The Glue of Genomics: Will Science’s Unsung Data Heroes Abandon Academia?
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Advances in the last twenty years of genomics have turbocharged our ability to decode DNA and other nucleic acids. At the turn of the century, the Human Genome Project completed a 13-year journey to produce the first complete human genome at a great financial cost.
In 2023, genome sequencing is a routine process that has expanded our understanding of biology and fueled a similar enhancement in other -omics disciplines.
These advances owe much to increased raw sequencing power and ever-more ingenious techniques for sampling genomic data, be it from millions of individuals in genomic-wide association sequencing (GWAS) studies or from the heart of a lone cell in single-nucleus RNA sequencing assays.
But all these advances would be for naught if we weren’t able to process and analyze the data torrent that pours from these sequencing projects. Genomic data analysis pipelines have had to progress as well to support this growing field.
A flexible field
Dr. Alison Meynert is a senior research fellow and the bioinformatics analysis core manager at the MRC Institute of Genetics and Cancer (IGC) at the University of Edinburgh. Meynert and her six-person team process data from researcher–clinicians across the University. Meynert’s own background is in computer science and software development, but she has now spent two decades in bioinformatics – basically, she says, “from its infancy.”
While wet-lab researchers in individual niches might be able to laser-focus their projects, Meynert’s team has to stay flexible. “We've had some years where there have been big, nationally funded, whole-genome sequencing projects going on where we'll have hundreds of samples coming in over the course of the year across different cohorts for different research projects,” she says. Now, novel techniques like nanopore and single-cell sequencing have to be considered as well. That requires a deep and broad knowledge base. “Different sequencing machines have totally different error profiles and output formats,” Meynert explains.
One thing that connects these different data sources is that, for Meynert and her team, the goal remains to take complex, raw genomic data and turn it into a form from which the researchers that produced it can extract relevant insights. “It’s a very collaborative process,” says Meynert.
What makes bioinformatics so collaborative?
That collaboration is not just with the wet-lab researchers who come to the core team for help, but with other core labs across the UK, Europe and even globally. Meynert and her team are part of a community of bioinformaticians called nf-core. This project began at the National Genomics Infrastructure in Stockholm, Sweden, which created a set of standards for data analysis pipelines. The project is sponsored by the Chan Zuckerberg Initiative and runs on cloud credits provided by Amazon Web Services and Microsoft Azure, but the team is largely volunteers.
The IGC team is hosting nf-core’s next hackathon, Meynert tells me. “We basically get the best of everybody’s contributions across the bioinformatics community. To develop these pipelines, we have lots of arguments about how to do things. Sometimes you come up with multiple ways of doing things.”
These collaborative events are commonplace across the bioinformatics world. But this underlying ethos of sharing and collaboration, while heartening, stands in stark contrast to processes in biological and biomedical science. Projects like the hugely influential FAIR Guiding Principles, which aimed to exploit the massive increase in digital data to make information in science more findable, accessible, interoperable and re-usable, have increased the amount of lip service paid to data sharing. But a recent study in the Journal of Clinical Investigation skewered the idea that science has significantly embraced open-access principles. This paper trawled through 3,556 articles from over 300 open-access journals that were all published in January 2019.
Just half of these studies indicated that authors were willing to share their data, and of these, a jaw-dropping 93% of authors either did not respond to or declined requests for data access. Is the more relaxed attitude to data sharing in bioinformatics circles a sign that staff in these areas are more magnanimous and beneficent people? Meynert says that the reasons are likely to be more practical. “The bioinformatics community has always been very strongly based around open-source software, I think in large part because if we want to develop a new tool, we're going to need data to test it on. We need someone to have shared their data for us to do that.”
