs; and males are equipped with venom similar to that of reptiles. Analysis of the first monotreme genome aligned these features with genetic innovations. We find that reptile and platypus venom proteins have been co-opted independently from the same gene families; milk protein genes are conserved despite platypuses laying eggs; and immune gene family expansions are directly related to platypus biology. Expansions of protein, non-protein-coding RNA and microRNA families, as well as repeat elements, are identified. Sequencing of this genome now provides a valuable resource for deep mammalian comparative analyses, as well as for monotreme biology and conservation.
We have used the availability of many closely-related genomes to go beyond traditional phylogenetic methods and develop new 'phylogenomic' methods for resolving gene phylogenies across complete genomes jointly, enabling much more accurate inference methods, by recognizing two distinct evolutionary forces, one acting at the gene level, and one at the genome level. We applied these methods to Candida genomes, including both strong and weak human pathogens, and revealed new candidate virulence genes that are rapidly evolving and expanding in pathogens, thus expanding potential drug target genes.
Bansal, Wu, Alm, Kellis
The accurate inference of gene trees is a necessary step in many evolutionary studies. While the problem of accurate gene tree inference has received considerable attention, most existing methods are only applicable to gene families unaffected by horizontal gene transfer. As a result, the accurate inference of gene trees affected by horizontal gene transfer remains a largely unaddressed problem. In this work, we introduce a new and highly effective method for gene tree error-correction in the presence of horizontal gene transfer. Our method efficiently models horizontal gene transfers, gene duplications, and losses, and uses a statistical hypothesis testing framework (Shimodaira-Hasegawa test) to balance sequence likelihood with topological information from a known species tree. Using a thorough simulation study, we show that existing phylogenetic methods yield inaccurate gene trees when applied to horizontally transferred gene families and that our method dramatically improves gene tree accuracy. We apply our method to a dataset of 11 cyanobacterial species and demonstrate the large impact of gene tree accuracy on downstream evolutionary analyses. An implementation of our method is available at http://compbio.mit.edu/treefix-dtl/
Libeskind-Hadas, Wu, Bansal, Kellis
Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem. We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies. Availability and implementation: Our Python tools are freely available at www.cs.hmc.edu/~hadas/xscape.
Wu, Rasmussen, Bansal, Kellis
Accurate gene tree-species tree reconciliation is fundamental to inferring the evolutionary history of a gene family. However, although it has long been appreciated that population-related effects such as incomplete lineage sorting (ILS) can dramatically affect the gene tree, many of the most popular reconciliation methods consider discordance only due to gene duplication and loss (and sometimes horizontal gene transfer). Methods that do model ILS are either highly parameterized or consider a restricted set of histories, thus limiting their applicability and accuracy. To address these challenges, we present a novel algorithm DLCpar for inferring a most parsimonious (MP) history of a gene family in the presence of duplications, losses, and ILS. Our algorithm relies on a new reconciliation structure, the labeled coalescent tree (LCT), that simultaneously describes coalescent and duplication-loss history. We show that the LCT representation enables an exhaustive and efficient search over the space of reconciliations, and, for most gene families, the least common ancestor (LCA) mapping is an optimal solution for the species mapping between the gene tree and species tree in a MP LCT. Applying our algorithm to a variety of clades, including flies, fungi, and primates, as well as to simulated phylogenies, we achieve high accuracy, comparable to sophisticated probabilistic reconciliation methods, at reduced runtime and with far fewer parameters. These properties enable inference of complex evolution of gene families across a broad range of species and large data sets.
