COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.National Institute of General Medical Sciences (U.S.) (Go grant 1RC2GM092602-01

B. P. Anton

Brenner

C. Delisi

D. Segre

D. Vitkup

Fleischmann

G. Housman

Galperin

Green

H.-P. Choi

Heurgue-Hamard

Hsiao

J. Guleria

J. N. Rachlin

K. R. Tao

L. L. Faller

L. Osmani

M. G. McGettrick

M. Steffen

N. Klitgord

R. J. Roberts

R. M. Pokrzywa

R. Swaminathan

Roberts

S. Kasif

S. L. Salzberg

S. Letovsky

V. Mazumdar

Y.-C. Chang

Z. Hu

Nucleic Acids Research

PubMed

Roberts, Richard J.

Chang, Yi-Chien

Hu, Zhenjun

Rachlin, John N.

Anton, Brian P.

Pokrzywa, Revonda M.

Choi, Han-Pil

Faller, Lina L.

Guleria, Jyotsna

Housman, Genevieve

Klitgord, Niels

Mazumdar, Varun

McGettrick, Mark G.

Osmani, Lais

Swaminathan, Rajeswari

Tao, Kevin R.

Letovsky, Stan

Vitkup, Dennis

Segrè, Daniel

Salzber, Steven L.

Delisi, Charles

Steffen, Martin

Kasif, Simon

DSpace@MIT

COMBREX: a project to accelerate the functionalannotation of prokaryotic genomesRichard J. Roberts1,2,*, Yi-Chien Chang1, Zhenjun Hu1, John N. Rachlin3,Brian P. Anton1,2, Revonda M. Pokrzywa4, Han-Pil Choi5, Lina L. Faller1,Jyotsna Guleria4, Genevieve Housman4, Niels Klitgord1, Varun Mazumdar1,Mark G. McGettrick3, Lais Osmani4, Rajeswari Swaminathan4, Kevin R. Tao4,Stan Letovsky1, Dennis Vitkup6,7, Daniel Segre`1,4,8, Steven L. Salzberg9,Charles Delisi1,4, Martin Steffen1,4,5,* and Simon Kasif1,4,10,*1Bioinformatics Program, Boston University, Boston, MA 02215, 2New England Biolabs, 240 County Road,Ipswich, MA 01938, 3Diatom Software LLC, 260 Winter St., Holliston, MA 01746, 4Department of BiomedicalEngineering, Boston University, Boston, MA 02215, 5Department of Pathology and Laboratory Medicine,Boston University School of Medicine, Boston University, Boston, MA 021184, 6Department of BiomedicalInformatics, Columbia University, New York, NY 10032, 7Center for Computational Biology and Bioinformatics,Columbia University, New York, 10032, 8Department of Biology, Boston University, Boston MA 02215,9Department of Computer Science, University of Maryland, College Park, MD, 20742 and 10Children’sHospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology (CHIP@HST),Cambridge, MA 02139, USAReceived October 15, 2010; Revised October 31, 2010; Accepted November 1, 2010ABSTRACTCOMBREX (http://combrex.bu.edu) is a project toincrease the speed of the functional annotation ofnew bacterial and archaeal genomes. It consists of adatabase of functional predictions produced bycomputational biologists and a mechanism for ex-perimental biochemists to bid for the validation ofthose predictions. Small grants are available tosupport successful bids.INTRODUCTIONIn the last 15 years, since the determination of the completesequence of the Haemophilus inﬂuenzae strain Rd genome(1), there has been a rapid increase in the number of pro-karyotic genomes that are being sequenced each year. Withthe cost of DNA sequencing continuing to drop, this hasled to an explosion in the number of genes that are pre-dicted computationally, but for which no solid functionalannotation can be provided (2). This is illustrated in Table1, which shows that in a selection of genomes, at best,maybe 70% of the genes have either known, experimental-ly validated functions or can be assigned function compu-tationally on the basis of sequence similarity, but oftenwith varying or unknown degrees of conﬁdence. Witheach new genome typically containing anywhere from500 to 1000 new genes of unknown function, we face thedaunting challenge of determining those functions so thatthe annotation of new genome sequences can be carried outcomputationally with just a few key functions being testedexperimentally. This means that our ability to predictfunction computationally will need to be quite accurateand must include all genes.Currently, the quality of computational predictions offunction is far from perfect. Indeed, for many of the genesin GenBank the present annotations are either incorrector so general as to be of little value to the user (3–6).The reason for this is that by far the most commonway of making predictions is by checking each newly pre-dicted gene for its similarity to genes annotated in theINSDC (International Nucleotide Sequence DatabaseConsortium) databases (7–9). When a new gene shareshigh sequence similarity to an annotated gene then it isassigned the same function as that presumed known gene.If they are identical or nearly so, then this method is quite*To whom correspondence should be addressed. Tel: +1 617 358 1845; Fax: +1 617 353 6766; Email: kasif@bu.eduCorrespondence may also be addressed to Richard J. Roberts. Tel: +1 978 380 7405; Fax: +1 978 380 7406; Email: roberts@neb.comCorrespondence may also be addressed to Martin Steffen. Tel: +1 617 826-9486/7935; Fax: +1 617 414 7073; Email: steffen@bu.eduPublished online 20 November 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D11–D14doi:10.1093/nar/gkq1168 The Author(s) 2010. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.reliable. However, when the degree of sequence similarityis poor, or perhaps even when it is reasonably high withonly a few key amino acids difference, this can lead toproblems, because one can be less sure that the newgene really is an ortholog of the known gene. Perhaps,the new gene encodes a protein with a function that issimilar to the known one, but different in some subtleway such that its substrate preference has changed.Unless the sequence differences can be interpretedproperly so that the new protein’s function is notdeclared to be identical to that of the old gene product,then a mis-annotation ensues and will likely be propagated(3–6).A number of now classic examples of such mis-annotations have been noted in the literature and onlywhen biochemical experiments were carried out, couldthe annotation be corrected. One classic example wasthe family of genes labeled hemK. These genes had beenannotated as either a protoporphyrinogen oxidase or aDNA methyltransferase. It later turned out that thehemK gene in Escherichia coli actually encoded a proteinmethyltransferase, a ﬁnding of some considerable interestbecause the hemK gene is widely conserved from humansall the way to bacteria (10,11), although further testingon remote homolog’s would still be appropriate. Thisemphasizes the need for biochemical characterization ofgene products whenever possible, but certainly whenthe sequence distance to a known gene product is insufﬁ-ciently high to be certain of the assignment. The degree ofcaution necessary often varies, depending on the level ofconservation or its location. In some cases, one or a fewamino acid changes in a region of a protein responsible forsubstrate recognition can completely alter its function.Unfortunately, we often do not know in which regionsof a protein we should look for such changes and thecomputer blindly labels the new gene incorrectly.THE COMBREX DATABASEOnce a particular gene’s function is characterized bio-chemically, then that function can be propagated withsome degree of certainty to the likely orthologous genesin other organisms, although remote homolog’s willrequire experimental validation. It is precisely this com-bination of computational prediction and biochemical val-idation of function that the COMBREX (COMputationalBRidges to EXperiments) project, recently funded byNIGMS, is all about. At the heart of COMBREX is adatabase (http://combrex.bu.edu) of computational pre-dictions of gene function. This database, which is current-ly under construction, contains all of the annotated genespresent in the bacterial and archaeal genomes section ofthe NCBI’s Protein RefSeq Database (12,13). We takeadvantage of the Clusters Database (14) in which thesegenes have been sorted into families of similar sequencesto organize the genes for predictive purposes. However, inaddition to the annotations contained in the ClustersDatabase, which are themselves of course predictions forthe most part, the COMBREX database also containsfunctional predictions made by other groups of computa-tional biologists. A major goal is to provide reliabilityestimates for those predictions. We already have someongoing collaborations and it is our hope that we caninvolve many others in the bioinformatics communitywho are making gene predictions and who are willing toshare them through the medium of the COMBREXdatabase where they will be publicly and freely available.The reason for doing so is that we are recruiting biochem-ists to test those predictions experimentally.The involvement of the biochemists is the otherleg of this project. We are inviting them to browse theCOMBREX database and identify predictions thatmatch their own laboratory’s biochemical expertise.They can then make a bid to test the function of anyhigh-value predictions lying within their area of expertise.For instance, if an unknown protein is predicted to hydro-lyze carbohydrates, then we would look for a laboratorythat is expert in such hydrolases that would have a largerange of carbohydrate substrates and suitable assaysto detect hydrolytic activity. The idea is that they wouldmake some of the protein from the gene in questionand run it through their battery of assays. For USlaboratories, we are able to offer small grants throughCOMBREX that are typically in the range of $5000 to$10 000 to support his work, which might be carried outby