Researchers in genomics and other biological disciplines fear being “scooped” by rival scientists almost as much as they fear a deafening silence at the end of their symposium talk. The resulting culture of secrecy and security around data has proved difficult to rectify. But bioinformatics’ open-source approach has racked up numerous success stories that benefit genomics data analysis researchers like Meynert every day. She points to file formats like binary alignment map (BAM), a compressed version of the text-based sequence alignment map (SAM) format. SAM/BAM (and CRAM, a reference-aligned and compressed version) are some of the most used formats in the genomics field. But they originated from individual research groups becoming frustrated with existing formats and devising changes. Initiatives like the Global Alliance for Genomics and Healthcare (GA4GH) have helped these formats become standardized, enabling them to be widely adopted in the field. Massive data repositories like GitHub make it easy for these innovations to be shared and used by other research groups. GA4GH’s Genome Analysis Toolkit, developed in close collaboration with the Broad Institute, itself a collaboration between MIT and Harvard, is one of the “workhorse tools of genomics,” says Meynert.
The unsung heroes of genomics
The efforts that have gone into creating these resources have arguably advanced our ability to understand the genome as much as technical advances in gene sequencing technology. But the researchers that make these innovations still work in academic circles, where the standard for receiving grants and recognition remains calculated in terms of publication and citations. How can these scientists receive the recognition they deserve for their efforts? It’s a conundrum that has motivated Neil Chue Hong, the founding director and principal investigator of the Software Sustainability Institute (SSI), a project that works with all seven of the UK’s major research councils to improve the practice of using software in science.
Chue Hong noted that one obvious barrier stopping academics who create software and analysis tools from being recognized was the lack of a formal place in the scientific register. At an SSI workshop in 2012, a group coined the term research software engineer (RSE) to codify this position. “It’s a role that has always been present in research for the last maybe four or five decades,” says Chue Hong. “Because there's always been this idea of the researcher – the one who's good at coding – that you ask, ‘How do I fix this piece of software that's not working?’ I think what was causing problems was that there wasn't enough recognition for this role, and as software use became more and more prevalent, that role became more and more important.”
But the increasing influence of RSEs in academia has not been reflected in the amount of recognition they receive. The supremacy of “the final publication” is part of the issue. This gives an undue amount of credit to a mythical figure that Chue Hong calls the “lone hero principal investigator” who is, in theory, meant to be given responsibility for the bulk of work that goes into a scientific paper. In reality, research is a collaborative practice, and a funding system that recognizes the contribution of each lab member to a final publication is an important first step towards realizing this, says Chue Hong: “[Funding bodies] are moving towards recognizing things like narrative CVs and researcher resumes that show it’s not just about the publications, but about the way you disseminate knowledge and the way you pass on skills to other people,” he adds.
There’s progress being made elsewhere toward recognizing the contribution of RSE to genomics and other fields. The American National Standards Institute (ANSI) and National Information Standards Organization (NISO) jointly announced the publication of its CRediT Contributor Roles Taxonomy last year, a framework that divides the practice of science into 14 contributor roles, including conceptualization, funding acquisition, investigation and software. In 2022, the American Chemical Society announced a pilot of CRediT taxonomy in its journals. Chue Hong says that he believes the main sticking point in this area lies in the peer review process. “The last remaining barrier is getting peer reviewers to understand that science is done very differently in 2023 than 20 years ago,” he suggests. This might involve challenging principles that are embedded within researchers’ psyches. “Survivorship bias is unhelpful. Just because someone was successful, by fighting through a particular way of doing things in academic research, doesn't mean that everyone else has to fight that same fight,” says Chue Hong.
Genomics without glue
The battle to get recognition for RSEs is one that should concern all of science. Much of genomics’ analysis toolkit wouldn’t exist without them, and one doesn’t have to look too far ahead to see what might happen if RSEs continue to be shut out of a recognized place in academia. “Increasingly, what people will do is quit academia,” says Chue Hong. You used to have a choice between an academic role and an industry role in research. The difference was that academia was meant to give you more job security, a better pension and a more fruitful working environment, where you've got lots and lots of really interesting people to collaborate with. The tradeoff was salary.
