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Cell-type specificity of ChIP-predicted transcription factor binding sites
Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding?
We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity.
Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.
Transcription factors (TFs) are proteins that bind sequence elements in DNA and thereby affect expression of neighboring or distal genes. Depending on cellular contexts, such as hormone stimulus or the cell's differentiation state or cell type, a TF can bind to different subsets of the TF's potential binding sites and regulate different gene expression programs
There are three main factors that determine a TF's binding activity at a potential binding site. First, TFs bind to specific sequence motifs
The TF binding activities that result from a given cellular context form in sum a transcription regulatory network. There are many different methods of inferring the structure of such regulatory networks
In comparison, many traditional methods for inferring regulatory networks are context-indifferent, typically relying on sequence motifs to map putative transcription factor binding sites (TFBS). Some of these methods use additional data such as a putative site's conservation level in related species
Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is the current high-throughput experimental technique of choice for mapping the genome-wide state of chromatin, and this technique is also used for experimentally identifying TFBS
The few studies that have investigated cell-type specificity of TFBS show that in general, binding differences increase with functional and evolutionary distance. A study investigating MyoD-binding in the highly related cell types myoblasts and myotubes found the majority of predicted binding sites to be common in both tissues
To address this question, we used ChIP-seq data from two ENCODE cell lines
The number of peaks varies between cell-types
Recent genome-wide analyses of TF binding have shown that for a few individual TFs, binding sites can vary substantially between cell-types
Additional file 1: Peaks and SNPs
This tab-separated file includes all peaks and the SNPs mapping to the peak regions. The fields are peakID, cell type, TF, chromosome, peak start, peak stop, height, overlapK562, overlapHeLa-S3, overlapGM12878, overlapPromoter, SNPs. The SNP field is further delimited by a dash (−) for each SNP in the peak region. Each SNP is described by ID, genotype (0=as reference, 1=alternate allelle), genotyping quality score estimated by SAMtools
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Figure 1
Discrepancy in peak count and variable peak overlap between cell types
Discrepancy in peak count and variable peak overlap between cell types. A) Number of ChIP-seq peak regions per TF in cell types K562 and HeLa-S3. The number of peaks varied for each TF, but there were also big differences between cell types for the same TF. B) Percentage of peaks found in promoter regions per TF in K562 and HeLa-S3. A promoter region was defined as the region 2000bp upstream and 200bp downstream of the RefSeq genes' transcription start sites plus their first intron. On average, a third of all peaks were found in promoter regions. C) Peak overlap between K562 and HeLa-S3 relative to potential overlap; that is, the number of peak regions in K562 that overlap by at least one base pair with a peak region in HeLa-S3, divided by the lesser of the peak counts in K562 and HeLa-S3. 33% of K562 and 30% of HeLa-S3 peaks had at least one overlapping peak of the same TF in the other cell type.
Nearly half of the K562 peaks (47%) and more than a third of the HeLa-S3 peaks (34%) were associated with promoter regions; specifically, the 2000bp upstream and 200bp downstream of RefSeq genes' transcription start sites plus their first introns (Figure
Only a third of peaks are found in both cell types
Given the high variability in putative TFBS, we wondered to what extent these binding sites were cell-type specific. By comparing the genomic loci of peaks across cell types, we found that 33% of K562 and 30% of HeLa-S3 peaks overlapped with at least one peak of the same TF in the other cell type. Taking into account that the relative overlap is limited by the cell type having the fewest peaks for the TF, we found that the potential overlap varied between 16% (TAF1) and 93% (BRF2) (Figure
An extreme example of seemingly different binding between cell types is the cell-cycle associated factor E2F4, which had 8780 peaks in K562 but only 631 (7%) in HeLa-S3. A large fraction (81%) of the relatively few E2F4 peaks in HeLa-S3 overlapped with K562 peaks. Others using ChIP-chip have previously found E2F4 to have between 500 and 700 target genes with little cell-type specific binding
Peaks in regions associated with tissue-independent regulation have higher overlap than have other regions
Because of the unexpected differences between the two cell lines for the E2F4 peaks, we wondered whether overlapping peaks were enriched in genomic regions known to have limited tissue-specific activity. A previous study has indicated that TFs primarily mediate cell-type specific regulation through enhancers located far from core promoter regions
Figure 2
Explaining regions of increased peak overlap and TF expression difference
Explaining regions of increased peak overlap and TF expression difference. A) Relative overlap (see Figure
Housekeeping genes tend to maintain similar expression levels across cell types and are therefore also likely to have similar regulation within different cell types. Indeed, the overlap was greater for peaks mapping to promoters of housekeeping genes
Cell-type-specific peaks map to different genomic regions in the cell types
Some of the "missing" overlap could perhaps be explained by alternative transcription factor binding sites, where a binding site active in only one cell type could have another nearby site active in the other cell type. To test this possibility, we counted the number of overlaps when allowing increasingly larger regions surrounding the original peak regions (Figure
Differences in TF expression can partly explain differences in peak counts
The variability in peak numbers could suggest that some TFs have more binding activity in one cell type compared to the other. As one important determinant of binding activity could be TF availability, we looked at how the TF mRNA expression levels, as measured by RNA sequencing, correlated with the number of peaks. We found some, but insignificant, correlation between TF expression and peak numbers (Pearson coefficient
Of course, the mRNA level does not necessarily alone determine the TF's actual protein-level
High peaks are less cell-type specific and more associated with active gene regulation than are low peaks
The height of a ChIP-seq peak indicates the TF's binding activity or strength at the site. Moreover, peak height can give a measure of the likelihood for the TFBS to be active in a given cellular context, as spurious binding events occurring within a fraction of the cells will give low ChIP-seq peaks. We therefore wondered whether cell-type specific binding or binding within regions consistent with regulatory activity, such as promoters or open chromatin, varied with increasing peak height (Figure
Additional file 2: Figure S1
Higher peaks in HeLa-S3 have more consistent support in other data marking regulatory regions. As Figure
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Additional file 3: Figure S2
Higher peaks in GM12878 have more consistent support in other data marking regulatory regions. As Figure
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Figure 3
Higher peaks overlap more and have more consistent support in other data marking regulatory regions
Higher peaks overlap more and have more consistent support in other data marking regulatory regions. Peaks in K562 for each TF (panel columns) were binned in 10 equally-sized groups with increasing peak height (ordered along x-axis) and the average values for different genomic characteristics (panel rows) were computed for each group (y-axis). From top to bottom row, the genomic characteristics are: "HeLa-S3": percentage of peaks in K562 that overlap with a peak in HeLa-S3.; "Promoter": percentage of peaks that overlap with promoter regions.; "CpG rich": percentage of peaks that overlap with CpG-rich regions.; "DNase": average count of DNase-seq reads in peak region—a measure of chromatin accessibility.; "H3K4me3": average count of H3K4me3 ChIP-seq reads in a peak region—a measure of chromatin activity.; "phyloP": average phyloP scores in peak region for a 28-way placental mammals multiple alignment—a measure of sequence conservation.; "PWMscore": average maximal PWM score in peak region (where available).; "ClusterTF": average number of peaks in peak cluster. See Methods section for definition of promoters, CpG-rich regions, and clusters and for details on other genomic data. The blue line in each panel is a linear regression line between peak height bin and genomic characteristic; the dark gray areas surrounding these lines are 95% confidence intervals; blue stars mark significant regression line slopes (p ≤0.05).
For 10 out of 13 TFs in K562, there were significantly more overlaps among higher peaks compared with lower peaks (Figure
High peaks were generally more associated with regulatory regions such as promoters (8 TFs significant) and CpG-rich regions (10 TFs significant). The TFs that showed little or no association were not expected to be associated with Pol II promoters; CTCF acts primarily in intergenic and intronic regions
Additional file 4: Figure S3
Pol III promoter overlap correlates with peak height for Pol III-associated factors. Similar to Promoter data in Figure
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Even though others have demonstrated poor conservation of binding sites across species for some TFs