an individual such as a graduate student, a supervisedrotation student or anyone with sufﬁcient proven expertiseto complete the task. There should be opportunities herefor teaching colleges to participate as well as for some ofthe top universities to incorporate this approach into theirnormal curriculum. The successful bid would guaranteesix months of sole access to that gene product throughCOMBREX and at the end of that time, a report wouldbe written and would be available on the COMBREXwebsite describing the experiments that were performedand the results, either a positive validation or a failureto detect the predicted activity. We would encourage thelaboratory to publish these results in the peer reviewedliterature, and they would be featured on theCOMBREX web site. They would also be propagated tothe appropriate databases of the INSDC.A GOLD STANDARD DATABASE OF PROTEINSOne important problem that has been identiﬁed during theinitial stage of the COMBREX project, is that theTable 1. Distribution of annotated genes in selected genomesOrganism PublicationdateUnknownfunctiona(%)Known orpredictedfunctionTotalHaemophilus inﬂuenzae Rd 1995 429 (26) 1228 1657Methanocaldococcus jannaschii 1996 616 (35) 1155 1771Helicobacter pylori 26695 1997 552 (35) 1021 1573Escherichia coli MG1655 1997 550 (13) 3594 4144Pseudomonas aeruginosa PA7 2010 2653 (42) 3633 6286aBased on genes annotated as unknown or conserved hypothetical inthe RefSeq ﬁles. These represent lower estimates as the accuracy of thepredicted functions is unknown.D12 Nucleic Acids Research, 2011, Vol. 39, Database issuesuccessful propagation of annotations from one gene toanother, depends critically on knowing when a protein ofknown sequence has an experimentally demonstrated bio-chemical function, because this becomes a standardagainst which similar proteins can have their functionspredicted. It turns out that this information is notalways easily deciphered. For many proteins the biochem-ical characterization was carried out on a puriﬁed proteinfrom a bacterial strain many years before the gene for thatprotein was cloned and sequenced. In this case the clonedgene may well come from a strain that is different from theone in which the original characterization was done andthere may be subtle, but important, differences insequence. Sometimes accurate strain information can befound in the major databases, because both the sequenceand the characterization were described in a single publi-cation. But often the necessary pedigree information is noteasily traced. Recognizing that this is a major problem,not just for COMBREX, but for all groups trying topropagate annotation, we have now undertaken aproject in collaboration with the RefSeq Database atNCBI (12) and the UniProt Knowledge Database atEBI (14) to identify a ‘Gold Standard Set of Proteins’for which the function is known and the exact sequenceof the gene/protein on which the functional tests weredone is known. This gold standard project is currentlybeing managed by COMBREX and a pipeline has beenset up whereby candidate genes/proteins for gold standardstatus are identiﬁed and distributed to individual annota-tors who will check in detail that they do indeed, have goldstandard properties. The ﬁnal gold standard databasewill be maintained and distributed from both NCBI andUniProt.This gold standard project is another community-basedproject in which individuals from around the world canhelp either by identifying potential gold standard genes orby helping in the manual curation. In a similar fashion, wehope that COMBREX will also become a community-based project around the world so that experimental char-acterization of function for COMBREX predictionscan be carried out in laboratories in many countries. Itis worth noting that this approach can be used as ateaching tool for students learning how to do hands-onbiochemistry, while at the same time making a valuablecontribution to biology. There are many smalllaboratories with appropriate biochemical expertise totest certain speciﬁc predictions within the area of theirexpertise and for whom a small amount of money canmake a large difference. Already a number of collabor-ators are helping with this project. We hope that byengaging the larger community of computational biolo-gists and biochemists, we can build momentum in aproject that has the potential to greatly increase theaccuracy of genome annotation. It should enablebioinformaticians to make better predictions by providinga set of reference points in the gold standard set ofproteins and facilitating the biochemical testing of theirpredictions. The feedback from this validation processshould then impact their ability to make more reliablepredictions. The involvement of the experimental commu-nity will highlight the importance of their discipline whilealso providing some funds that might help train the futuregeneration. A successful outcome to the project shouldmean that the number of genes in need of experimentalveriﬁcation will diminish with time, because the computerpredictions can be assessed by rigorously documentingtheir distance to a gold standard protein.The size of the functional annotation problem isenormous and it is essential that it be tackled if we areto keep pace with the genome and metagenome projectsthat are currently underway. It is one where collaborationis demanded and competition would merely serve towaste money. While the original idea was proposed in2004 (15), before the current ‘Wiki’ approaches werepopular, COMBREX can be seen as essentially a similarcommunity-based approach to the problem of functionalannotation of prokaryotic genomes. We anticipate thatif this project is successful, the model will be expandedfrom its initial focus on bacteria and archaea to covergenomes in all kingdoms of life. It will also providesome more of the raw data on function that may oneday permit systems biology to really view an organismas a system. Furthermore, it will also have an impact onmany of the databases in this issue. For a relatively smallinvestment and a massively parallel human effort, itshould be possible to achieve high throughput.FUNDINGGO grant from National Institute of GeneralMedical Sciences (NIGMS) (1RC2GM092602-01 toCOMBREX). The open access publication charge forthis paper has been waived by Oxford University Press -NAR Editorial Board members are entitled to one freepaper per year in recognition of their work on behalf ofthe journal.Conﬂict of interest statement. None declared.REFERENCES1. Fleischmann,R.D., Adams,M.D., White,O., Clayton,R.A.,Kirkness,E.F., Kerlavage,A.R., Bult,C.J., Tomb,J.F.,Dougherty,B.A., Merrick,J.M. et al. (1995) Whole-genomerandom sequencing and assembly of Haemophilus inﬂuenzae Rd.Science, 269, 496–512.2. Galperin,M.Y. and Koonin,E.V. (2010) From complete genomesequence to ‘complete’ understanding? Trends Biotechnol., 28,398–406.3. Brenner,S.E. (1999) Errors in genome annotation. Trends Genet.,15, 132–133.4. Schnoes,A.M., Brown,S.D., Dodevski,I. and Babbitt,P.C. (2009)Annotation error in public databases: misannotation of molecularfunction in enzyme superfamilies. PLoS Comp. Biol., 5, e1000605.5. Green,M.L. and Karp,P.A. (2005) Genome annotation errorsin pathway databases due to semantic ambiguity in partialEC numbers. Nucleic Acids Res., 33, 4035–4039.6. Hsiao,T.L., Revelles,O., Chen,L., Sauer,U. and Vitkup,D. (2010)Automatic policing of biochemical annotations using genomiccorrelations. Nat. Chem. Biol., 6, 34–40.7. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J. andSayers,E.W. (2010) Genbank. Nucleic Acids Res., 38, D46–D51.8. Kaminuma,E., Mashima,J., Kodama,Y., Gojobori,T.,Ogasawara,O., Okubo,K., Takagi,T. and Nakamura,Y. (2010)DDBJ launches a new archive database with analytical tools fornext-generation sequence data. Nucleic Acids Res., 38, D33–D38.Nucleic Acids Research, 2011, Vol. 39, Database issue D139. Leinonen,R., Akhtar,R., Birney,E., Bonﬁeld,J., Corbett,M.,Cheng,Y., Demiralp,F., Faruque,N., Goodgame,N., Gobson,R.et al. (2010) Improvements to services at the EuropeanNucleotide Archive. Nucleic Acids Res., 38, D39–D45.10. Heurgue-Hamard,V., Champ,S., Engstrom,A., Ehrenberg,M. andBuckingham,R.H. (2002) The hemK gene in Escherichia coliencodes the N(5)-glutamine methyltransferase that modiﬁespeptide release factors. EMBO J., 21, 769–778.11. Nakahigashi,K., Kubo,N., Narita,S., Shimaoka,T., Goto,S.,Oshima,T., Mori,H., Maeda,M., Wada,C. and Inokuchi,H. (2002)HemK, a class of protein methyl transferase with similarity toDNA methyl transferases, methylates polypeptide chain releasefactors, and hemK knockout induces defects in translationaltermination. Proc. Natl Acad. Sci. USA, 99, 1473–1478.12. Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2007) NCBIreference sequences (RefSeq): a curated non-redundant sequencedatabase of genomes, transcripts and proteins. Nucleic Acids Res.,35, D61–65.13. Klimke,W., Agarwala,R., Badretdin,A., Chetvernin,S., Ciufo,S.,Fedorov,B., Kiryutin,B., O’Neill,K., Resch,W., Resenchuk,S.et al. (2009) The National Center for BiotechnologyInformation’s Protein Clusters Database. Nucleic Acids Res., 37,D216–223.14. The UniProt Consortium. (2010) The Universal Protein Resource(UniProt) in 2010. Nucleic Acids Res., 38, D142–D148.15. Roberts,R.J. (2004) Identifying protein function–a callfor community action. PLoS Biol., 2, E42.D14 Nucleic Acids Research, 2011, Vol. 39, Database issue