“Now, industry offers you possibly better working conditions, probably a better pension, definitely better people to work with and a better salary. You have to really want to work in academia now, if you're in a software role. The challenge that I think the RSE sector faces is to encourage people to stay in academic research environments and not go into industrial research environments.”
Those struggles will be familiar to many within academia. But RSEs play a vital role at the intersection between an increasingly digital data-rich informatics environment and the wet-lab work that drives biology. Meynert says that her day job will often involve taking tools created by other research groups and fitting them to the task at hand, applying mortar to make different analysis tools work in tandem. “An awful lot of informatics,” she says, “is gluing things together.” If academic research begins to lose RSEs and their contribution, it might start to see how much is reliant on that glue’s grip.
Glue ya Genomics: Je, Mashujaa wa Sayansi Wasiojulikana wa Takwimu Wataacha Chuo Kikuu? Jaza fomu hapa chini ili kufungua upatikanaji wa makala zote za sauti. Maendeleo ya miaka 20 iliyopita katika genomics yameongeza uwezo wetu wa kufuta DNA na asidi nyingine za nyuklia. Katika karne ya 20, mradi wa Human Genome Project ulimaliza safari ya miaka 13 ya kutengeneza genome ya kwanza ya binadamu kwa gharama kubwa. Katika 2023, mfuatano wa genome ni mchakato wa kawaida ambao umeongeza uelewa wetu wa biolojia na kuchochea uboreshaji sawa katika taaluma zingine za omics. Maendeleo haya yanahitajika kwa nguvu ya mfuatano wa mbichi na mbinu za ubunifu zaidi za sampuli za data ya genomic, iwe ni kutoka kwa mamilioni ya watu katika masomo ya mfuatano wa ushirika wa genomic (GWAS) au kutoka kwa moyo wa seli moja katika vipimo vya mfuatano wa RNA ya single-nucleus. Lakini maendeleo haya yote yangekuwa bure ikiwa hatungeweza kusindika na kuchambua mtiririko wa data ambayo hutiririka kutoka kwa miradi hii ya mpangilio. Mabomba ya uchambuzi wa data ya genomic yamelazimika kusonga mbele pia ili kuunga mkono uwanja huu unaokua. Alison Meynert ni mwanachama mwandamizi wa utafiti na meneja wa msingi wa uchambuzi wa bioinformatics katika Taasisi ya MRC ya Genetics na Saratani (IGC) katika Chuo Kikuu cha Edinburgh. Meynert na timu yake ya watu sita mchakato data kutoka mtafiti-kliniki katika Chuo Kikuu. "Mwanamke huyo wa kwanza, ambaye ana uzoefu wa miaka 20 katika sayansi ya kompyuta na maendeleo ya programu, alisema: ""Wakati watafiti wa maabara ya mvua katika niches za mtu binafsi wanaweza kuzingatia miradi yao, timu ya Meynert inapaswa kubaki rahisi." "Tumekuwa na miaka kadhaa ambapo kumekuwa na miradi mikubwa ya utaratibu wa genome nzima inayofadhiliwa kitaifa, ambapo tutakuwa na mamia ya sampuli zinazoingia kwa mwaka mzima katika vikundi tofauti kwa miradi tofauti ya utafiti, ""alisema." Sasa, mbinu mpya kama vile nanopore na utaratibu wa chembe moja lazima uzingatiwe pia. Hilo huhitaji msingi wa maarifa wenye kina na mpana. <unk>Mashine tofauti za