High peaks were also generally more associated with sequence motifs (peaks for 5 of 7 TFs in K562 and 7 of 7 TFs in HeLa-S3 were positively correlated with PWM scores). However, c-Fos peak heights in K562 correlated negatively with PWM scores for the AP-1 motif, which c-Fos recognizes as a dimer with co-factor Jun. One possible explanation could be that competition with Jun homodimers and Jun-ARF2 heterodimers for the AP-1 motif may have pushed c-Fos to bind more in non-canonical motif regions
Using discriminative motif discovery on the 10% highest and 10% lowest c-Fos peaks (see Methods), we found that the highest c-Fos peaks in K562 had motif sequences such as CCAAT and CGCGG, which resemble binding profiles for NF-Y and parts of AP-2, but we did not find any AP-1 binding motif or variant thereof. When using sequences from another TF (CTCF) as negative data and running motif discovery with high and low c-Fos peaks separately as positive data, we again could not find the AP-1 motif in the high peaks (Additional file
Additional file 5: Figure S4
Different motifs in low and high c-Fos peaks. A) The canonical AP-1 motif taken from the Transfac database
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As an additional measure of regulatory activity, we looked at how peaks from all TFs clustered together, under the assumption that clustered peaks indicate a region of high regulatory activity
Additional file 6: Figure S5
After balancing GC-content, higher peaks in K562 still have more consistent support in other data marking regulatory regions. As Figure
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Differences in chromatin state suggest cell-type specific regulation
High TF peaks both had a higher degree of overlap between cell types and were more associated with open chromatin and active transcription, compared to low peaks. However, it was still unclear to what extent cell-type specific differences in high and low peaks were related to differences in chromatin structure or to spurious binding events. We therefore compared chromatin data for K562 and HeLa-S3 in the regions that contained the 30% highest overlapping and non-overlapping peaks and repeated the comparisons for the 30% lowest peaks.
In general, chromatin was more accessible and had a higher signal for the active histone mark H3K4me3 in the cell type where the cell-type specific peak regions were found, compared to the same regions in the other cell type (Figures
Figure 4
Differences in chromatin accessibility in cell-type specific peak regions suggest cell-type specific regulation
Differences in chromatin accessibility in cell-type specific peak regions suggest cell-type specific regulation. Chromatin accessibility as measured by DNase sensitivity for the two cell types in peak regions that are cell-type specific for K562 (A) and HeLa-S3 (B) and in peak regions that are common for both cell types (blue bars in A and B). Only the 30% highest peaks are analyzed. A) Box-plots showing for K562-specific TF peak regions, the distributions of DNase-seq signal in K562 (red) and HeLa-S3 (green), and for TF peak regions common to K562 and HeLa-S3, the distribution of DNase-seq signal in K562 (blue). The DNase-seq signal was the read per million-normalized number of reads mapping to each peak region divided by the region length. BRF2 has only one box-plot as all BRF2 peaks overlapped in the two cell lines. B) Similar data as in (A), but for HeLa-S3-specific peak regions the DNase-seq signal in HeLa-S3 (green) and K562 (red) and for peak regions common to K562 and HeLa-S3 the DNase-seq signal in HeLa-S3 (blue). Most of the TFs have comparable DNase-seq signals at the common peak regions in the two cell lines (compare blue bars in A and B). Moreover, most of the TFs show symmetric signals at the cell-type specific peak regions, such that the regions that are specific for K562 have higher DNase-seq signals in K562 than in HeLa-S3 (A) and vice versa (B).
Figure 5
Differences in H3K4me3 signal in cell-type specific peak regions suggest cell-type specific regulation
Differences in H3K4me3 signal in cell-type specific peak regions suggest cell-type specific regulation. H3K4me3 enrichment for the two cell types in peak regions that are cell-type specific for K562 (A) and HeLa-S3 (B) and in peak regions that are common for both cell types (A and B). Only the 30% highest peaks are analyzed. A) Box-plots showing for K562-specific TF peak regions, the distributions of H3K4me3 ChIP-seq signal in K562 (red) and HeLa-S3 (green), and for TF peak regions common to K562 and HeLa-S3, the distribution of H3K4me3 ChIP-seq signal in K562 (blue). The H3K4me3 ChIP-seq signal was the read per million-normalized number of reads mapping to each peak region divided by the region length. BRF2 has only one box-plot as all BRF2 peaks overlapped in the two cell lines. B) Similar data as in (A), but for HeLa-S3-specific peak regions the H3K4me3 ChIP-seq signal in HeLa-S3 (green) and K562 (red) and for peak regions common to K562 and HeLa-S3 the H3K4me3 ChIP-seq signal in HeLa-S3 (blue). The H3K4me3 ChIP-seq data show the same patterns as the DNase-seq data (see Figure
For the lowest 30% of peaks we expected to see smaller differences between the chromatin state in peak and non-peak regions, as a smaller difference would be consistent with increased stochastic noise due to more spurious binding within the peak data. Indeed, although the differences in DNase sensitivity and H3K4me3 signal were more consistent for the weak than for the strong peaks (11 TFs significant for DNase both in K562 and HeLa-S3; 11 TFs in K562 and 13 TFs in HeLa-S3 for H3K4me3; Additional file
Additional file 7: Figure S6
Low cell-type specific peaks have differences in chromatin accessibility. As Figure
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Additional file 8: Figure S7
Low cell-type specific peaks have differences in active histone modifications. As Figure
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Additional file 9: Figure S8
High cell-type specific peaks have a larger difference in chromatin signal than low peaks. A) Peaks were binned in equally-sized bins sorted on peak height. The upper panel (K562) shows the difference in median DNase-seq read count pr bin between K562 and HeLa-S3 for K562 specific peaks, whereas the lower panel shows the difference between HeLa-S3 and K562 for HeLa-S3 specific peaks. B) As A), but for H3K4me3 signal instead of DNase accessibility. Within the set of peaks that are unique to a cell type, the higher peaks have larger difference in chromatin accessibility and active marks between the cell types than lower peaks. This suggests that higher cell-type specific peaks are more likely to be due to cell-type specific regulation of the chromatin.
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Different genotypes can contribute to binding differences
Another factor that could contribute to differences in binding between cell types is single nucleotide polymorphisms (SNPs), as K562 and HeLa-S3 are established from two different individuals with different genotypes. We aligned available DNA sequencing data for K562 and HeLa-S3 to the reference genome (hg18) and by using SNPs from the HapMap database and the aligned sequence reads, we calculated the most likely genotype for each cell type for the SNPs in peak regions (see Methods).
In total, 12% of the peaks harboured at least one SNP. Relative to peak count, more overlapping peaks had SNPs (13.9%) than K562-specific (11.8%) or HeLa-S3-specific (12.1%) peaks. But a relatively higher number of SNPs in cell-type specific peaks were homozygous with different alleles in the cell types (18%, 17.8%, and 10% for K562-specific, HeLa-S3-specific, and overlapping peak SNPs, respectively).
We reasoned that the SNPs that were homozygous for one allele in one cell type and homozygous for another allele in the other cell type could affect TF binding if the SNP resided in a high-scoring TF motif (Figure
Figure 6
Genotype differences in sequence motifs can give cell-type specific peaks
Genotype differences in sequence motifs can give cell-type specific peaks.A) A specific example of how different alleles can create differences in sequence motifs, possibly causing cell-type specific TF-binding. SNP rs7138374 (chr12:130,642,970) is located at the highly conserved position 7 in the highest scoring AP-1 sequence motif in a K562-specific c-Fos peak region. K562 is homozygous for the A allele (green letter) and has a peak (illustrated here by a curve; top of panel). HeLa-S3 is homozygous for the T allele (red letter), which disrupts the motif, and has no peak (illustrated by absence of curve; middle of panel). The bottom part of the panel shows the sequence logo for the AP-1 sequence motif. B) A comparison of PWM motif score distributions in K562- and HeLa-S3-specific peaks that contain SNPs in the highest-scoring sequence motif regions in the peaks, and where these SNPs are homozygous but have different alleles in the two cell types. The two leftmost box-plots compare for the K562-specific peaks that contain such homozygous SNPs, the PWM motif scores for the K562 and HeLa-S3 genotypes (K562/K562 and K562/HeLa, respectively); the two rightmost box-plots compare for the HeLa-S3-specific peaks that contain such homozygous SNPs, the motif scores for the HeLa-S3 and K562 genotypes (HeLa/HeLa and HeLa/K562, respectively). The K562-specific peaks have significantly higher motif scores for the K562 genotype (K562/K562) than for the HeLa-S3 genotype (K562/HeLa), whereas the HeLa-S3-specific peaks have significantly higher motif scores for the HeLa-S3 genotype (HeLa/HeLa) than for the K562 genotype (HeLa/K562;
Genotype-related differences were also evident in peaks common between the two cell types. Specifically, for the SNPs that had different homozygous genotypes and were located in high-scoring motif regions, the difference in motif score between the genotypes correlated positively with the difference in peak height between the cell lines (
Peak height and clustering are the most important factors for cell-type specificity
Our results so far indicated that the cell-type specificity we observed among ChIP-seq peaks was related to a number of different factors that included peak height and locus, sequence properties of the peak region, and the region's cell-type specific chromatin context. But which factors are the most important indicators of cell-type specific TF binding and to what extent do combinations of factors determine cell-type specific binding?