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.GO grant from National Institute of General Medical Sciences (NIGMS) (1RC2GM092602-01 to COMBREX). The open access publication charge for this paper has been waived by Oxford University Press - NAR Editorial Board members are entitled to one free paper per year in recognition of their work on behalf of the journal. (1RC2GM092602-01 - National Institute of General Medical Sciences (NIGMS))Published versio

Segre, Daniel

Salzberg, Steven L.

Boston University Institutional Repository (OpenBU)

Boston UniversityOpenBU http://open.bu.eduBiomedical Engineering BU Open Access Articles2011-01-01COMBREX: a project to acceleratethe functional annotation ofprokaryotic genomesThis work was made openly accessible by BU Faculty. Please share how this access benefits you.Your story matters.Version Published versionCitation (published version): Richard J Roberts, Yi-Chien Chang, Zhenjun Hu, John N Rachlin, BrianP Anton, Revonda M Pokrzywa, Han-Pil Choi, Lina L Faller, JyotsnaGuleria, Genevieve Housman, Niels Klitgord, Varun Mazumdar, MarkG McGettrick, Lais Osmani, Rajeswari Swaminathan, Kevin R Tao,Stan Letovsky, Dennis Vitkup, Daniel Segre, Steven L Salzberg,Charles Delisi, Martin Steffen, Simon Kasif. 2011. "COMBREX: aproject to accelerate the functional annotation of prokaryoticgenomes." NUCLEIC ACIDS RESEARCH, Volume 39, pp. D11 - D14 (4).https://doi.org/10.1093/nar/gkq1168https://hdl.handle.net/2144/39164Boston UniversityCOMBREX: a project to accelerate the functionalannotation of prokaryotic genomesRichard J. Roberts1,2,*, Yi-Chien Chang1, Zhenjun Hu1, John N. Rachlin3,Brian P. Anton1,2, Revonda M. Pokrzywa4, Han-Pil Choi5, Lina L. Faller1,Jyotsna Guleria4, Genevieve Housman4, Niels Klitgord1, Varun Mazumdar1,Mark G. McGettrick3, Lais Osmani4, Rajeswari Swaminathan4, Kevin R. Tao4,Stan Letovsky1, Dennis Vitkup6,7, Daniel Segre`1,4,8, Steven L. Salzberg9,Charles Delisi1,4, Martin Steffen1,4,5,* and Simon Kasif1,4,10,*1Bioinformatics Program, Boston University, Boston, MA 02215, 2New England Biolabs, 240 County Road,Ipswich, MA 01938, 3Diatom Software LLC, 260 Winter St., Holliston, MA 01746, 4Department of BiomedicalEngineering, Boston University, Boston, MA 02215, 5Department of Pathology and Laboratory Medicine,Boston University School of Medicine, Boston University, Boston, MA 021184, 6Department of BiomedicalInformatics, Columbia University, New York, NY 10032, 7Center for Computational Biology and Bioinformatics,Columbia University, New York, 10032, 8Department of Biology, Boston University, Boston MA 02215,9Department of Computer Science, University of Maryland, College Park, MD, 20742 and 10Children’sHospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology (CHIP@HST),Cambridge, MA 02139, USAReceived October 15, 2010; Revised October 31, 2010; Accepted November 1, 2010ABSTRACTCOMBREX (http://combrex.bu.edu) is a project toincrease the speed of the functional annotation ofnew bacterial and archaeal genomes. It consists of adatabase of functional predictions produced bycomputational biologists and a mechanism for ex-perimental biochemists to bid for the validation ofthose predictions. Small grants are available tosupport successful bids.INTRODUCTIONIn the last 15 years, since the determination of the completesequence of the Haemophilus influenzae strain Rd genome(1), there has been a rapid increase in the number of pro-karyotic genomes that are being sequenced each year. Withthe cost of DNA sequencing continuing to drop, this hasled to an explosion in the number of genes that are pre-dicted computationally, but for which no solid functionalannotation can be provided (2). This is illustrated in Table1, which shows that in a selection of genomes, at best,maybe 70% of the genes have either known, experimental-ly validated functions or can be assigned function compu-tationally on the basis of sequence similarity, but oftenwith varying or unknown degrees of confidence. Witheach new genome typically containing anywhere from500 to 1000 new genes of unknown function, we face thedaunting challenge of determining those functions so thatthe annotation of new genome sequences can be carried outcomputationally with just a few key functions being testedexperimentally. This means that our ability to predictfunction computationally will need to be quite accurateand must include all genes.Currently, the quality of computational predictions offunction is far from perfect. Indeed, for many of the genesin GenBank the present annotations are either incorrector so general as to be of little value to the user (3–6).The reason for this is that by far the most commonway of making predictions is by checking each newly pre-dicted gene for its similarity to genes annotated in theINSDC (International Nucleotide Sequence DatabaseConsortium) databases (7–9). When a new gene shareshigh sequence similarity to an annotated gene then it isassigned the same function as that presumed known gene.If they are identical or nearly so, then this method is quite*To whom correspondence should be addressed. Tel: +1 617 358 1845; Fax: +1 617 353 6766; Email: kasif@bu.eduCorrespondence may also be addressed to Richard J. Roberts. Tel: +1 978 380 7405; Fax: +1 978 380 7406; Email: roberts@neb.comCorrespondence may also be addressed to Martin Steffen. Tel: +1 617 826-9486/7935; Fax: +1 617 414 7073; Email: steffen@bu.eduPublished online 20 November 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D11–D14doi:10.1093/nar/gkq1168 The Author(s) 2010. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.reliable. However, when the degree of sequence similarityis poor, or perhaps even when it is reasonably high withonly a few key amino acids difference, this can lead toproblems, because one can be less sure that the newgene really is an ortholog of the known gene. Perhaps,the new gene encodes a protein with a function that issimilar to the known one, but different in some subtleway such that its substrate preference has changed.Unless the sequence differences can be interpretedproperly so that the new protein’s function is notdeclared to be identical to that of the old gene product,then a mis-annotation ensues and will likely be propagated(3–6).A number of now classic examples of such mis-annotations have been noted in the literature and onlywhen biochemical experiments were carried out, couldthe annotation be corrected. One classic example wasthe family of genes labeled hemK. These genes had beenannotated as either a protoporphyrinogen oxidase or aDNA methyltransferase. It later turned out that thehemK gene in Escherichia coli actually encoded a proteinmethyltransferase, a finding of some considerable interestbecause the hemK gene is widely conserved from humansall the way to bacteria (10,11), although further testingon remote homolog’s would still be appropriate. Thisemphasizes the need for biochemical characterization ofgene products whenever possible, but certainly whenthe sequence distance to a known gene product is insuffi-ciently high to be certain of the assignment. The degree ofcaution necessary often varies, depending on the level ofconservation or its location. In some cases, one or a fewamino acid changes in a region of a protein responsible forsubstrate recognition can completely alter its function.Unfortunately, we often do not know in which regionsof a protein we should look for such changes and thecomputer blindly labels the new gene incorrectly.THE COMBREX DATABASEOnce a particular gene’s function is characterized bio-chemically, then that function can be propagated withsome degree of certainty to the likely orthologous genesin other organisms, although remote homolog’s willrequire experimental validation. It is precisely this com-bination of computational prediction and biochemical val-idation of function that the COMBREX (COMputationalBRidges to EXperiments) project, recently funded byNIGMS, is all about. At the heart of COMBREX is adatabase (http://combrex.bu.edu) of computational pre-dictions of gene function. This database, which is current-ly under construction, contains all of the annotated genespresent in the bacterial and archaeal genomes section ofthe NCBI’s Protein RefSeq Database (12,13). We takeadvantage of the Clusters Database (14) in which thesegenes have been sorted into families of similar sequencesto organize the genes for predictive purposes. However, inaddition to the annotations contained in the ClustersDatabase, which are themselves of course predictions forthe most part, the COMBREX database also containsfunctional predictions made by other groups of computa-tional biologists. A major goal is to provide reliabilityestimates for those predictions. We already have someongoing collaborations and it is our hope that we caninvolve many others in the bioinformatics communitywho are making gene predictions and who are willing toshare them through the medium of the COMBREXdatabase where they will be publicly and freely available.The reason for doing so is that we are recruiting biochem-ists to test those predictions experimentally.The involvement of the biochemists is the otherleg of this project. We are inviting them to browse theCOMBREX database and identify predictions thatmatch their own laboratory’s biochemical expertise.They can then make a bid to test the function of anyhigh-value predictions lying within their area of expertise.For instance, if an unknown protein is predicted to hydro-lyze carbohydrates, then we would look for a laboratorythat is expert in such hydrolases that would have a largerange of carbohydrate substrates and suitable assaysto detect hydrolytic activity. The idea is that they wouldmake some of the protein from the gene in questionand run it through their battery of assays. For USlaboratories, we are able to offer small grants throughCOMBREX that are typically in the range of $5000 to$10 000 to support his work, which might be carried outby an individual such as a graduate student, a supervisedrotation student or anyone with sufficient proven expertiseto complete the task. There should be opportunities herefor teaching colleges to participate as well as for some ofthe top universities to incorporate this approach into theirnormal curriculum. The successful bid would guaranteesix months of sole access to that gene product throughCOMBREX and at the end of that time, a report wouldbe written and would be available on the COMBREXwebsite describing the experiments that were performedand the results, either a positive validation or a failureto detect the predicted activity. We would encourage thelaboratory to publish these results in the peer reviewedliterature, and they would be featured on theCOMBREX web site. They would also be propagated tothe appropriate databases of the INSDC.A GOLD STANDARD DATABASE OF PROTEINSOne important problem that has been identified during theinitial stage of the COMBREX project, is that theTable 1. Distribution of annotated genes in selected genomesOrganism PublicationdateUnknownfunctiona(%)Known orpredictedfunctionTotalHaemophilus influenzae Rd 1995 429 (26) 1228 1657Methanocaldococcus jannaschii 1996 616 (35) 1155 1771Helicobacter pylori 26695 1997 552 (35) 1021 1573Escherichia coli MG1655 1997 550 (13) 3594 4144Pseudomonas aeruginosa PA7 2010 2653 (42) 3633 6286aBased on genes annotated as unknown or conserved hypothetical inthe RefSeq files. These represent lower estimates as the accuracy of thepredicted functions is unknown.D12 Nucleic Acids Research, 2011, Vol. 39, Database issuesuccessful propagation of annotations from one gene toanother, depends critically on knowing when a protein ofknown sequence has an experimentally demonstrated bio-chemical function, because this becomes a standardagainst which similar proteins can have their functionspredicted. It turns out that this information is notalways easily deciphered. For many proteins the biochem-ical characterization was carried out on a purified proteinfrom a bacterial strain many years before the gene for thatprotein was cloned and sequenced. In this case the clonedgene may well come from a strain that is different from theone in which the original characterization was done andthere may be subtle, but important, differences insequence. Sometimes accurate strain information can befound in the major databases, because both the sequenceand the characterization were described in a single publi-cation. But often the necessary pedigree information is noteasily traced. Recognizing that this is a major problem,not just for COMBREX, but for all groups trying topropagate annotation, we have now undertaken aproject in collaboration with the RefSeq Database atNCBI (12) and the UniProt Knowledge Database atEBI (14) to identify a ‘Gold Standard Set of Proteins’for which the function is known and the exact sequenceof the gene/protein on which the functional tests weredone is known. This