utaratibu zina maelezo tofauti kabisa ya makosa na muundo wa pato,<unk> Meynert anaelezea. "Kama kitu ambacho huunganisha vyanzo hivi tofauti vya data ni kwamba, kwa Meynert na timu yake, lengo bado ni kuchukua data ngumu, mbichi ya genomic na kuigeuza kuwa fomu ambayo watafiti ambao waliizalisha wanaweza kutoa ufahamu muhimu. """ "Ni mchakato wa ushirikiano sana", anasema Meynert. Ni nini kinachofanya bioinformatics iwe ya ushirikiano sana? Ushirikiano huo sio tu na watafiti wa maabara ya mvua ambao wanakuja kwa timu ya msingi kwa msaada, lakini na maabara nyingine za msingi kote Uingereza, Ulaya na hata ulimwenguni. Meynert na timu yake ni sehemu ya jamii ya bioinformaticians aitwaye nf-msingi. Mradi huu ulianza katika National Genomics Infrastructure huko Stockholm, Sweden, ambayo iliunda seti ya viwango vya mirija ya uchambuzi wa data. Mradi huo unafadhiliwa na Chan Zuckerberg Initiative na unaendeshwa na mikopo ya wingu iliyotolewa na Amazon Web Services na Microsoft Azure, lakini timu hiyo ni wajitoleaji. Timu ya IGC inakaribisha nf-core's next hackathon, Meynert ananiambia. <unk>Kimsingi tunapata bora ya michango ya kila mtu katika jamii ya bioinformatics. Kwa kuwa tunahitaji kuendeleza mirija hii, tunahitaji kujadili na kujadili jinsi ya kufanya hivyo. Wakati mwingine wewe kuja na njia nyingi za kufanya mambo. <unk> Hizi matukio ya ushirikiano ni ya kawaida katika ulimwengu wa bioinformatics. Lakini maadili haya ya msingi ya kushiriki na ushirikiano, wakati wa kufurahisha, inasimama kinyume kabisa na michakato katika sayansi ya kibiolojia na biomedical. "Mipango kama vile ""Fairies"" yenye ushawishi mkubwa, ambayo ilikusudiwa kutumia ongezeko kubwa la data ya dijiti ili kufanya habari katika sayansi iweze kupatikana zaidi, kupatikana, kuingiliana na kutumika tena, imeongeza kiasi cha huduma ya mdomo iliyolipwa kwa kushiriki data." Utafiti uliochapishwa katika jarida la Journal of Clinical Investigation ulionyesha kuwa utafiti wa kisayansi umekuwa na athari kubwa kwa jamii. Makala hii ilichunguza makala 3,556 kutoka kwa jarida zaidi ya 300 la upatikanaji wazi ambazo zote zilichapishwa mnamo Januari 2019. Nusu tu ya masomo haya yalionyesha kwamba waandishi walikuwa tayari kushiriki data zao, na kati ya hizo, 93% ya waandishi hawakujibu au kukataa maombi ya upatikanaji wa data. Je, mtazamo wa kupumzika zaidi kwa kushiriki data katika duru za bioinformatics ni ishara kwamba wafanyakazi katika maeneo haya ni watu wenye ukarimu zaidi na wenye fadhili? Meynert anasema kwamba huenda sababu hizo zikawa za vitendo zaidi. "Kama vile bioinformatics imekuwa na ""mfumo wa wazi"" kwa muda mrefu, kwa sababu ni muhimu sana kwa ajili ya maendeleo ya teknolojia ya kisasa, ambayo inahitaji data ya kutosha." Tunahitaji mtu ambaye ameshiriki data zao kwa ajili yetu kufanya hivyo. <unk> Watafiti katika genomics na taaluma nyingine za kibiolojia wanaogopa kuwa <unk>scooped<unk> na wanasayansi wapinzani karibu kama wao hofu ya ukimya deafening mwishoni mwa hotuba yao symposium. Utamaduni unaotokana na siri na usalama kuzunguka