To address these questions, we used a machine learning approach. Specifically, we created support vector machine (SVM) classifiers
Additional file 10: Figure S9
ROC curves. A) ROC curves on 10-fold stratified cross-validation on K562 and HeLa-S3 training and testing data. X-axis is true positive rate, Y-axis is 1-false positive rate. B) ROC curves on 10-fold stratified cross-validation on K562/HeLa-S3 training and GM12878 testing data.
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To assess which SVM features were most important for correct classification, we removed a single feature group at a time from the SVM and measured how this affected the SVM's cross-validation performance (Figure
Figure 7
Most important features for classification of peak cell-type specificity
Most important features for classification of peak cell-type specificity.A) Difference in average 10-fold crossvalidated ROC-score for each TF SVM classifier after removing all features within a feature group (see Table
Given the major importance of peak height and clustering for classifier performance, and in light of previous results (Figure
Additional file 11: Figure S10
Most important feature group for classification. Difference in ROC score after first removing confounding factors (peak height and clustering), and grouping features into three groups (cell-type specific, promoter/sequence, phyloP) and then removing a given feature group. Error bars show average ROC score change on 10 cross-validation folds plus/minus one standard deviation.
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Peak height and clustering were the dominant factors in explaining cell-type specificity and could therefore confound the effects of other features when analyzing the total set of all peaks. We therefore did a separate analysis of the highest and most clustered peaks, as differences between cell types for these peaks should be due to other factors than peak height and clustering. Specifically, we first identified the subset of peaks that were among the 10% highest and that also occurred among the top 20% largest peak clusters; that is, in clusters containing peaks from six or more TFs. Second, we did a feature removal analysis on the five TFs that had more than 100 remaining peaks in both the overlapping and the cell-type specific datasets. Although none of the features on this reduced dataset showed the same consistent pattern as the height and cluster features did on the complete dataset, five of the eight features (Length, TSS, H3K4me3, H3K27me3, and CpG) had a positive impact on the prediction for the majority of the five TFs (Figure
Models for identifying cell-type specific peaks are consistent between cell lines
The feature analysis suggested that even though peak height alone was a critical factor for identifying cell-type specific peaks, other features could also help predictions. To further assess the effect of adding additional features, we therefore developed two reference models, which we refer to as Height and HPP. These models were also based on SVMs, but contained fewer features; Height used only peak height as its single feature and HPP used peak height, phyloP conservation score, and PWM score (Figure
Figure 8
Best prediction performance with all features
Best prediction performance with all features.A) ROC-score for each TF classifier using the SVM model with all features (SVM), with peak height only (Height), and with peak height, phyloP conservation and PWM score (HPP), after 10-fold stratified cross-validation on the dataset consisting of K562 and HeLa-S3 peaks. B) ROC-score after 10-fold stratified cross-validation when training classifiers on K562 and HeLa-S3 peaks, and then testing for overlap with peaks from a third cell type, GM12878.
Consistent with the feature analyses, the SVM model that included all features (termed SVM in Figure
So far we had tested how the models performed when classifying new unseen peaks from the same cell types (K562 and HeLa-S3) used during training, but would the models generalize to a different cell type? To address this question, we used a stratified 10-fold cross-validation procedure where we first trained the models on a subset of K562 peaks that were common and cell-type specific compared with HeLa-S3 peaks. We then tested the models on a different subset of K562 peaks that were common and cell-type specific compared to peaks in the GM12878 ENCODE cell line; that is, we trained the models on K562 and HeLa-S3 data and tested the models on K562 and GM12878 data. Importantly, none of the peaks in these train and test sets overlapped and the cell-type specific features (Chromatin, H3K4me3, and H3K27me3) were derived from two different cell types in the train (HeLa-S3) and test (GM12878) sets. We trained and tested the models on the five TFs with ChIP-seq data available in all three cell types.
Whereas the Height and HPP models had similar performance in the new cell type (
These inconsistencies between the cell types also strongly affected the Height and HPP models on the c-Fos data. As already mentioned, Height could not predict c-Fos peaks that overlapped between K562 and HeLa-S3, whereas the HPP model trained on K562 and HeLa-S3 data could not predict peaks that overlapped in K562 and GM12878. Of the five TFs, c-Fos had the least consistent binding sites between the three cell types (Additional file
Additional file 12: Figure S11
K562 peaks overlap. Overlap in different cell types for all K562 peaks. CTCF and GABP have many common peaks between all the cell types, whereas c-Fos have few common peaks between all cell types.
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There is limited knowledge on how regulation by transcription factors in general varies between different cellular conditions, and what causes specific transcription factor binding. Most newer approaches for inferring gene regulatory networks rely on experimental data that partly limit the inferred networks to a specific cellular context. While this makes it possible to focus on the interactions that are active in the particular context under study, it also means that it can be difficult to completely map the general regulatory network by using these approaches
Our initial analysis indicated that in general, the number and location of binding sites as given by ChIP-seq peak data varies substantially between HeLa-S3 and K562—even for the same TF. The average peak overlap of 30% we found is in accordance with previous findings on TF binding specificity among different cell-lines
These binding differences can partly be explained by cell-type specific regulation. Specifically, our results show that chromatin accessibility, histone modifications, and genetic variations in peak regions can explain some of the cell-type specific peaks. Another factor that could establish alternative binding contexts is a difference in availability of TF co-factors across cell types
Additional file 13: Table S1
Significant co-factors. This table shows the co-factors having significant expression differences between K562 and HeLa-S3. Co-factors shown in bold text also had a PWM available and significant difference in PWM score distributions between cell-type specific peaks.
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Both high and low affinity binding sites are, however, known to have biological importance in regulating gene expression
Peak height and clustering are the most important features for the SVM. Consequently, if context-specific peaks are indeed much more noisy than context-indifferent peaks, the SVM classifiers are perhaps to a greater degree learning to differentiate between real binding sites and noise, and to a lesser degree learning to differentiate between context-independent and context-dependent binding sites.
Our results show that low peaks are less consistent than high peaks, but what are then potential causes for these inconstancies? Weak, apparently random TF binding, non-specific antibody binding, and sequencing errors combined with peak calling artefacts
Many methods for inferring gene regulatory networks are based on ChIP-seq-predicted transcription factor binding sites, but little is known about how specific these data are to the experimental context. Here we find that on average a third of ChIP-seq predicted transcription factor binding sites overlap between the cell lines K562 and HeLa-S3. This number is in accordance with previous findings on cell-type specificity and suggests that regulatory networks inferred uncritically from ChIP-seq data will, in general, generalize poorly to different cellular contexts. However, some subgroups of peaks are less context-specific than others. Specifically, peaks in regions associated with tissue-independent regulation, such as CpG-rich promoter regions, and more generally, high and clustered peaks associated with strong and conserved binding sites, tend to be more cell-type independent.
Even though some of the discrepancy between the two cell types can be caused by cell-type specific regulation of chromatin and co-factors, the results indicate that most of the discrepancy—especially among the low peaks—is due to stochastic noise in the ChIP-seq data. Our results do, however, not exclude that different sub-populations contribute to the discrepancy. Specifically, the small but consistent differences in DNase sensitivity and H3K3me3 signal we observed for the 30% lowest peaks are consistent with distinct sub-populations within the data. Nevertheless, context-independent attributes, such as PWM score and sequence conservation, are more associated with stronger and more overlapping peaks than with weak and isolated peaks.
Moreover, both sequence-specific TFs and sequence-independent cofactors shared the same characteristic of stronger peaks being more cell-type independent. Indeed, even the sites for the Pol III subunit RPC155, which is enriched at Pol III promoters and shows highly correlated peak heights between different cell lines