gold standard project is currentlybeing managed by COMBREX and a pipeline has beenset up whereby candidate genes/proteins for gold standardstatus are identified and distributed to individual annota-tors who will check in detail that they do indeed, have goldstandard properties. The final gold standard databasewill be maintained and distributed from both NCBI andUniProt.This gold standard project is another community-basedproject in which individuals from around the world canhelp either by identifying potential gold standard genes orby helping in the manual curation. In a similar fashion, wehope that COMBREX will also become a community-based project around the world so that experimental char-acterization of function for COMBREX predictionscan be carried out in laboratories in many countries. Itis worth noting that this approach can be used as ateaching tool for students learning how to do hands-onbiochemistry, while at the same time making a valuablecontribution to biology. There are many smalllaboratories with appropriate biochemical expertise totest certain specific predictions within the area of theirexpertise and for whom a small amount of money canmake a large difference. Already a number of collabor-ators are helping with this project. We hope that byengaging the larger community of computational biolo-gists and biochemists, we can build momentum in aproject that has the potential to greatly increase theaccuracy of genome annotation. It should enablebioinformaticians to make better predictions by providinga set of reference points in the gold standard set ofproteins and facilitating the biochemical testing of theirpredictions. The feedback from this validation processshould then impact their ability to make more reliablepredictions. The involvement of the experimental commu-nity will highlight the importance of their discipline whilealso providing some funds that might help train the futuregeneration. A successful outcome to the project shouldmean that the number of genes in need of experimentalverification will diminish with time, because the computerpredictions can be assessed by rigorously documentingtheir distance to a gold standard protein.The size of the functional annotation problem isenormous and it is essential that it be tackled if we areto keep pace with the genome and metagenome projectsthat are currently underway. It is one where collaborationis demanded and competition would merely serve towaste money. While the original idea was proposed in2004 (15), before the current ‘Wiki’ approaches werepopular, COMBREX can be seen as essentially a similarcommunity-based approach to the problem of functionalannotation of prokaryotic genomes. We anticipate thatif this project is successful, the model will be expandedfrom its initial focus on bacteria and archaea to covergenomes in all kingdoms of life. It will also providesome more of the raw data on function that may oneday permit systems biology to really view an organismas a system. Furthermore, it will also have an impact onmany of the databases in this issue. For a relatively smallinvestment and a massively parallel human effort, itshould be possible to achieve high throughput.FUNDINGGO grant from National Institute of GeneralMedical Sciences (NIGMS) (1RC2GM092602-01 toCOMBREX). The open access publication charge forthis paper has been waived by Oxford University Press -NAR Editorial Board members are entitled to one freepaper per year in recognition of their work on behalf ofthe journal.Conflict of interest statement. None declared.REFERENCES1. Fleischmann,R.D., Adams,M.D., White,O., Clayton,R.A.,Kirkness,E.F., Kerlavage,A.R., Bult,C.J., Tomb,J.F.,Dougherty,B.A., Merrick,J.M. et al. (1995) Whole-genomerandom sequencing and assembly of Haemophilus influenzae Rd.Science, 269, 496–512.2. Galperin,M.Y. and Koonin,E.V. (2010) From complete genomesequence to ‘complete’ understanding? Trends Biotechnol., 28,398–406.3. Brenner,S.E. (1999) Errors in genome annotation. Trends Genet.,15, 132–133.4. Schnoes,A.M., Brown,S.D., Dodevski,I. and Babbitt,P.C. (2009)Annotation error in public databases: misannotation of molecularfunction in enzyme superfamilies. PLoS Comp. Biol., 5, e1000605.5. Green,M.L. and Karp,P.A. (2005) Genome annotation errorsin pathway databases due to semantic ambiguity in partialEC numbers. Nucleic Acids Res., 33, 4035–4039.6. Hsiao,T.L., Revelles,O., Chen,L., Sauer,U. and Vitkup,D. (2010)Automatic policing of biochemical annotations using genomiccorrelations. Nat. Chem. Biol., 6, 34–40.7. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J. andSayers,E.W. (2010) Genbank. Nucleic Acids Res., 38, D46–D51.8. Kaminuma,E., Mashima,J., Kodama,Y., Gojobori,T.,Ogasawara,O., Okubo,K., Takagi,T. and Nakamura,Y. (2010)DDBJ launches a new archive database with analytical tools fornext-generation sequence data. Nucleic Acids Res., 38, D33–D38.Nucleic Acids Research, 2011, Vol. 39, Database issue D139. Leinonen,R., Akhtar,R., Birney,E., Bonfield,J., Corbett,M.,Cheng,Y., Demiralp,F., Faruque,N., Goodgame,N., Gobson,R.et al. (2010) Improvements to services at the EuropeanNucleotide Archive. Nucleic Acids Res., 38, D39–D45.10. Heurgue-Hamard,V., Champ,S., Engstrom,A., Ehrenberg,M. andBuckingham,R.H. (2002) The hemK gene in Escherichia coliencodes the N(5)-glutamine methyltransferase that modifiespeptide release factors. EMBO J., 21, 769–778.11. Nakahigashi,K., Kubo,N., Narita,S., Shimaoka,T., Goto,S.,Oshima,T., Mori,H., Maeda,M., Wada,C. and Inokuchi,H. (2002)HemK, a class of protein methyl transferase with similarity toDNA methyl transferases, methylates polypeptide chain releasefactors, and hemK knockout induces defects in translationaltermination. Proc. Natl Acad. Sci. USA, 99, 1473–1478.12. Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2007) NCBIreference sequences (RefSeq): a curated non-redundant sequencedatabase of genomes, transcripts and proteins. Nucleic Acids Res.,35, D61–65.13. Klimke,W., Agarwala,R., Badretdin,A., Chetvernin,S., Ciufo,S.,Fedorov,B., Kiryutin,B., O’Neill,K., Resch,W., Resenchuk,S.et al. (2009) The National Center for BiotechnologyInformation’s Protein Clusters Database. Nucleic Acids Res., 37,D216–223.14. The UniProt Consortium. (2010) The Universal Protein Resource(UniProt) in 2010. Nucleic Acids Res., 38, D142–D148.15. Roberts,R.J. (2004) Identifying protein function–a callfor community action. PLoS Biol., 2, E42.D14 Nucleic Acids Research, 2011, Vol. 39, Database issue