data umethibitika kuwa mgumu kurekebisha. Lakini mbinu ya bioinformatics ya chanzo wazi imekusanya hadithi nyingi za mafanikio ambazo zinafaidi watafiti wa uchambuzi wa data ya genomics kama Meynert kila siku. "Kama ilivyoelezwa hapo juu, ""Binary Alignment Map"" (BAM) ni toleo la maandishi ya maandishi ya maandishi ya maandishi ya maandishi ya maandishi ya maandishi." SAMB (na CRAM, toleo la kumbukumbu-aligned na compressed) ni baadhi ya muundo kutumika zaidi katika uwanja wa genomics. Lakini zilitokana na vikundi vya utafiti vya mtu binafsi kuwa na kukatishwa tamaa na miundo iliyopo na kubuni mabadiliko. Mipango kama vile Global Alliance for Genomics and Healthcare (GA4GH) imesaidia muundo huu kuwa wa kawaida, ikiwezesha kupitishwa kwa wingi katika uwanja. Hifadhi kubwa za data kama GitHub hufanya iwe rahisi kwa uvumbuzi huu kushiriki na kutumiwa na vikundi vingine vya utafiti. GA4GH's Genome Analysis Toolkit, iliyoundwa kwa kushirikiana kwa karibu na Taasisi ya Broad, yenyewe ni ushirikiano kati ya MIT na Harvard, ni moja ya "vifaa vya kazi ya genomics", anasema Meynert. "Wanafunzi wa genomics wanaamini kuwa ""jitihada za kuunda rasilimali hizi zimesababisha uwezo wetu wa kuelewa genome kama maendeleo ya kiufundi katika teknolojia ya utaratibu wa jeni.""" Lakini watafiti ambao hufanya uvumbuzi huu bado wanafanya kazi katika duru za kitaaluma, ambapo kiwango cha kupokea ruzuku na utambuzi bado kinahesabiwa katika suala la uchapishaji na nukuu. Wanasayansi hao wanawezaje kupata utambuzi wanaostahili kwa ajili ya jitihada zao? Neil Chu Hong, Mkurugenzi wa Usimamizi wa Programu ya Usimamizi wa Programu (SSI), ni mtafiti wa programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu ya programu. Chu Hong alisema kwamba kikwazo kimoja cha wazi cha kuzuia wasomi wanaotengeneza programu na zana za uchambuzi kutambuliwa ni ukosefu wa nafasi rasmi katika orodha ya kisayansi. Katika warsha ya SSI mnamo 2012, kikundi kiliunda neno mhandisi wa programu ya utafiti (RSE) kuorodhesha nafasi hii. "Ni jukumu ambalo limekuwapo daima katika utafiti kwa miongo minne au mitano iliyopita", anasema Chue Hong. "Kwa sababu daima imekuwa na wazo hili la mtafiti - yule ambaye ni mzuri katika coding - kwamba unauliza, ""Ninawezaje kurekebisha kipande hiki cha programu ambayo haifanyi kazi?"" Nadhani kile kilichokuwa kikisababisha matatizo ilikuwa kwamba hakukuwa na utambuzi wa kutosha kwa jukumu hili, na kama matumizi ya programu yakawa zaidi na zaidi, jukumu hilo likawa muhimu zaidi na zaidi." Utukufu wa "chapisho la mwisho" ni sehemu ya suala hilo. "Hii inatoa kiasi kisichofaa cha sifa kwa takwimu ya hadithi ambayo Chu Hong anaita ""mchunguzi mkuu wa shujaa wa pekee"" ambaye, kwa nadharia, anamaanisha kupewa jukumu la sehemu kubwa ya kazi ambayo inakwenda kwenye karatasi ya kisayansi." "Kwa kweli, utafiti ni mazoezi ya ushirikiano, na mfumo wa ufadhili ambao unatambua mchango wa kila mwanachama wa maabara kwa uchapishaji wa mwisho ni hatua muhimu ya kwanza kuelekea kutambua hii, ""Chue Hong anasema: ""Mifumo ya ufadhili inaelekea kutambua