It might be a while before binding sites of most TFs are mapped in a majority of tissues and cell lines. Until then, any available ChIP-seq data, however noisy and condition-specific, is likely to serve as our best source of information on the location and context of regulatory elements.
ChIP-seq data and peak calling
The ChIP-seq data were based on public genome-wide ChIP-seq datasets from the ENCODE project
ChIP-seq peaks were identified in sample and replicate data by two different peak-finder programs, MACS
Defining overlap regions and peak clusters
Data from the cell type K562 was used as a reference in further analysis unless stated otherwise. A peak in a cell type was defined as overlapping with another peak in a different cell type if the two peak regions shared at least one base pair. Relative overlap was calculated as the number of overlaps divided by the number of peaks in the cell type (K562 or HeLa-S3) having fewest peaks for the given TF. Promoter regions were defined as the region 2000 bp upstream to 200 bp downstream of transcription start sites of RefSeq genes (as of October 22. 2009), together with the first intron of the gene. Pol III promoters were defined as the regions -2000bp upstream and +200bp downstream of tRNA transcription start sites from the tRNAscan-SE Genomic tRNA Database
CpG frequencies and CpG region types were computed as in
Peaks were clustered by first extending each peak region to a total length of 2000bp. Peaks overlapping within the extended region were then identified as belonging to the same cluster
TF expression
Paired-end RNA-seq reads were downloaded from the ENCODE Caltech RNA-Seq track in the UCSC Genome Browser. Both available replicates were used and the number of reads mapping to each RefSeq exon was counted. The count was normalized on exon length and averaged, to get the expression for a given RefSeq. Counts were also normalized on total number of reads within one experiment when comparing across experiments. The expression for a TF was averaged from all RefSeqs whose gene symbol mapped to the TF and had at least 1 read mapping to an exon.
Peak binning, chromatin differences, and PWM score
To investigate how peak height correlated with other genomic features, we binned peaks in 10 approximately equally-sized groups according to peak height (i.e. number of tags mapping to peak region). Each peak got a measure of chromatin accessibility using data on DNaseI hypersensitivity from the ENCODE Open Chromatin track available from the UCSC Genome Browser: Using the tag files with aligned reads from the DNaseI hypersensitivity sequencing (DNase-seq) experiments, we counted the number of tags that overlapped with each peak region and divided by the peak region length and total number of tags (in millions) in the experiment to control for variation in peak region length and differences in sequencing depth between experiments. We used version 2 of the dataset and all available experimental replicates. Likewise, data on H3K4me3 and H3K27me3 was taken from the ENCODE UW Histone ChIP track. The average phyloP score in each peak region was calculated from the phyloP28Way placental mammals multiple alignment scores, also available from UCSC.
An in-house developed program was used to calculate PWM scores
Additional file 14: Table S2
PWMs. The position weight-matrix identifiers of the PWMs taken from Transfac Professional
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Genotyping and SNP analysis
Raw reads from ENCODE
A list of potential co-factors for each TF was made by combining data from the Fantom consortium
Discriminative motif discovery in 500 bp regions centered on cell-type specific peaks was done with DREME
SVM features and classification setup
The list of features for the SVM method is available in Table
The chromatin and histone data for each peak were calculated as explained above. For the features we calculated the average read count in the peak region in two cell types, and also the difference in read count between the cell types. CpG features were computed as explained above (in "Defining overlap regions and peak clusters"). The phyloP feature was calculated for each peak region by using a weighted average of all the signal values that overlapped with a peak region, weighting each value by the length of the signal region.
Two 10-fold crossvalidation tests were performed on all three methods using peaks from the K562 cell type. The first test investigated the ability to recognize which K562 peaks overlapped with HeLa-S3 peaks when trained and tested on different parts of the K562 dataset. The second tested how the methods could predict which K562 peaks overlapped with GM12878 peaks when being trained on data from comparing K562 peaks with HeLa-S3.
When doing feature elimination on single features, all features within a single feature group were removed from the dataset before training and testing. Feature elimination was also done with larger groups of features, as explained in Results. All of the tested methods were based on the same Support Vector Machine setup for consistency. The classification was performed using a combination of own code and the PyML machine learning framework
The ChIP-seq peak region data are available upon request.
Competing interests
The authors declare that they have no competing interests.
Author's contributions
Analyzed the data and drafted the article (TH). Created the peak region datasets and cluster regions from ChIP-seq tag count data (MR). Calculated genotype for SNPs in the cell types (RM). Conceived the study (TH, PS, FD). Helped prepare the final article and supervised the work (PS). All authors read and approved the final manuscript.
PS receives funding from the Norwegian Functional Genomics Program (FUGE) and the eScience Program of the Norwegian Research Council.
Insights from genomic profiling of transcription factors
What are DNA sequence motifs?
The role of chromatin accessibility in directing the widespread, overlapping patterns of Drosophila transcription factor binding
Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection
Low nucleosome occupancy is encoded around functional human transcription factor binding sites
A genomic code for nucleosome positioning
The role of chromatin during transcription
Myc-binding-site recognition in the human genome is determined by chromatin context
Histone modifications at human enhancers reflect global cell-type-specific gene expression
Linking DNA methylation and histone modification: patterns and paradigms
An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man
ChIP-seq: advantages and challenges of a maturing technology
ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context
Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles
Computational discovery of gene modules and regulatory networks
Inferring transcriptional modules from ChIP-chip, motif and microarray data
Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model
SIRENE: supervised inference of regulatory networks
MotifMap: a human genome-wide map of candidate regulatory motif sites
MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model
Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity
Assessment of composite motif discovery methods
Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution
Stubb: a program for discovery and analysis of cis-regulatory modules
Applied bioinformatics for the identification of regulatory elements
High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites
A nucleosome-guided map of transcription factor binding sites in yeast
Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity
Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming
Cell cycle genes are the evolutionarily conserved targets of the E2F4 transcription factor
Serum response factor binding sites differ in three human cell types
Tissue-specific transcriptional regulation has diverged significantly between human and mouse
Five-Vertebrate ChIP-seq Reveals the Evolutionary Dynamics of Transcription Factor Binding
Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project
Genomic binding profiles of functionally distinct RNA polymerase III transcription complexes in human cells
A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs
Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart
A complex intronic enhancer regulates expression of the CFTR gene by direct interaction with the promoter
Human housekeeping genes are compact
A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters
Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome
Comparing protein abundance and mRNA expression levels on a genomic scale
CTCF: master weaver of the genome
Asymmetric recognition of nonconsensus AP-1 sites by Fos-Jun and Jun-Jun influences transcriptional cooperativity with NFAT1
Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements
Support vector machines and kernels for computational biology
Cloning and expression of human TAFII250: a TBP-associated factor implicated in cell-cycle regulation
A ChIP-Seq Benchmark Shows That Sequence Conservation Mainly Improves Detection of Strong Transcription Factor Binding Sites
Extensive low-affinity transcriptional interactions in the yeast genome
Extensive role of the general regulatory factors, Abf1 and Rap1, in determining genome-wide chromatin structure in budding yeast
A signal–noise model for significance analysis of ChIP-seq with negative control
Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors
The UCSC genome browser database
Model-based analysis of ChIP-Seq (MACS)
Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data
GtRNAdb: a database of transfer RNA genes detected in genomic sequence
TRANSFAC: Transcriptional regulation, from patterns to profiles
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update
Initial sequencing and analysis of the human genome
Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. (dbSNP Build ID: 130)
The sequence alignment/map format and SAMtools
The international HapMap project
DREME: Motif discovery in transcription factor ChIP-seq data
Quantifying similarity between motifs
genenames. org: the HGNC resources in 2011
PyML - machine learning in Python
WebLogo: a sequence logo generator