Bansal, Alm, Kellis
Phylogenetic tree reconciliation is a powerful approach for inferring evolutionary events like gene duplication, horizontal gene transfer, and gene loss, which are fundamental to our understanding of molecular evolution. While duplication-loss (DL) reconciliation leads to a unique maximum-parsimony solution, duplication-transfer-loss (DTL) reconciliation yields a multitude of optimal solutions, making it difficult to infer the true evolutionary history of the gene family. This problem is further exacerbated by the fact that different event cost assignments yield different sets of optimal reconciliations. Here, we present an effective, efficient, and scalable method for dealing with these fundamental problems in DTL reconciliation. Our approach works by sampling the space of optimal reconciliations uniformly at random and aggregating the results. We show that even gene trees with only a few dozen genes often have millions of optimal reconciliations and present an algorithm to efficiently sample the space of optimal reconciliations uniformly at random in O(mn(2)) time per sample, where m and n denote the number of genes and species, respectively. We use these samples to understand how different optimal reconciliations vary in their node mappings and event assignments and to investigate the impact of varying event costs. We apply our method to a biological dataset of approximately 4700 gene trees from 100 taxa and observe that 93% of event assignments and 73% of mappings remain consistent across different multiple optima. Our analysis represents the first systematic investigation of the space of optimal DTL reconciliations and has many important implications for the study of gene family evolution.
Thompson, Roy, Chan, Styczynsky, Pfiffner, French, Socha, Thielke, Napolitano, Muller, Kellis, Konieczka, Wapinski, Regev.
Divergence in gene regulation can play a major role in evolution. Here, we used a phylogenetic framework to measure mRNA profiles in 15 yeast species from the phylum Ascomycota and reconstruct the evolution of their modular regulatory programs along a time course of growth on glucose over 1 billion years. We found that modules have diverged proportionally to phylogenetic distance, with prominent changes in gene regulation accompanying changes in lifestyle and ploidy, especially in carbon metabolism. Paralogs have significantly contributed to regulatory divergence, typically within a very short window from their duplication. Paralogs from a whole genome duplication (WGD) event have a uniquely substantial contribution that extends over a longer span. Similar patterns occur when considering the evolution of the heat shock regulatory program measured in eight of the species, suggesting that these are general evolutionary principles
Roy, Wapinski, Pfiffner, French, Socha, Konieczka, Habib, Kellis, Thompson, Regev
Comparative functional genomics studies the evolution of biological processes by analyzing functional data, such as gene expression profiles, across species. A major challenge is to compare profiles collected in a complex phylogeny. Here, we present Arboretum, a novel scalable computational algorithm that integrates expression data from multiple species with species and gene phylogenies to infer modules of coexpressed genes in extant species and their evolutionary histories. We also develop new, generally applicable measures of conservation and divergence in gene regulatory modules to assess the impact of changes in gene content and expression on module evolution. We used Arboretum to study the evolution of the transcriptional response to heat shock in eight species of Ascomycota fungi and to reconstruct modules of the ancestral environmental stress response (ESR). We found substantial conservation in the stress response across species and in the reconstructed components of the ancestral ESR modules. The greatest divergence was in the most induced stress, primarily through module expansion. The divergence of the heat stress response exceeds that observed in the response to glucose depletion in the same species. Arboretum and its associated analyses provide a comprehensive framework to systematically study regulatory evolution of condition-specific responses.
Wu, Rasmussen, Bansal, Kellis.
Accurate gene tree reconstruction is a fundamental problem in phylogenetics, with many important applications. However, sequence data alone often lack enough information to confidently support one gene tree topology over many competing alternatives. Here, we present a novel framework for combining sequence data and species tree information, and we describe an implementation of this framework in TreeFix, a new phylogenetic program for improving gene tree reconstructions. Given a gene tree (preferably computed using a maximum likelihood phylogenetic program), TreeFix finds a "statistically equivalent" gene tree that minimizes a species tree based cost function. We have applied TreeFix to two clades of 12 Drosophila and 16 fungal genomes, as well as to simulated phylogenies, and show that it dramatically improves reconstructions compared to current state-of-the-art programs. Given its accuracy, speed, and simplicity, TreeFix should be applicable to a wide range of analyses and have many important implications for future investigations of gene evolution. The source code and a sample dataset are available at http://compbio.mit.edu/treefix
Gene family evolution is driven by evolutionary events such as speciation, gene duplication, horizontal gene transfer and gene loss, and inferring these events in the evolutionary history of a given gene family is a fundamental problem in comparative and evolutionary genomics with numerous important applications. Solving this problem requires the use of a reconciliation framework, where the input consists of a gene family phylogeny and the corresponding species phylogeny, and the goal is to reconcile the two by postulating speciation, gene duplication, horizontal gene transfer and gene loss events. This reconciliation problem is referred to as duplication-transfer-loss (DTL) reconciliation and has been extensively studied in the literature. Yet, even the fastest existing algorithms for DTL reconciliation are too slow for reconciling large gene families and for use in more sophisticated applications such as gene tree or species tree reconstruction. Here, we present two new algorithms for the DTL reconciliation problem that are dramatically faster than existing algorithms, both asymptotically and in practice. We also extend the standard DTL reconciliation model by considering distance-dependent transfer costs, which allow for more accurate reconciliation and give an efficient algorithm for DTL reconciliation under this extended model. We implemented our new algorithms and demonstrated up to 100 000-fold speed-up over existing methods, using both simulated and biological datasets. This dramatic improvement makes it possible to use DTL reconciliation for performing rigorous evolutionary analyses of large gene families and enables its use in advanced reconciliation-based gene and species tree reconstruction methods. Our programs can be freely downloaded from http://compbio.mit.edu/ranger-dtl/.