English

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.National Institute of General Medical Sciences (1RC2GM092602-01

DeLisi, Charles

Name not available

COMBREX: A Project to Accelerate the Functional Annotation of Prokaryotic Genomes

Boston UniversityOpenBU http://open.bu.eduBiomedical Engineering ENG: Biomedical Engineering: Scholarly Papers2010-11-20COMBREX: A Project to Acceleratethe Functional Annotation ofProkaryotic GenomesRoberts, Richard J., Yi-Chien Chang, Zhenjun Hu, John N. Rachlin, Brian P. Anton,Revonda M. Pokrzywa, Han-Pil Choi, Lina L. Faller, Jyotsna Guleria, GenevieveHousman, Niels Klitgord, Varun Mazumdar, Mark G. McGettrick, Lais Osmani,Rajeswari Swaminathan, Kevin R. Tao, Stan Letovsky, Dennis Vitkup, Daniel Segré;,Steven L. Salzberg, Charles Delisi, Martin Steffen, Simon Kasif. "COMBREX: a projectto accelerate the functional annotation of prokaryotic genomes" Nucleic AcidsResearch 39(Database issue): D11-D14. (2010)https://hdl.handle.net/2144/3036Boston UniversityCOMBREX: a project to accelerate the functionalannotation of prokaryotic genomesRichard J. Roberts1,2,*, Yi-Chien Chang1, Zhenjun Hu1, John N. Rachlin3,Brian P. Anton1,2, Revonda M. Pokrzywa4, Han-Pil Choi5, Lina L. Faller1,Jyotsna Guleria4, Genevieve Housman4, Niels Klitgord1, Varun Mazumdar1,Mark G. McGettrick3, Lais Osmani4, Rajeswari Swaminathan4, Kevin R. Tao4,Stan Letovsky1, Dennis Vitkup6,7, Daniel Segre`1,4,8, Steven L. Salzberg9,Charles Delisi1,4, Martin Steffen1,4,5,* and Simon Kasif1,4,10,*1Bioinformatics Program, Boston University, Boston, MA 02215, 2New England Biolabs, 240 County Road,Ipswich, MA 01938, 3Diatom Software LLC, 260 Winter St., Holliston, MA 01746, 4Department of BiomedicalEngineering, Boston University, Boston, MA 02215, 5Department of Pathology and Laboratory Medicine,Boston University School of Medicine, Boston University, Boston, MA 021184, 6Department of BiomedicalInformatics, Columbia University, New York, NY 10032, 7Center for Computational Biology and Bioinformatics,Columbia University, New York, 10032, 8Department of Biology, Boston University, Boston MA 02215,9Department of Computer Science, University of Maryland, College Park, MD, 20742 and 10Children’sHospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology (CHIP@HST),Cambridge, MA 02139, USAReceived October 15, 2010; Revised October 31, 2010; Accepted November 1, 2010ABSTRACTCOMBREX (http://combrex.bu.edu) is a project toincrease the speed of the functional annotation ofnew bacterial and archaeal genomes. It consists of adatabase of functional predictions produced bycomputational biologists and a mechanism for ex-perimental biochemists to bid for the validation ofthose predictions. Small grants are available tosupport successful bids.INTRODUCTIONIn the last 15 years, since the determination of the completesequence of the Haemophilus influenzae strain Rd genome(1), there has been a rapid increase in the number of pro-karyotic genomes that are being sequenced each year. Withthe cost of DNA sequencing continuing to drop, this hasled to an explosion in the number of genes that are pre-dicted computationally, but for which no solid functionalannotation can be provided (2). This is illustrated in Table1, which shows that in a selection of genomes, at best,maybe 70% of the genes have either known, experimental-ly validated functions or can be assigned function compu-tationally on the basis of sequence similarity, but oftenwith varying or unknown degrees of confidence. Witheach new genome typically containing anywhere from500 to 1000 new genes of unknown function, we face thedaunting challenge of determining those functions so thatthe annotation of new genome sequences can be carried outcomputationally with just a few key functions being testedexperimentally. This means that our ability to predictfunction computationally will need to be quite accurateand must include all genes.Currently, the quality of computational predictions offunction is far from perfect. Indeed, for many of the genesin GenBank the present annotations are either incorrector so general as to be of little value to the user (3–6).The reason for this is that by far the most commonway of making predictions is by checking each newly pre-dicted gene for its similarity to genes annotated in theINSDC (International Nucleotide Sequence DatabaseConsortium) databases (7–9). When a new gene shareshigh sequence similarity to an annotated gene then it isassigned the same function as that presumed known gene.If they are identical or nearly so, then this method is quite*To whom correspondence should be addressed. Tel: +1 617 358 1845; Fax: +1 617 353 6766; Email: kasif@bu.eduCorrespondence may also be addressed to Richard J. Roberts. Tel: +1 978 380 7405; Fax: +1 978 380 7406; Email: roberts@neb.comCorrespondence may also be addressed to Martin Steffen. Tel: +1 617 826-9486/7935; Fax: +1 617 414 7073; Email: steffen@bu.eduPublished online 20 November 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D11–D14doi:10.1093/nar/gkq1168 The Author(s) 2010. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.reliable. However, when the degree of sequence similarityis poor, or perhaps even when it is reasonably high withonly a few key amino acids difference, this can lead toproblems, because one can be less sure that the newgene really is an ortholog of the known gene. Perhaps,the new gene encodes a protein with a function that issimilar to the known one, but different in some subtleway such that its substrate preference has changed.Unless the sequence differences can be interpretedproperly so that the new protein’s function is notdeclared to be identical to that of the old gene product,then a mis-annotation ensues and will likely be propagated(3–6).A number of now classic examples of such mis-annotations have been noted in the literature and onlywhen biochemical experiments were carried out, couldthe annotation be corrected. One classic example wasthe family of genes labeled hemK. These genes had beenannotated as either a protoporphyrinogen oxidase or aDNA methyltransferase. It later turned out that thehemK gene in Escherichia coli actually encoded a proteinmethyltransferase, a finding of some considerable interestbecause the hemK gene is widely conserved from humansall the way to bacteria (10,11), although further testingon remote homolog’s would still be appropriate. Thisemphasizes the need for biochemical characterization ofgene products whenever possible, but certainly whenthe sequence distance to a known gene product is insuffi-ciently high to be certain of the assignment. The degree ofcaution necessary often varies, depending on the level ofconservation or its location. In some