mambo kama vile CV za hadithi na resume za mtafiti ambazo zinaonyesha sio tu juu ya machapisho, lakini juu ya jinsi unavyosambaza maarifa na jinsi unavyopitisha ujuzi kwa watu wengine.""" Kuna maendeleo yanayofanywa mahali pengine kuelekea kutambua mchango wa RSE kwa genomics na nyanja nyingine. Taasisi ya Taifa ya Viwango ya Amerika (ANSI) na Shirika la Taifa la Viwango vya Habari (NISO) kwa pamoja walitangaza kuchapishwa kwa Taxonomy ya Majukumu ya Mchangiaji wa CREDIT mwaka jana, mfumo ambao unagawanya mazoezi ya sayansi katika majukumu 14 ya mchangiaji, pamoja na dhana, upatikanaji wa fedha, uchunguzi na programu. Mwaka 2022, Shirika la Kemikali la Marekani lilitangaza majaribio ya taxonomy ya CREDIT katika majarida yake. Chue Hong anasema kwamba anaamini kwamba hatua kuu ya kushikamana katika eneo hili iko katika mchakato wa ukaguzi wa wenzao. "Kizuizi cha mwisho kinachobaki ni kuwafanya wahakiki wa wenzao kuelewa kwamba sayansi inafanywa tofauti sana mnamo 2023 kuliko miaka 20 iliyopita,"" anaonyesha." Hii inaweza kuhusisha kanuni changamoto kwamba ni embedded ndani ya watafiti 'psyches. <unk>Upendeleo wa kuokoka hausaidii. """Kwa sababu tu mtu alifanikiwa, kwa kupigana kupitia njia fulani ya kufanya mambo katika utafiti wa kitaaluma, haimaanishi kwamba kila mtu mwingine anapaswa kupigana vita sawa,"" anasema Chu Hong." Genome bila gundi: vita vya kupata utambuzi wa RSE ni moja ambayo inapaswa kuhusisha sayansi yote. """Mengi ya chombo cha uchambuzi wa genomics haingekuwepo bila wao, na mtu haipaswi kuangalia mbali sana mbele ili kuona nini kinaweza kutokea ikiwa RSE zinaendelea kufungwa nje ya nafasi inayotambuliwa katika chuo kikuu." "Kuna watu wengi zaidi ambao huacha masomo ya chuo kikuu", anasema Chue Hong. Wewe kutumika kuwa na uchaguzi kati ya jukumu kitaaluma na jukumu sekta katika utafiti. Tofauti ilikuwa kwamba chuo kikuu kilikuwa na lengo la kukupa usalama zaidi wa kazi, pensheni bora na mazingira ya kazi yenye kuzaa matunda zaidi, ambapo una watu wengi wa kuvutia kushirikiana nao. Ubadilishaji huo ulikuwa mshahara. "Sasa, sekta inakupa uwezekano wa hali bora za kazi, pengine pensheni bora, dhahiri watu bora kufanya kazi na mshahara bora. """ Unapaswa kuwa na nia ya kufanya kazi katika chuo kikuu sasa, kama wewe ni katika jukumu la programu. """Nafikiri changamoto ambayo sekta ya RSE inakabiliwa nayo ni kuhamasisha watu kubaki katika mazingira ya utafiti wa kitaaluma na sio kwenda katika mazingira ya utafiti wa viwanda, ""aliongeza." Lakini RSE zina jukumu muhimu katika makutano kati ya mazingira ya habari ya dijiti yenye data nyingi na kazi ya maabara ya mvua ambayo huendesha biolojia. Meynert anasema kazi yake ya siku mara nyingi inahusisha kuchukua zana zilizotengenezwa na vikundi vingine vya utafiti na kuziweka kwa kazi iliyopo, kutumia mortar kufanya zana tofauti za uchambuzi zifanye kazi kwa pamoja. "Mengi ya informatics, "anasema, "ni gluing mambo pamoja. "Kama utafiti wa kitaaluma huanza kupoteza RSEs na mchango wao, inaweza kuanza kuona jinsi mengi ni kutegemea kwamba gundi 'kushikilia.