text:
Catholic Life Communities
Teens "Go Deeper" at CLC
Catholic Life Communities (CLCs) at All Saints have been a tremendous source of support for the many young people who have been a part of them. A small faith community of teens committed to living their Catholic faith in their schools and families, CLC provides an atmosphere where it is fun to strive for holiness.
Boys CLC meets from 7:30-9:00pm on Thursdays in the Upper Room.
Girls CLC meets from 7:30-9:00pm on Wednesdays in the Upper Room.
For more information on Girls' CLC, contact Robin Dalusung at firstname.lastname@example.com
For more information on Guys's CLC, contact Todd Albrigo at email@example.com
You can also use the information found on our "Contact Us" page for more details.

subset: commoncrawl


text:
Hello Guest Login Here or
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text:
BOR Hub front MD7 for Quickrelease
Article details
BOR hub front MD7 for Clamps
The BOR MD7 hub is a carefree package.
The 17 mm axle system permits the operation with quick release and 15mm thru-axle and gives the boss a sufficient capacity. The hub body made of high strength aluminum has a brake disc mount IS2000 Standard - 6 hole - and is supported by two industrial ball bearings.
Technical specifications:
Material: Aluminum
intended use: Cross Country to All Mountain
Disc mount: 6-hole IS2000 standard
axle type: Quick Release
bearing: 2 times
Mounting width: 100 mm
Number of holes: 32 holes
Color: black, orange, blue or red
Weight: 122g
Delivery: 1x front hub without quick release!
|Product weight:


text:
They rush her to the vet's
I just lay there doing nothing
They say she's not gonna survive.
Say they have to put her down.
Momma is crying,
Brother is acting stupid.
And Daddy just sits there, with a sad look on his face.
Then they stick the syrenge in her,
her eyes slowly close.
Of course I wasn't there for that.
And then they find a box and they put her inside,
lower her into the ground.
As they do that, I realize, I barely had time to say goodbye.


text:
Del Potro rolls to Estoril Open title
OEIRAS, Portugal (AP) – Juan Martin del Potro won his second consecutive Estoril Open title by beating Richard Gasquet in straight sets on Sunday.
The top-seeded Del Potro used his strong serve to win 6-4, 6-2 on the outdoor clay court in 1 hour, 27 minutes.
It was the 12th-ranked Argentine's 11th career title and second this year.
The second-seeded Gasquet, who also lost the final here in 2007 to current top-ranked Novak Djokovic, dropped service games early on in both sets.
Del Potro didn't give up a break chance in the match while hitting six aces on the way to improving his head-to-head record with his French opponent to 4-1.
Posted | Updated


text:
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|An awesome magazine theme for your WordPress site. Responsive layout, post thumbnails support, featured posts, an easy-to-use social menu and much more.
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Monitor compressed air usage and waste!
EXAIR's award-winning USB Data Logger Model 9147 connects directly to your Digital Flowmeter and is simple to use. Download the software to configure the Data Logger to record your flow rate from once a second (about nine hours of data) up to once every 12 hours (over 2 years!).
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text:
Sitter in Brownsburg
hello my name is Kelsey Clore.I have been babysitting for about 8 years now and have also helped raised my 3 younger siblings (14,11,8) and I have a 3 month old little one myself. I am a regular at my church on Sundays. I did child care there for a bit. I play softball and I am currently doing online schooling part time. my email is 317-381-... thank you and I can't wait to hear from you.have a blessed day:)or you can call/text me any time my number is
Sitter Details
|Email Address
|Phone Number
|317-381-... Confirmed
Notes made here will only be seen by you. If you want to message the owner of this listing
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text:
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Prescriptions for North American country pet drugs are well-grounded for the period such that by veterinariansability and filled totally according to their directionsability. North American country pills stores can also enough prescriptionsability for one yr if they are handwritten suitably. Maximum pharmaciesability displace a 90-day secure of pet medicines unless other than directionsability are suppliedability by the prescribingability doctor.
The feature of pet medicines is similar to what patrons can get in U.S. Few pet drugs may have different trade name name calling but have same generic opus. Pet owners are well-advised to advert to prices timetabled on the Web tract. Peak sites intelligibly order if a medicine is a taxon interpretation of the mark mark drug, so clientele will cognise what they are exploit.Post ads:
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Most pharmaciesability attribution a business fee per carton for an without end figure of prescriptionsability if pet owners establish the prescriptionsability by letters. All prescription remedy prices take in veterinarian charges and medication deliver fees. If an landowner purchasesability medicinal drug for more than one pet, all pet must have its own forms and prescriptionsability. However, cellular phone consultationsability may be addressable for several pets at the same case and their drugs can be shipped both as very well.
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text:
Heart Cambridge
Heather Small headlined on the opening night of the Big Weekend in Cambridge. Check out our pictures here.
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text:
Robin Gibb Reportedly Battling Cancer
November 21, 2011 by staff
Robin Gibb Reportedly Battling Cancer, Robin Gibb, singer and musician with legendary band the Bee Gees, is being treated for liver cancer after being diagnosed with the disease several months ago, Britain's Sunday Mirror reported.
The 61-year-old singer, whose frail appearance has caused concern among Britain's media, was hospitalised last week after making an emergency call from his house in Thame, south-west England, according to the tabloid newspaper.
Robin's twin brother Maurice died in 2003 from complications resulting from a twisted intestine, and younger brother Andy died in 1988 following a battle against cocaine addiction.
Older brother and fellow Bee Gee Barry has returned to Britain along with 91-year-old mother Barbara, according to the report.
The brothers – born in Britain and later raised in Redcliffe, Queensland – scaled the heights of the pop world in the 1970s with disco hits Stayin' Alive and Night Fever, and notched up over 200 million record sales during a career which has spanned seven decades.
Robin was forced to cancel several appointments last month, including one with British prime minister David Cameron, after he was hospitalised with severe stomach pains.
Please feel free to send if you have any questions regarding this post , you can contact on
Disclaimer: The views expressed on this site are that of the authors and not necessarily that of U.S.S.POST.


text:
Science & Medicine, Natural SciencesFollow
|Top 200
|Science & Medicine
|Natural Sciences
Science meets comedy and pop culture on StarTalk Radio! Astrophysicist and Hayden Planetarium director Neil deGrasse Tyson, his comic co-hosts, guest celebrities and scientists discuss astronomy, physics, and everything else about life in the universe. But wait... there's more! Our new show, StarTalk All-Stars, features a revolving slate of all-star scientists and science educators including Bill Nye the Science Guy, backed up by your favorite comic co-hosts. Catch new episodes of StarTalk Radio on Fridays at 7pm ET, and StarTalk All-Stars on Tuesdays at 7pm ET. Keep Looking Up!
August 5th, 2014
Episode 148 of 372 episodes
Did you smile for Cassini? StarTalk Radio host Neil deGrasse Tyson and Carolyn Porco, leader of the Cassini Imaging Team, explore the significance of the images of Saturn and Earth taken by the Cassini spacecraft. Discover why she considers these photos the greatest thing she has ever done, and her inspiration behind the legendary scene. If you enjoyed this StarTalk SoundBite, be sure to hear the full interview here: http://www.startalkradio.net/show/madame-saturn-a-conversation-with-carolyn-porco-part-2/
You'll need to sign in to write a comment.
Welcome to the Brain Training Podcast, the daily audio workout for your head. In this podcast we have two games for you, each with three rounds which get progressively harder. To enjoy the full experience, relax, and avoid distractions whilst you listen.