Rasmussen, Kellis
Gene phylogenies provide a rich source of information about the way evolution shapes genomes, populations, and phenotypes. In addition to substitutions, evolutionary events such as gene duplication and loss (as well as horizontal transfer) play a major role in gene evolution, and many phylogenetic models have been developed in order to reconstruct and study these events. However, these models typically make the simplifying assumption that population-related effects such as incomplete lineage sorting (ILS) are negligible. While this assumption may have been reasonable in some settings, it has become increasingly problematic as increased genome sequencing has led to denser phylogenies, where effects such as ILS are more prominent. To address this challenge, we present a new probabilistic model, DLCoal, that defines gene duplication and loss in a population setting, such that coalescence and ILS can be directly addressed. Interestingly, this model implies that in addition to the usual gene tree and species tree there exists a third tree, the locus tree, which will likely have many applications. Using this model, we develop the first general reconciliation method that accurately infers gene duplications and losses in the presence of ILS, and we show its improved inference of orthologs, paralogs, duplications, and losses for a variety of clades, including flies, fungi, and primates. Also, our simulations show that gene duplications increase the frequency of ILS, further illustrating the importance of a joint model. Going forward, we believe this unified model can offer insights to questions in both phylogenetics and population genetics.
Wu, Rasmussen, Kellis
Although the possibility of gene evolution by domain rearrangements has long been appreciated, current methods for reconstructing and systematically analyzing gene family evolution are limited to events such as duplication, loss, and sometimes, horizontal transfer. However, within the Drosophila clade, we find domain rearrangements occur in 35.9% of gene families, and thus, any comprehensive study of gene evolution in these species will need to account for such events. Here, we present a new computational model and algorithm for reconstructing gene evolution at the domain level. We develop a method for detecting homologous domains between genes, and present a phylogenetic algorithm for reconstructing maximum parsimony evolutionary histories that include domain generation, duplication, loss, merge (fusion), and split (fission) events. Using this method, we find that genes involved in fusion and fission are enriched in signaling and development, suggesting that domain rearrangements and reuse may be crucial in these processes. We also find that fusion is more abundant than fission and that fusion and fission events occur predominantly alongside duplication, with 92.5% and 34.3% of fusion and fission events retaining ancestral architectures in the duplicated copies. We provide a catalog of ~9000 genes that undergo domain rearrangement across nine sequenced species, along with possible mechanisms for their formation. These results dramatically expand on evolution at the sub-gene level and offer several insights into how new genes and functions arise between species.
Recent sequencing and computing advances have enabled phylogenetic analyses to expand to both entire genomes and large clades, thus requiring more efficient and accurate methods designed specifically for the phylogenomic context. Here we present SPIMAP, an efficient Bayesian method for reconstructing gene trees in the presence of a known species tree. We observe many improvements in reconstruction accuracy, achieved by modeling multiple aspects of evolution, including gene duplication and loss rates, speciation times, and correlated substitution rate variation across both species and loci. We have implemented and applied this method on two clades of fully-sequenced species, 12 Drosophila and 16 fungal genomes as well as simulated phylogenies, and find dramatic improvements in reconstruction accuracy as compared to the most popular existing methods, including those that take the species tree into account. We find that reconstruction inaccuracies of traditional phylogenetic methods overestimate the number of duplication and loss events by as much as 2 to 3 fold, while our method achieves significantly higher accuracy. We feel the results and methods presented here will have many important implications for future investigations of gene evolution.