cases, one or a fewamino acid changes in a region of a protein responsible forsubstrate recognition can completely alter its function.Unfortunately, we often do not know in which regionsof a protein we should look for such changes and thecomputer blindly labels the new gene incorrectly.THE COMBREX DATABASEOnce a particular gene’s function is characterized bio-chemically, then that function can be propagated withsome degree of certainty to the likely orthologous genesin other organisms, although remote homolog’s willrequire experimental validation. It is precisely this com-bination of computational prediction and biochemical val-idation of function that the COMBREX (COMputationalBRidges to EXperiments) project, recently funded byNIGMS, is all about. At the heart of COMBREX is adatabase (http://combrex.bu.edu) of computational pre-dictions of gene function. This database, which is current-ly under construction, contains all of the annotated genespresent in the bacterial and archaeal genomes section ofthe NCBI’s Protein RefSeq Database (12,13). We takeadvantage of the Clusters Database (14) in which thesegenes have been sorted into families of similar sequencesto organize the genes for predictive purposes. However, inaddition to the annotations contained in the ClustersDatabase, which are themselves of course predictions forthe most part, the COMBREX database also containsfunctional predictions made by other groups of computa-tional biologists. A major goal is to provide reliabilityestimates for those predictions. We already have someongoing collaborations and it is our hope that we caninvolve many others in the bioinformatics communitywho are making gene predictions and who are willing toshare them through the medium of the COMBREXdatabase where they will be publicly and freely available.The reason for doing so is that we are recruiting biochem-ists to test those predictions experimentally.The involvement of the biochemists is the otherleg of this project. We are inviting them to browse theCOMBREX database and identify predictions thatmatch their own laboratory’s biochemical expertise.They can then make a bid to test the function of anyhigh-value predictions lying within their area of expertise.For instance, if an unknown protein is predicted to hydro-lyze carbohydrates, then we would look for a laboratorythat is expert in such hydrolases that would have a largerange of carbohydrate substrates and suitable assaysto detect hydrolytic activity. The idea is that they wouldmake some of the protein from the gene in questionand run it through their battery of assays. For USlaboratories, we are able to offer small grants throughCOMBREX that are typically in the range of $5000 to$10 000 to support his work, which might be carried outby an individual such as a graduate student, a supervisedrotation student or anyone with sufficient proven expertiseto complete the task. There should be opportunities herefor teaching colleges to participate as well as for some ofthe top universities to incorporate this approach into theirnormal curriculum. The successful bid would guaranteesix months of sole access to that gene product throughCOMBREX and at the end of that time, a report wouldbe written and would be available on the COMBREXwebsite describing the experiments that were performedand the results, either a positive validation or a failureto detect the predicted activity. We would encourage thelaboratory to publish these results in the peer reviewedliterature, and they would be featured on theCOMBREX web site. They would also be propagated tothe appropriate databases of the INSDC.A GOLD STANDARD DATABASE OF PROTEINSOne important problem that has been identified during theinitial stage of the COMBREX project, is that theTable 1. Distribution of annotated genes in selected genomesOrganism PublicationdateUnknownfunctiona(%)Known orpredictedfunctionTotalHaemophilus influenzae Rd 1995 429 (26) 1228 1657Methanocaldococcus jannaschii 1996 616 (35) 1155 1771Helicobacter pylori 26695 1997 552 (35) 1021 1573Escherichia coli MG1655 1997 550 (13) 3594 4144Pseudomonas aeruginosa PA7 2010 2653 (42) 3633 6286aBased on genes annotated as unknown or conserved hypothetical inthe RefSeq files. These represent lower estimates as the accuracy of thepredicted functions is unknown.D12 Nucleic Acids Research, 2011, Vol. 39, Database issuesuccessful propagation of annotations from one gene toanother, depends critically on knowing when a protein ofknown sequence has an experimentally demonstrated bio-chemical function, because this becomes a standardagainst which similar proteins can have their functionspredicted. It turns out that this information is notalways easily deciphered. For many proteins the biochem-ical characterization was carried out on a purified proteinfrom a bacterial strain many years before the gene for thatprotein was cloned and sequenced. In this case the clonedgene may well come from a strain that is different from theone in which the original characterization was done andthere may be subtle, but important, differences insequence. Sometimes accurate strain information can befound in the major databases, because both the sequenceand the characterization were described in a single publi-cation. But often the necessary pedigree information is noteasily traced. Recognizing that this is a major problem,not just for COMBREX, but for all groups trying topropagate annotation, we have now undertaken aproject in collaboration with the RefSeq Database atNCBI (12) and the UniProt Knowledge Database atEBI (14) to identify a ‘Gold Standard Set of Proteins’for which the function is known and the exact sequenceof the gene/protein on which the functional tests weredone is known. This gold standard project is currentlybeing managed by COMBREX and a pipeline has beenset up whereby candidate genes/proteins for gold standardstatus are identified and distributed to individual annota-tors who will check in detail that they do indeed, have goldstandard properties. The final gold standard databasewill be maintained and distributed from both NCBI andUniProt.This gold standard project is another community-basedproject in which individuals from around the world canhelp either by identifying potential gold standard genes orby helping in the manual curation. In a similar fashion, wehope that COMBREX will also become a community-based project around the world so that experimental char-acterization of function for COMBREX predictionscan be carried out in laboratories in many countries. Itis worth noting that this approach can be used as ateaching tool for students learning how to do hands-onbiochemistry, while at the same time making a valuablecontribution to