text:
184th Mechanized Infantry Division
Sarna News
|184th Mechanized Infantry Division
|Unit Profile (as of 2784)
|Parent Formation
|V Corps
The 184th Mechanized Infantry Division was a part of the Star League Defense Force Regular Army.
In 2764 the 184th was a part of V Corps within the Eleventh Army and was assigned to District 1 of the Lyran Commonwealth Military Region. The 18th was subsequently transferred to District 3 by 2765 in response to the Periphery Uprising.[1] The 184th survived the war and the Hegemony Campaign, but it is unknown what happened to the 184th after the liberation of the Hegemony.[1]
The Mechanised Infantry Divisions of the Star League were used offensively to exploit gaps in the enemy front created by the BattleMech Divisions. They also bore the brunt of defensive actions.
Composition History[edit]
The Division comprised two Brigades of Mechanised Infantry and a single BattleMech Brigade. Also attached was a Ground Aero Wing.[2]


text:
- Created by: Garry Stahl
- Appearance: None Yet
- Number of Members: About 100,000 Federation wide
- Nature of Members: Bureaucrats, people fascinated with decimal points and accuracy.
- Organization: Bureaucracy
- Game Role: Occasionally someone you might have to deal with.
- World Role: Maintain the Standard of weight and measure.
- Relative Influence: Major: Trade throughout the Federation is affected.
- Public or Secret?: Public, but hardly noticed.
- Publicly Stated Goal: Maintain the accuracy of the standards of measure.
- Relative Wealth: Minor. A small budget is all that is required.
- Group advantages: Authority over the Standards of weight and measure.
- Group disadvantages: Bound by the rules governing the Federation civil servants.
- Those who favor them: People wanting fair measurements.
- Those opposed to them: People trying to short the scale.
- Area of Operation: Federation wide
- Headquarters Location: Shi'kar Vulcan
- Public Face: Soulless bureaucrats flogging minutia, if any.
- Notable Members: None
- History of the Organization: Established by the Articles of Federation as a body to establish and govern a unified system of time, weights and measure throughout the Federation. At the time of the Federation signing each world had it's own system for measuring everything. Nothing matched and conversion was a bear.
It was the duty of the Federation Bureau of Measures & Standards to come up with a unified standard and to regulate that standard. To that end such things as the Stardate, the Unified Meter, Unified Light Year and other methods of measure came into being. The Federation Bureau of Measures & Standards also keeps accurate conversion tables for each and every system of measure used in the Federation today. Most local systems have not gone out of use, but don't tend to leave their planet of origin.
All Starships are built to The Federation Standard system, use the Federation Standard Stardate and so forth. This ensured that navigation is possible and that there is interoperability between dockyards and at least ships built within the Federation. Those merchant concerns that deal with interstellar trade also prefer the use of the Federation system to facilitate trade and making sure Andorian bulk containers fit in Vulcan ships.
The average Federation Citizen couldn't tell you where the office of the Federation Bureau of Measures & Standards is located on their world. There will be one, but they quietly work behind the scenes.


text:
Hey CH, I've never been to the Southwest before, but have used these board for Pacific Northwest cities before with great results.
Me, my wife and our 10 month old girl are out for a week, staying at a rental in the Gilbert/Chandler area. This is our first trip with our new baby so our usual late night happy hour excursions are out. A couple of questions..
I've got Pig & Pickle, Pizzaria Bianco (as these look more casual thus kid friendly) on my list. Any other can't miss eateries? Looking also for solid Mexican food (Vancouver BC ain't quite there yet)
Is there anything worth checking out in the Gilbert/Chandler area?
Finally, I love beer, have any breweries/brewpubs that are worth a pilgrimage? (no drive is too far for good beer)


text:
November 08, 2011
OK, this couldn't possibly have been Washington's game-plan against the Niners: Roy Helu(notes) saw as many pass targets on Sunday (17) as all of the Redskins' wide receivers combined (Gaffney 5, Hankerson 5, Austin 5, Armstrong 1, Paul 1).
Washington quarterback John Beck(notes) seemed to be acutely aware — perhaps too aware — of the prior week's sack total. Beck was dropped 10 times by the Bills in Week 8, and many of the sacks were entirely his fault, as he held the ball far too long. He certainly wasn't guilty of that mistake in the San Francisco game (only one sack), but he rarely allowed anything to develop deep, instead checking down, Jason Campbell(notes)-style. That approach helped Helu's PPR owners in no small way, though it also eliminated the possibility of big plays for Washington's offense. The Redskins' longest play from scrimmage went for 17 yards.
So this is a team (and a quarterback) still searching for the right risk/reward balance. It's tough to expect double-digit catches from any running back, versus any opponent. Let's not assume Washington intended to pepper Helu with quite so many targets. But clearly his competence as a receiver played a role in the decision to elevate him to the starting role. (That, and the fact that Ryan Torain(notes) is not especially good).
Here are just a few of Mike Shanahan's postgame thoughts, via the Post's Mike Jones:
"You have to be disciplined enough to know where to dump the football," Shanahan said. "Those are the things you have to do as a quarterback. You have to understand the clock and John would probably like to have four or five throws back to actually throw a little bit farther downfield.
"It's a combination of all of those things. You can have some hot reads, you can have a four-man rush. You've got to know where your outlet is. I thought John did a great job on a number of throws getting the ball out when they did a good job on a couple of stunts and he dumped the ball off a lot quicker than the week before. Then, a couple of times, I thought he did a little too quick where, the week before, he was kind of standing in there because we were going against a team that wasn't known for a great pass rush and this team was a lot better. And it's a growing experience."
Helu is the Washington running back to own going forward, if you absolutely have to own one. Torain can be kicked to the curb. Washington's rest-of-season schedule isn't stellar for rushing purposes (Dal, Sea, NYJ, Min, et al), but Helu was either Plan A or Plan B in every passing situation in Week 9. He should have lasting fantasy value, even if he never sees another 17-target game. When a bad quarterback finds himself a security blanket, a PPR star can quickly emerge. Helu is a pickup of interest, no doubt.
(Appreciate the courtesy laugh from Danyelle. Total pro move right there).
Running Backs
It's just ridiculous how low our expectations have fallen for Chris Johnson. If he would have delivered his Week 9 stats last season (110 scrimmage yards, no TDs), you would have complained loudly that he was killing your team. But within the context of Johnson's miserable 2011 campaign, 11 fantasy points seemed like a breakout performance. Honestly, I didn't think any of his carries were unusually impressive, not if you still hold him to a top-tier standard. He managed to escape a defender or two on the early 20-yard run, but the 21-yard catch that followed was a layup, a nice playcall, easy yardage. CJ had a few other nice moments, double-digit gains where he didn't go down in the arms of the first would-be tackler, so I guess that's progress. But he was relatively quiet in the second half.
"What you saw in the first half is what we hope will continue the rest of the year," said Mike Munchak.
The fantasy community hopes for the same thing, coach. If Johnson is truly rounding into form, he'll have a monster day at Carolina in Week 10 — and yes, I can recall saying the same thing about his match-up with Indy in Week 8.
Bottom line: I'm not dropping Javon Ringer(notes) just yet, but we can't dismiss the faint pulse detected with CJ. If you've bought low, please share the trade details in comments.
Brandon Jacobs(notes) doesn't see many five-target games — he hadn't had one since '09, in fact — so it's nice to see him produce a little something when he gets an opportunity. He has a rough match-up at San Francisco in Week 10, however, and Ahmad Bradshaw(notes) is due back soon. ... Danny Woodhead(notes) returned to the Kevin Faulk(notes) role, at least for a week, while Faulk himself was inactive (knee). Nothing to get too excited about here. These are low-yield PPR options, not must-adds in all formats. ... DeMarco Murray(notes) flashed basically every skill you want in a feature back in the Seattle win, facing a tough run defense. Remember, that dude caught 71 passes at Oklahoma last year, and 157 in his collegiate career. He looks like an all-weather, all-situation back, and you have to appreciate the upcoming schedule. These are Murray's match-ups through Week 16: Buf, at Was, Mia, at Ari, NYG, at TB, Phi. Of those seven opponents, only one ranks in the top-half of the league in run defense, and it's a one-win team (Miami, No. 14 vs. the run).
Wide Receivers
Another week, another huge target total for Brandon Lloyd(notes). He's had no fewer than 12 targets in every game since heading to St. Louis. Obviously the lousy team context hurts him; the Rams average just 12.5 total points per game, and they rank dead-last in passing TDs (four). But the yardage and target totals help Lloyd hold his value. And c'mon, it's not as if you'd feel any better about him if he were still tied to Tim Tebow(notes).
Denarius Moore(notes) is clearly back on the fantasy radar after Carson Palmer(notes) gave the rookie receiver a shot at a big day. Moore finished with twice as many targets as Jacoby Ford(notes), though the latter player had the much better fantasy line (5-105-1). Moore's target total on Sunday actually equaled Darrius Heyward-Bey(notes)'s snap count (not a joke), so we may need to rearrange the hierarchy in this receiving corps. Ford deserves more than a cameo role going forward, certainly, because he's a big-play threat who's proven himself capable of making difficult catches in tight coverage. (You might recall his ridiculous day against Brandon Flowers(notes) last season. If you don't, here's a clip). Suddenly, DHB feels like a dice roll.
Rams slot receiver Greg Salas(notes) suffered a season-ending injury in Week 9, fracturing his left fibula in the loss at Arizona. Brutal news, clearly. He'd emerged as a reliable PPR asset. Austin Pettis(notes) likely takes over his role, and he's roster-worthy in any format where Salas was owned. ... David Nelson(notes)'s fantasy day was saved by the garbage-time touchdown against the Jets, and it seemed as if half his targets arrived on that late drive. Still, that guy does something impressive each week — might be a TD, or a badass block, or a painful catch in heavy traffic. Keep Nelson in mind when you have a need. ... Seems like Antonio Brown(notes) and Mike Wallace(notes) should have each received credit for half-a-target on this play. That's the way I woulda scored it. ... Malcom Floyd(notes) isn't expected to return for the Thursday nighter against Oakland, so rookie Vincent Brown(notes) should make another appearance in the box score for San Diego. He's coming off a six-target, four-catch, 79-yard game.
Bears receiver Earl Bennett(notes) didn't quite make the target leader board in his return from injury, but he clearly had a massive impact on the game's outcome. That guy is worth an add in many leagues, though perhaps not in your standard 10-teamer. (I'd make Laurent Robinson(notes) my first WR claim, just for the record, and Bennett my second). Earl finished with five catches on five targets against the Eagles, gaining 95 yards and finding the end zone late in the game. On Bennett's 5-yard TD reception in the fourth quarter, the Bears were actually supposed to run a shovel pass (which everyone always hates, unless it works). But Jay Cutler(notes) vetoed the play, made eye contact with his former college teammate, changed the play telepathically, and it resulted in this go-ahead touchdown — and that, my friends, is rapport between a quarterback and receiver.
And then Earl and Jay made sweet, sweet Twitter love after the game...
That's lovely. Add @EBBennett80 as needed.
Tight Ends
We've spent a fair amount of time discussing Jake Ballard(notes) around here, because he's almost impossible to tackle. It usually takes 6-8 defenders and a tranquilizer gun to bring him down. He's just a fun player to watch. Ballard obviously had the key catch in the Giants' Week 9 win at New England, a 1-yard TD in the final seconds. But his 28-yard grab earlier in the drive was a genuinely difficult catch, above the coverage. In each of Ballard's last five games, he has either 50 receiving yards or a touchdown (or both). He's an asset, only 34 percent owned.
Dallas Clark(notes) reportedly has a "significant" leg injury, and it's likely to sideline him for multiple weeks. Jacob Tamme(notes) presumably takes over, just like last year. Or rather, just like last year, but without a Hall of Fame-level quarterback. ... Jermichael Finley(notes) now has touchdown catches in consecutive weeks, and he's tied to a quarterback who's playing the position like few ever have. Let's hope you didn't make a panic deal, selling-low on a TE with elite skills. You've got nothing to complain about, Finley owner. If his first-half performance is your biggest fantasy gripe, then you're probably doing just fine. ... Brent Celek(notes) had a third straight useful game — no huge surprise, since he was facing a Chicago defense that allows the most fantasy points to tight ends. Brandon Pettigrew(notes) gets the Bears in Week 10, while Celek faces the user-friendly Cardinals. Both will likely rank as starters on my board.
Photo via Getty Images
Posted Dec 29 2013
2013 Fantasy Football Surprises
Posted Dec 29 2013
Fantasy draft outlook for 2014
Posted Dec 29 2013
Edited By Doug Farrar
Edited By 'Duk
Edited By Kelly Dwyer
Edited By Greg Wyshynski
Edited By Matt Hinton
Edited By Jeff Eisenberg
Edited By Jay Busbee
Edited By Jay Busbee
Edited By Steve Cofield
Edited By Chris Chase
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Edited By Brooks Peck
Edited By Andy Behrens
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Edited By Mark J. Miller