Comparative genomics provides a general methodology for discovering functional DNA elements and understanding their evolution. The availability of many related genomes enables more powerful analyses, but requires rigorous phylogenetic methods to resolve orthologous genes and regions. Here, we use 12 recently sequenced Drosophila genomes and nine fungal genomes to address the problem of accurate gene-tree reconstruction across many complete genomes. We show that existing phylogenetic methods that treat each gene tree in isolation show large-scale inaccuracies, largely due to insufficient phylogenetic information in individual genes. However, we find that gene trees exhibit common properties that can be exploited for evolutionary studies and accurate phylogenetic reconstruction. Evolutionary rates can be decoupled into gene-specific and species-specific components, which can be learned across complete genomes. We develop a phylogenetic reconstruction methodology that exploits these properties and achieves significantly higher accuracy, addressing the species-level heterotachy and enabling studies of gene evolution in the context of species evolution.
Organ, Rasmussen, Baldwin, Kellis, Edwards
Gene duplication is a fundamental aspect of genome evolution that produces large and small gene families of (usually) related function. We perform a phylogenomic analysis of gene duplication in the chicken (Gallus gallus) to characterize the dynamics and evolution of gene duplication on the evolutionary line to birds. In Gallus, the distribution of the number of paralogs per gene family is heavily skewed towards small families. This finding is in accord with other studies that find gene family size typically follows a power-law distribution in animals, a pattern thought to be produced by differential rates of pseudogenization among families. We also test for within-family evolutionary rate variation in Gallus, finding that the vast majority of gene families exhibit substantial rate variation among lineages. This rate variation probably stems from two sources: natural deviations in the clock as commonly found, for example, in phylogenetic analyses of different species; and bursts of adaptive evolution among newly evolved gene family members. The age of gene duplications in Gallus are distributed exponentially, with most duplications occurring recently, a pattern consistent with analyses on other eukaryotes. Taken together, these results begin to reveal the dynamics of gene family evolution in birds, the most speciose group of living amniotes, though whole genome data are required from more bird and reptile species to fully understand patterns of gene gain and loss in this group.
While most of our papers have been in biological journals and in
collaboration with experimental investigators, we have also written a
small number of computational papers, for computational biology
conferences or computational journals. These have frequently
introduced new algorithms that we have independently or in parallel
applied to biological datasets, and other times provided more details
on the methods underlying some of our genomics results. These papers
will be of particular interest to computational scientists seeking to
gain a deeper understanding of the computational challenges associated
with the genomics problems we are working on. The algorithms presented
in these papers include methods for ortholog and paralog
determination, gene identification from their evolutionary signatures,
motif discovery and clustering, network motif discovery using symmetry
breaking conditions, and network inference methods.