biology. There are many smalllaboratories with appropriate biochemical expertise totest certain specific predictions within the area of theirexpertise and for whom a small amount of money canmake a large difference. Already a number of collabor-ators are helping with this project. We hope that byengaging the larger community of computational biolo-gists and biochemists, we can build momentum in aproject that has the potential to greatly increase theaccuracy of genome annotation. It should enablebioinformaticians to make better predictions by providinga set of reference points in the gold standard set ofproteins and facilitating the biochemical testing of theirpredictions. The feedback from this validation processshould then impact their ability to make more reliablepredictions. The involvement of the experimental commu-nity will highlight the importance of their discipline whilealso providing some funds that might help train the futuregeneration. A successful outcome to the project shouldmean that the number of genes in need of experimentalverification will diminish with time, because the computerpredictions can be assessed by rigorously documentingtheir distance to a gold standard protein.The size of the functional annotation problem isenormous and it is essential that it be tackled if we areto keep pace with the genome and metagenome projectsthat are currently underway. It is one where collaborationis demanded and competition would merely serve towaste money. While the original idea was proposed in2004 (15), before the current ‘Wiki’ approaches werepopular, COMBREX can be seen as essentially a similarcommunity-based approach to the problem of functionalannotation of prokaryotic genomes. We anticipate thatif this project is successful, the model will be expandedfrom its initial focus on bacteria and archaea to covergenomes in all kingdoms of life. It will also providesome more of the raw data on function that may oneday permit systems biology to really view an organismas a system. Furthermore, it will also have an impact onmany of the databases in this issue. For a relatively smallinvestment and a massively parallel human effort, itshould be possible to achieve high throughput.FUNDINGGO grant from National Institute of GeneralMedical Sciences (NIGMS) (1RC2GM092602-01 toCOMBREX). The open access publication charge forthis paper has been waived by Oxford University Press -NAR Editorial Board members are entitled to one freepaper per year in recognition of their work on behalf ofthe journal.Conflict of interest statement. None declared.REFERENCES1. Fleischmann,R.D., Adams,M.D., White,O., Clayton,R.A.,Kirkness,E.F., Kerlavage,A.R., Bult,C.J., Tomb,J.F.,Dougherty,B.A., Merrick,J.M. et al. (1995) Whole-genomerandom sequencing and assembly of Haemophilus influenzae Rd.Science, 269, 496–512.2. Galperin,M.Y. and Koonin,E.V. (2010) From complete genomesequence to ‘complete’ understanding? Trends Biotechnol., 28,398–406.3. Brenner,S.E. (1999) Errors in genome annotation. Trends Genet.,15, 132–133.4. Schnoes,A.M., Brown,S.D., Dodevski,I. and Babbitt,P.C. (2009)Annotation error in public databases: misannotation of molecularfunction in enzyme superfamilies. PLoS Comp. Biol., 5, e1000605.5. Green,M.L. and Karp,P.A. (2005) Genome annotation errorsin pathway databases due to semantic ambiguity in partialEC numbers. Nucleic Acids Res., 33, 4035–4039.6. Hsiao,T.L., Revelles,O., Chen,L., Sauer,U. and Vitkup,D. (2010)Automatic policing of biochemical annotations using genomiccorrelations. Nat. Chem. Biol., 6, 34–40.7. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J. andSayers,E.W. (2010) Genbank. Nucleic Acids Res., 38, D46–D51.8. Kaminuma,E., Mashima,J., Kodama,Y., Gojobori,T.,Ogasawara,O., Okubo,K., Takagi,T. and Nakamura,Y. (2010)DDBJ launches a new archive database with analytical tools fornext-generation sequence data. Nucleic Acids Res., 38, D33–D38.Nucleic Acids Research, 2011, Vol. 39, Database issue D139. Leinonen,R., Akhtar,R., Birney,E., Bonfield,J., Corbett,M.,Cheng,Y., Demiralp,F., Faruque,N., Goodgame,N., Gobson,R.et al. (2010) Improvements to services at the EuropeanNucleotide Archive. Nucleic Acids Res., 38, D39–D45.10. Heurgue-Hamard,V., Champ,S., Engstrom,A., Ehrenberg,M. andBuckingham,R.H. (2002) The hemK gene in Escherichia coliencodes the N(5)-glutamine methyltransferase that modifiespeptide release factors. EMBO J., 21, 769–778.11. Nakahigashi,K., Kubo,N., Narita,S., Shimaoka,T., Goto,S.,Oshima,T., Mori,H., Maeda,M., Wada,C. and Inokuchi,H. (2002)HemK, a class of protein methyl transferase with similarity toDNA methyl transferases, methylates polypeptide chain releasefactors, and hemK knockout induces defects in translationaltermination. Proc. Natl Acad. Sci. USA, 99, 1473–1478.12. Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2007) NCBIreference sequences (RefSeq): a curated non-redundant sequencedatabase of genomes, transcripts and proteins. Nucleic Acids Res.,35, D61–65.13. Klimke,W., Agarwala,R., Badretdin,A., Chetvernin,S., Ciufo,S.,Fedorov,B., Kiryutin,B., O’Neill,K., Resch,W., Resenchuk,S.et al. (2009) The National Center for BiotechnologyInformation’s Protein Clusters Database. Nucleic Acids Res., 37,D216–223.14. The UniProt Consortium. (2010) The Universal Protein Resource(UniProt) in 2010. Nucleic Acids Res., 38, D142–D148.15. Roberts,R.J. (2004) Identifying protein function–a callfor community action. PLoS Biol., 2, E42.D14 Nucleic Acids Research, 2011, Vol. 39, Database issue

Crossref

Annotation error in public databases: misannotation of molecular function in enzyme superfamilies.

Automatic policing of biochemical annotations using genomic correlations.

DDBJ launches a new archive database with analytical tools for next-generation sequence data.

Errors in genome annotation.

From complete genome sequence to ‘complete’ understanding?

Genome annotation errors in pathway databases due to semantic ambiguity in partial EC numbers.

HemK, a class of protein methyl transferase with similarity to DNA methyl transferases, methylates polypeptide chain release factors, and hemK knockout induces defects in translational termination.

Identifying protein function–a call for community action.

NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.

The hemK gene in Escherichia coli encodes the N(5)-glutamine methyltransferase that modiﬁes peptide release factors.

The National Center for Biotechnology Information’s Protein Clusters Database.

The UniProt Consortium.

Whole-genome random sequencing and assembly of Haemophilus inﬂuenzae Rd.

https://open.bu.edu/bitstream/2144/3036/1/gkq1168.pdf

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

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