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The Ravens released their Wednesday practice report and perhaps the only name that drew a head-tilt was that of defensive tackle Haloti Ngata, who did not participate because of a hip injury.
Defensive tackle Terrence Cody (knee), center Ryan Jensen (foot) and linebacker Albert McClellan (shoulder) also did not practice. Seven other members of the team were limited participants: receivers Marlon Brown (thigh), Jacoby Jones (knee) and Brandon Stokley (thigh), running backs Shaun Draughn (ankle) and Bernard Pierce (thigh), guard Kelechi Osemele (back) and defensive tackle Marcus Spears (knee).
The Packers, on the other hand, have already ruled out four players for Sunday's game.
Leading that group is star linebacker Clay Matthews, who is expected to miss a month with a broken thumb. Green Bay also ruled out linebacker Brad Jones (hamstring), tailback James Starks (knee) and offensive lineman Greg Van Roten (foot).


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"Ryan Kerrigan had his knee scoped yesterday and it went fine," our Mike Jones wrote last Friday. "He's already off crutches."
If you do the math, you'll realize that "yesterday" in this case refers to Jan. 9, which is the day the Redskins hired Jay Gruden as their new head coach, which is why Kerrigan apparently found out about the new hire from his surgeon.
"Actually I was getting ready to go into surgery, and the doctor that was about to perform on me said, 'Oh, you guys hired a new head coach today,' " Kerrigan told Larry Michael on "Redskins Nation." "So I was like, 'Oh, that's pretty cool.' And then when I came out of surgery I saw the introductory press conference. So it was definitely an exciting day, and I look forward to what the future holds with Coach Gruden."
I could have used a few more details on this anecdote, but I'm guessing it won't be the last time Kerrigan recounts it. That day after his surgery, Kerrigan also went on ESPN 980's "Inside the Locker Room," and he was asked about the prospect of moving forward with Jim Haslett still in place as defensive coordinator.
"Haz and I have a great relationship," Kerrigan said. "I know he was one of the main reasons they drafted me here. Haz and I have really developed a close relationship over the past couple years, and he's someone that I really like to play for and someone who I really want to play hard for, so I would be more than happy with him here. … [Unhappy fans] have the right to complain, because we didn't well defensively, but that's a total team thing. That's not just the coaches or just the players; it's both of us. The players, we're the ones out there playing, we're the ones that have to execute the calls regardless of what the calls are. The scheme that Haz teaches us, it's a really good scheme, and when we execute it well it's hard to stop. We've just got to, as players, execute it better."


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Q: I want to buy individual stocks but I'm afraid of losing tons of money. Can I buy insurance?
A: You're wise to want to prevent catastrophic losses. After all, if you sell a stock for a 35% loss, you need to find one that goes up 54% just to get your money back. And there aren't many stocks that go up 54% right away.
Can you buy insurance to protect you from catastrophic stock losses? After all, you can buy catastrophic medical insurance and get insurance to protect your home from fires, high winds and earthquakes. So why not your portfolio?
The answer is yes, but it's a little different than a standard homeowners insurance policy. You don't write checks every month. Insurance for your portfolio is more like a trading or hedging strategy.
There are two approaches that might work, one that's a little less expensive but has risks of its own and another that will cost a bit more.
The first strategy is having a firm policy of cutting losses.
Famous investor, Gerald Loeb, told investors to sell any stock that fell 10% from the price paid. If you follow that rule, most of the time you'll be able to limit your losses. But this requires you to either set up and maintain stop loss orders or monitor the stocks constantly.
There's another danger. Let's say a stock is trading for $10, and you have a sell order in if it goes to $9, which would be a 10% decline. But what if the company discloses its accounting was a total fraud and it's going bankrupt. The shares would probably drop immediately to nearly $0. Let's say $0.10.
In this case your sell order doesn't do you any good. Your sell order converts into a market order when the stock falls below $9. Since the shares never traded hands at $9, and headed down to $0.10, you'd end up selling at $0.10. And even if you had a sell limit order, which lets you define a specific price to sell at, the sell would never have executed. In other words, you'd still own the shares.
That's where hedging and options come in. The other thing you could have done in this case is to buy a put option for the shares you owned. By paying a fee, or premium, you can force another investor to buy your shares at whatever price you set ahead of time.
So let's say in this case you owned a put option on the stock for $9. Even after the shares plummeted to $0.10, you would still have the right to sell the shares to the buyer of the option for $9. You, in effect, bought insurance to cap your loss at 10%.
There are drawbacks with put options. First, they cost money. In addition to the premium you pay, you also have to pay a brokerage fee that's usually higher than a usual market order for a stock.
And like any form of insurance, you're out the premiums you pay if nothing bad happens. The other drawback is that they expire, so you have to keep buying new put options to replace the ones that expire.
But if you're willing to put up with those drawbacks, put options can be a great way for investors to protect themselves.
Matt Krantz is a financial markets reporter at USA TODAY. He answers a different reader question every weekday in his Ask Matt column at money.usatoday.com. To submit a question, e-mail Matt at firstname.lastname@example.com.