Hubisz, Lin, Kellis, Siepel
The recent release of twenty-two new genome sequences has dramatically increased the data available for mammalian comparative genomics, but twenty of these new sequences are currently limited to ~2X coverage. Here we examine the extent of sequencing error in these 2X assemblies, and its potential impact in downstream analyses. By comparing 2X assemblies with high-quality sequences from the ENCODE regions, we estimate the rate of sequencing error to be 1-4 errors per kilobase. While this error rate is fairly modest, sequencing error can still have surprising effects. For example, an apparent lineage-specific insertion in a coding region is more likely to reflect sequencing error than a true biological event, and the length distribution of coding indels is strongly distorted by error. We find that most errors are contributed by a small fraction of bases with low quality scores, in particular, by the ends of reads in regions of single-read coverage in the assembly. We explore several approaches for automatic sequencing error mitigation (SEM), making use of the localized nature of sequencing error, the fact that it is well predicted by quality scores, and information about errors that comes from comparisons across species. Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses. Our error-mitigated alignments are available for download
Kellis, Patterson, Birren, Berger, Lander
In Kellis et al 2003 we reported the genome sequences of S. paradoxus, S. mikatae and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genome-wide comparative analysis allowed the identification of functionally important sequences, both coding and non-coding. In this companion paper we describe the mathematical and algorithmic results underpinning the analysis of these genomes. We developed methods for the automatic comparative annotation of the four species and the determination of orthologous genes and intergenic regions. The algorithms enabled the automatic identification of orthologs for more than 90% of genes despite the large number of duplicated genes in the yeast genome, and the discovery of recent gene family expansions and genome rearrangements. We also developed a test to validate computationally predicted protein-coding genes based on their patterns of nucleotide conservation. The method has high specificity and sensitivity, and enabled us to revisit the current annotation of S.cerevisiae with important biological implications. We developed statistical methods for the systematic de-novo identification of regulatory motifs. Without making use of coregulated gene sets, we discovered virtually all previously known DNA regulatory motifs as well as several noteworthy novel motifs. With the additional use of gene ontology information, expression clusters and transcription factor binding profiles, we assigned candidate functions to the novel motifs discovered. Our results demonstrate that entirely automatic genome-wide annotation, gene validation, and discovery of regulatory motifs is possible. Our findings are validated by the extensive experimental knowledge in yeast, confirming their applicability to other genomes
Transcriptional regulation in eukaryotes often involves multiple transcription factors binding to the same transcription control region, and to understand the regulatory content of eukaryotic genomes it is necessary to consider the co-occurrence and spatial relationships of individual binding sites. The determination of conserved sequences (often known as phylogenetic footprinting) has identified individual transcription factor binding sites. We extend this concept of functional conservation to higher-order features of transcription control regions. We used the genome sequences of four yeast species of the genus Saccharomyces to identify sequences potentially involved in multifactorial control of gene expression. We found 989 potential regulatory 'templates': pairs of hexameric sequences that are jointly conserved in transcription regulatory regions and also exhibit non-random relative spacing. Many of the individual sequences in these templates correspond to known transcription factor binding sites, and the sets of genes containing a particular template in their transcription control regions tend to be differentially expressed in conditions where the corresponding transcription factors are known to be active. The incorporation of word pairs to define sequence features yields more specific predictions of average expression profiles and more informative regression models for genome-wide expression data than considering sequence conservation alone. CONCLUSIONS: The incorporation of both joint conservation and spacing constraints of sequence pairs predicts groups of target genes that are specific for common patterns of gene expression. Our work suggests that positional information, especially the relative spacing between transcription factor binding sites, may represent a common organizing principle of transcription control regions.
In Kellis et al. (2003), we reported the genome sequences of S. paradoxus, S. mikatae, and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genomewide comparative analysis allowed the identification of functionally important sequences, both coding and noncoding. In this companion paper we describe the mathematical and algorithmic results underpinning the analysis of these genomes. (1) We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change. (2) We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. (3) We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on genomewide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs. Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast and will be invaluable in the study of complex genomes like that of the human.
Understanding the biological signals encoded in a genome is a key challenge of computational biology. These signals are encoded in the four-nucleotide alphabet of DNA and are responsible for all molecular processes in the cell. In particular, the genome contains the blueprint of all protein-coding genes and the regulatory motifs used to coordinate the expression of these genes. Comparative genome analysis of related species provides a general approach for identifying these functional elements, by virtue of their stronger conservation across evolutionary time. In this thesis we address key issues in the comparative analysis of multiple species. We present novel computational methods in four areas (1) the automatic comparative annotation of multiple species and the determination of orthologous genes and intergenic regions (2) the validation of computationally predicted protein-coding genes (3) the systematic de-novo identification of regulatory motifs (4) the determination of combinatorial interactions between regulatory motifs. We applied these methods to the comparative analysis of four yeast genomes, including the best-studied eukaryote, Saccharomyces cerevisiae or baker's yeast. Our results show that nearly a tenth of currently annotated yeast genes are not real, and have refined the structure of hundreds of genes. Additionally, we have automatically discovered a dictionary of regulatory motifs without any previous biological knowledge. These include most previously known regulatory motifs, and a number of novel motifs. We have automatically assigned candidate functions to the majority of motifs discovered, and defined biologically meaningful combinatorial interactions between them. Finally, we defined the regions and mechanisms of rapid evolution, with important biological implications. Our results demonstrate the central role of computational tools in modern biology. The analyses presented in this thesis have revealed biological findings that could not have been discovered by traditional genetic methods, regardless of the time or effort spent. The methods presented are general and may present a new paradigm for understanding the genome of any single species. They are currently being applied to a kingdom-wide exploration of fungal genomes, and the comparative analysis of the human genome with that of the mouse and other mammals.
Meyer, Marbach, Roy, Kellis
Unraveling transcriptional regulatory networks is essential for understanding and predicting cellular responses in different developmental and environmental contexts. Information-theoretic methods of network inference have been shown to produce high-quality reconstructions because of their ability to infer both linear and non-linear dependencies between regulators and targets. In this paper, we introduce MRNETB an improved version of the previous information-theoretic algorithm, MRNET, which has competitive performance with state-of-the-art algorithms. MRNET infers a network by using a forward selection strategy to identify a maximally-independent set of neighbors for every variable. However, a known limitation of algorithms based on forward selection is that the quality of the selected subset strongly depends on the first variable selected. In this paper, we present MRNETB, an improved version of MRNET that overcomes this limitation by using a backward selection strategy followed by a sequential replacement. Our new variable selection procedure can be implemented with the same computational cost as the forward selection strategy. MRNETB was benchmarked against MRNET and two other information-theoretic algorithms, CLR and ARACNE. Our benchmark comprised 15 datasets generated from two regulatory network simulators, 10 of which are from the DREAM4 challenge, which was recently used to compare over 30 network inference methods. To assess stability of our results, each method was implemented with two estimators of mutual information. Our results show that MRNETB has significantly better performance than MRNET, irrespective of the mutual information estimation method. MRNETB also performs comparably to CLR and significantly better than ARACNE indicating that our new variable selection strategy can successfully infer high-quality networks.
Prior to computational biology, Prof. Kellis worked in several
areas of computer science, and in particular computational geometry,
graphics, and vision. He co-developed an algorithm for surface
reconstruction (which has received 664 citations), methods for polygon
and curve morphing, shape-recognition methods for sketch-based image
retrieval, and efficient representations for online handwritten
character recognition.
Amenta, Bern, Kellis (as Kamvysselis)
We describe our experience with a new algorithm for the reconstruction of surfaces from unorganized sample points in 3D. The algorithm is the first for this problem with provable guarantees. Given a "good sample" from a smooth surface, the output is guaranteed to be topologically correct and convergent to the original surface as the sampling density increases. The definition of a good sample is itself interesting: the required sampling density varies locally, rigorously capturing the intuitive notion that featureless areas can be reconstructed from fewer samples. The output mesh interpolates, rather than approximates, the input points. Our algorithm is based on the three-dimensional Voronoi diagram. Given a good program for this fundamental subroutine, the algorithm is quite easy to implement.
Kellis (as Kamvysselis)
Morphing techniques that have been developed to progressively transform one two-dimensional image to another are usually pixel based and morph a source to a target by interpolating pixel values based on constraints specified by the user. In this work, we are using a model-based approach to propose a solution to morphing a two-dimensional polygon into another. Our model of an object will be its Extended Circular Image representation, the two-dimensional equivalent of the Extended Gaussian Image. This approach will work only for convex polygons, for which the ECI is unique. To morph non-convex polygons, one would have to separate them into convex components and morph each separately, or use an extended version of the ECI that would apply to non-convex polygons. This morphing method can be applied to convex polyhedra using the Extended Gaussian Image, and similarly be extended to non-convex polyhedra.