text:
Current Research
Knowledge Transfer and Task Modeling for Robot Manipulation Tasks
[Huckaby, et al., "Toward a Knowledge Transfer Framework for Process Abstraction in Manufacturing Robotics", ICML 2013 Workshop]
[Huckaby, et al., "A Taxonomic Framework for Task Modeling and Knowledge Transfer in Manufacturing Robotics", AAAI 2012 Workshop]
SysML for Robotics
The Systems Modeling Language, or SysML, is a relatively recent modeling language that has been developed specifically for modeling the various complex systems encountered in Systems Engineering. This project proposes the use of SysML for the modeling of robotic systems. SysML offers a number of advantages over other modeling options, such as robust tools for model checking, verification, and automatic code generation. This work discusses SysML as an option for the modeling of general robotics systems, and applies it specifically to action and skill modeling of robots in manufacturing environments.
[Huckaby, et al., "A Case for SysML in Robotics", CASE 2014 - In Review]
[Huckaby, et al., "Modeling Robot Assembly Tasks in Manufacturing Using SysML", ISR 2014]
Knowledge Transfer Through Platform Abstraction
In this work we extend research that has already been done using the SysML-based knowledge model for robots in manufacturing environments. While the approach has previously been demonstrated to be able to use this knowledge model to abstract across different assembly tasks using the same manipulator, we now attempt to demonstrate how it can be used without modification to transfer knowledge across different robot platforms. Our demonstrator platform in this case is a Universal Robots UR-5 robot manipulator.
[Huckaby, et al., "Skill Abstraction in Robot Manufacturing Tasks", ISER 2014 - In Review]
Planning Using a Task Modeling Framework
In this project we present the idea that by using AI planning in concert with formal task modeling, the overhead associated with plan creation for complex tasks can be reduced. The proposed approach uses a SysML taxonomy to model the system capabilities and the process specification, and the PDDL planning language to determine acceptable objective solutions. This idea is applied to the manufacturing domain, and examples are shown modeling a multi-robot system in an automobile manufacturing environment.
[Huckaby, et al., "Planning with a Task Modeling Framework in Manufacturing Robotics", IROS 2013]
Past Projects
Robotics for Biological Sampling
Currently most work involving the taking and analyzing of biological samples in the biomedical field is done manually. This can be time consuming, and makes some tasks quite difficult. In this project, we proposed and implemented a robotic solution to this problem. By using an RGBD camera and point cloud visualization tools, we were able to identify specific points on the sample, and have the robot autonomously take the samples at those points, and analyze the sample using a mass spectrometer. This method allowed us to generate a topographical map of the sample, which could be useful for a number of different applications (e.g. better targeted cancer treatment.)
[Bennet, et al., "Robotic Plasma Probe Ionization Mass Spectrometry (ROPPI-MS) of Non-Planar Surfaces", The Analyst 2014]
Mobile Manipulation Using a Low-DOF Manipulator
This work explored the use of a low degree-of-freedom manipulator as a viable and cost-effective solution to the mobile manipulation problem in a home setting. The project utilized the Jeeves mobile manipulation platform. Through experimental results we have shown that with a single degree of freedom manipulator and the control scheme proposed and implemented on the Jeeves platform, we can achieve reliable and repeatable results for a number of varied household object types. Perception was performed using a 3D laser scanner to create a point cloud, from which table top objects could be segmented. Ten objects were selected to serve as the representative categories of general objects found in domestic environments.
[Huckaby, et al., "Mobile Manipulation in Domestic Environments Using a Low-DOF Manipulator", Technical Report]
Networked Multi-Agent Control
This class project focused on the control of a simulated team of robots in a search and rescue operation. Controllers were designed to accommodate waypoint navigation of a team of up to twelve robots, team movement given limited communication distance and line of sight, navigation among complex and varied obstacles, ground coverage for search operations, split and merge operations, and precise formation achievement. In a competition measuring speed and accuracy, the controllers I designed for this project placed second in a class of 30+ students.
GT@Home - RoboCup@Home Team
The Cognitive Robotics Lab at Georgia Tech participated in the 2010 Robocup@Home competition in Singapore. The team, GT@Home, consisted of members from the lab, and the mobile manipulation platform 'Jeeves'. Work was done to demonstrate navigation, SLAM in domestic environments, semantic scene understanding, object segmentation and recognition, person following, verbal human interaction, and mobile manipulation.
Dynamic Characterization of Light-Weight Robots
High precision tasks are an important part of the manufacturing industry. With the recent availability of smaller, light-weight robot manipulators, the question arises of whether it would be feasible to use them in tasks that would otherwise be unsuitable for the standard large industrial robots. For this to be possible, the light weight robot would need to be stiff enough to be able to meet safety constraints. To that end, this study used experimental modal analysis to determine and analyze the dynamic characteristics of small, light-weight manipulators to determine whether it would be possible for them to be used in manufacturing tasks. Two light-weight robot manipulators were considered in the study: the KUKA Light-Weight Robot (LWR) and the KUKA KR5-sixx.
[Huckaby, et al., "Dynamic Characterization of KUKA Light-Weight Manipulators", Technical Report]
Georgia Tech PSA Design
Semantic Perception for Mobile Manipulation
Personal service robots will need to understand semantic object relationships and task context in order to assist humans in their everyday lives. This project demonstrated a technique using keywords, spatial relationships, colors, and other contextual information to assist in the mobile manipulation and object recognition tasks. Preliminary results using a mobile manipulation platform were also presented.
[Choi, et al., "Toward Semantic Perception for Mobile Manipulation", IROS 2009 Workshop]
Simulation for Mobile Manipulation
This class project utilized the srLib rigid multi-body dynamics simulation package to develop two main components: a dynamically balancing mobile platform, and a seven link manipulator. To demonstrate this project, we simulated a mobile manipulator comprised of our dynamically balancing platform with our seven link arm. We used this platform for a fetch and carry task, and were able to successfully move the platform, grasp an object, and deliver the object to a secondary location.
National Robotics Roadmap
I spent the summer of 2008 helping Dr. Henrik Christensen administer a series of workshops designed to formulate a long-term research roadmap for robotics in the United States. Each workshop brought together leaders from both academia and industry in targeted areas (manufacturing, healthcare, service, emerging technologies) to discuss business drivers, missing competencies, and research directions for 5, 10, and 15 year time frames. The resulting roadmap was consequently presented to Congress in 2009, and was one motivation for the National Robotics Initiative. The roadmap was recently updated (2013), and presented again to Congress in October 2013.
DARPA Urban Challenge
I spent one year working on the BYU team competing in the DARPA Urban Challenge, a competition aimed at enabling a car to drive in an urban environment autonomously. I served as the undergraduate group leader for the team controls group, and was responsible for leading a group of students in designing and implementing vehicle drive and correction control systems.


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text:
Don't Judge a Book By It's Cover.
The phrase goes back to at least the mid-19th century, as seen in the newspaper Piqua Democrat, June 1867:
"Don't judge a book by its cover, see a man by his cloth, as there is often a good deal of solid worth and superior skill underneath a jacket and yaller pants."
How to use it in a sentence.
Joe: Did you see the preppy kid Zach last night at the party? He was so drunk. I thought he would be home studying for finals.
James: I know I guess you can't judge a book by it's cover.
Don't judge a book by it's cover means don't prejudge someone or something just by their outside appearance.
Hanna Bowman
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Don't Judge a Book By It's Cover.
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Hanna Bowmanon 2 May 2014
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