Kellis (as Kamvysselis), Gajos, Blum, Vassef
Morphing is an interpolation technique used to create from two objects a series of intermediate objects that change continuously to make a smooth transition from the source to the target. Morphing has been done in two dimensions by varying the values of the pixels of one image to make a different image, or in three dimensions by varying the values of three-dimensional pixels. We're presenting here a new type of morphing, which transforms the geometry of three dimensional models, creating intermediate objects which are all clearly defined three-dimensional objects, which can be translated, rotated, scaled, zoomed-into.
Kellis (as Kamvysselis), Marina
As digital media become more popular, corporations and individuals gather an increasingly large number of digital images. As a collection grows to more than a few hundred images, the need for search becomes crucial. This thesis is addressing the problem of retrieving from a small database a particular image previously seen by the user. This thesis combines current ndings in cognitive science with the knowledge of previous image retrieval systems to present a novel approach to content based image retrieval and indexing. We focus on algorithms which abstract away information from images in the same terms that a viewer abstracts information from an image. The focus in Imagina is on the matching of regions, instead of the matching of global measures. Multiple representations, focusing on shape and color, are used for every region. The matches of individual regions are combined using a saliency metric that accounts for dierences in the distributions of metrics. Region matching along with conguration determines the overall match between a query and an image.
This paper presents our experience with an curvature space for online character recognition. The (x,y) coordinate data along the character curve is transformed to local curvature data. The peaks of this curvature represent the sharp turns made by the pen in constructing the character. The height and timestamp of these peaks are used as features. The number of peaks for a single character rarely exceeds five or six. Thus, we have transformed the 256 dimensional data of off-line recognition on a 16x16 grid, to less than 10 dimensional data. This aggressive reduction in dimensionality is certainly lossy. However this paper makes the claim that the curvature peaks preserve the information most important to character structure. To demonstrate the claim, we construct a classifier for handwritten digits that bases its decisions solely on the few peaks in curvature space. The results are promising: 200 digits of each character were correctly classified based on a HMM mixture model constructed from 300 examples of each digit. Unsupervised learning techniques are also envisioned. The character data plotted in the lower dimensional space suggests distinct clusters emerge, that could separate and group trained symbols, as well as recognize new symbols as categories.
Other areas of interest included robotics and artificial
intelligence, resulting in projects on robot programming, developing an interactive robot programming framework for kids, pattern classification methods for music or the stock market based on attentional states, and other past research.
Kellis (as Kamvysselis), Lueck, Rohrs
RoboLogo is a system that enables children to program interactive robots. Children can program a robotic truck that interacts with the environment without having to deal with low-level implementation details. Using iLogo, a high-level interactive language, the children can easily describe the robot's actions in the environment and just as easily program the robot's reactions to external events, such as encountering an obstacle. For example, to program a robot that bounces between two walls, the user need only write: do.while [ do.unless [ forward ] (or readsensor fl readsensor fr) [ # do nothing ] do.unless [ backward ] (or readsensor bl readsensor br) [ # do nothing ] ] TRUE This iLogo program is parsed and translated to assembly code. The resulting A51 file is linked with the low-level RoboLogo library routines and compiled into machine code. The Intel HEX file thus generated is downloaded onto the RoboLogo truck via the serial port. Unaware of the details, the child can now disconnect the programmed robot watch it run, and interact with it. The goal of our 6.115 project was to enable this transition from the abstract description of the robot's behavior to an actual moving robot. This involved managing the mechanics and sensors of a robotic truck, laying out the electronics to control it, writing the assembly routines that coordinate its actions and sensors, developing the iLogo language, and implementing the compiler that translates iLogo to assembly code.
Kellis (as Kamvysselis), Marina, Vassef, Carter
To understand a pattern, you have to be in the right mood, the right attentional state. We constructed a pattern recognition system that modelled the state transitions of the human brain, as described by Seymour Ullman and implemented by Sajit Rao for the visual cortex. We used the same states that Sajit used for vision, namely: Select Location Focus attention on new location Establish Properties at the new attentional state Learn low-level patterns and patterns of state changes. What makes the system able to generalize from one music piece to an entire category is the one thing that remains constant within a class of music compositions: namely, the pattern of changes in attentional state.
Kellis (as Kamvysselis), Nielsen
The W3C robot's purpose in crowling the web is not to accumulate data from web sites, to generate a database, or an index, or to update documents, or verify links, even though all of the functionality described above can easily be added, without changing the basic skeleton of the robot as it is today. The primary purpose of the robot is to understand the relations between web pages, and not the content of the pages themselves. How many paths can lead to a given page, what tree structures can be generated by following links under a certain constraint, how many degrees of separation lie between any two pages, what is the shortest path to reach a site, and how can intelligent navigation be automated. The robot will also be used as a test tool interacting with the w3c-libwww-99. The robot code is written in Tcl, the graphic interface to the user is written in Tk, and the library code used to provide all the network functionality is written in C. All functionality is therefore portable in almost any system with network access. The most elementary web robot can fetch a web document, extract the links from that document, follow the urls, fetch the documents, and restart for every document. The leads to an exponentially growing number of documents to follow every time you reach a new depth. Therefore, for a robot to be of any use, we need a way of specifying a Constraint Model that will allow the robot to choose which documents to follow and which not to. The Constraint Model is basically a set of rules, that the user specifies, or that the program itself can formulate, to follow a crawling model specified by the user at the beginning, or during the crawling. The next section describes the consituent blocks of the rules (or Rule Atoms) and the ways to combine those rules.
Kellis (as Kamvysselis), Mehrotra, Warshawsky
This paper describes our attempt at implementing a stock price prediction system implementing various AI techniques. We found relatively poor performance across the gamut of techniques attempted. We conclude that stock prices are a random walk across the general mean of the system. Therefore, we propose that it is virtually impossible to predict with a high degree of certainty how a stock price will perform in the future.
Lastly, Prof Kellis was recruited a few times to editors to write reviews or meeting reports
Bernstein, Kellis
Computational and experimental genomics researchers convened at Cold Spring Harbor Laboratory at the end of 2004 to address the ambitious goal of identifying all the functional elements in the human genome. The functional elements discussed at the meeting included protein-coding genes, regulatory elements, RNA genes and DNA sequences that dictate chromosome structure or replication. The presentations described diverse approaches to the problem, ranging from innovative comparative genomic methods to highthroughput functional assays designed to identify and validate such elements.
The annual meeting on Advances in Genome Biology and Technology was very different this year - in contrast to previous years, only a handful of talks covered the latest large-scale sequencing projects and the next species to be sequenced. This meeting took for granted that we can sequence, assemble and align complete genomes - achievements that only a few years ago seemed daunting, if not unthinkable. The focus of the meeting has instead shifted towards the new challenges in genomics, particularly in the areas of gene regulation, cell dynamics and genome evolution.
Ideally, we should be able to systematically discover all the functional genes in a newly sequenced genome from its sequence alone. Computational discovery methods rely both on the direct signals used by the cell to guide transcription, splicing, and translation, and also on indirect signals such as evolutionary conservation. In this paper, we summarize the principles of a classification-based approach for systematic gene identification, on the basis of comparative sequence information from multiple, closely related species. We first frame gene identification as a classification problem of distinguishing real genes from spurious gene predictions. We then present the Reading Frame Conservation (RFC) test, a new computational method implementing such a classification approach, on the basis of the patterns of nucleotide changes in the alignment of orthologous regions. We finally summarize our results of applying this method to reannotate the yeast genome, and the challenges of using related methods to discover all functional genes in the human genome.
Kellis, Rinn
From a chemical standpoint, a cell can seem as a densely intertwined mess of molecules, but their emergent systems-level properties exhibit structure, coordination and order, leading to the orchestrated complexity we call life. As the seventh annual meeting on Systems Biology: Global Regulation of Gene Expression at the Cold Spring Harbor Laboratory showcased, systems biology aims to bridge the vast gap of knowledge between the pairwise molecular interactions of individual cellular components and the large-scale behavior of cellular circuits that result from their synergism. The talks given at the meeting exemplified a new definition of systems biology, where genomic technologies can revolutionize our understanding of classical problems by enabling molecular interactions to be linked to systems behavior in a mechanistic way