Abstract
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
Original language | English (US) |
---|---|
Pages (from-to) | 221-227 |
Number of pages | 7 |
Journal | Nature Methods |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2013 |
Externally published | Yes |
ASJC Scopus subject areas
- Biotechnology
- Biochemistry
- Molecular Biology
- Cell Biology
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A large-scale evaluation of computational protein function prediction. / Radivojac, Predrag; Clark, Wyatt T.; Oron, Tal Ronnen; Schnoes, Alexandra M.; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M.; Talwalkar, Ameet S.; Repo, Susanna; Souza, Michael L.; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W.A.; Bryson, Kevin; Jones, David T.; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K.; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M.; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E.; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A.; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N.; Sternberg, Michael J.E.; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A.I.; Van Dijk, Aalt D.J.; Ter Braak, Cajo J.F.; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C.; Brenner, Steven E.; Orengo, Christine; Rost, Burkhard; Mooney, Sean D.; Friedberg, Iddo.
In: Nature Methods, Vol. 10, No. 3, 03.2013, p. 221-227.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - A large-scale evaluation of computational protein function prediction
AU - Radivojac, Predrag
AU - Clark, Wyatt T.
AU - Oron, Tal Ronnen
AU - Schnoes, Alexandra M.
AU - Wittkop, Tobias
AU - Sokolov, Artem
AU - Graim, Kiley
AU - Funk, Christopher
AU - Verspoor, Karin
AU - Ben-Hur, Asa
AU - Pandey, Gaurav
AU - Yunes, Jeffrey M.
AU - Talwalkar, Ameet S.
AU - Repo, Susanna
AU - Souza, Michael L.
AU - Piovesan, Damiano
AU - Casadio, Rita
AU - Wang, Zheng
AU - Cheng, Jianlin
AU - Fang, Hai
AU - Gough, Julian
AU - Koskinen, Patrik
AU - Törönen, Petri
AU - Nokso-Koivisto, Jussi
AU - Holm, Liisa
AU - Cozzetto, Domenico
AU - Buchan, Daniel W.A.
AU - Bryson, Kevin
AU - Jones, David T.
AU - Limaye, Bhakti
AU - Inamdar, Harshal
AU - Datta, Avik
AU - Manjari, Sunitha K.
AU - Joshi, Rajendra
AU - Chitale, Meghana
AU - Kihara, Daisuke
AU - Lisewski, Andreas M.
AU - Erdin, Serkan
AU - Venner, Eric
AU - Lichtarge, Olivier
AU - Rentzsch, Robert
AU - Yang, Haixuan
AU - Romero, Alfonso E.
AU - Bhat, Prajwal
AU - Paccanaro, Alberto
AU - Hamp, Tobias
AU - Kaßner, Rebecca
AU - Seemayer, Stefan
AU - Vicedo, Esmeralda
AU - Schaefer, Christian
AU - Achten, Dominik
AU - Auer, Florian
AU - Boehm, Ariane
AU - Braun, Tatjana
AU - Hecht, Maximilian
AU - Heron, Mark
AU - Hönigschmid, Peter
AU - Hopf, Thomas A.
AU - Kaufmann, Stefanie
AU - Kiening, Michael
AU - Krompass, Denis
AU - Landerer, Cedric
AU - Mahlich, Yannick
AU - Roos, Manfred
AU - Björne, Jari
AU - Salakoski, Tapio
AU - Wong, Andrew
AU - Shatkay, Hagit
AU - Gatzmann, Fanny
AU - Sommer, Ingolf
AU - Wass, Mark N.
AU - Sternberg, Michael J.E.
AU - Škunca, Nives
AU - Supek, Fran
AU - Bošnjak, Matko
AU - Panov, Panče
AU - Džeroski, Sašo
AU - Šmuc, Tomislav
AU - Kourmpetis, Yiannis A.I.
AU - Van Dijk, Aalt D.J.
AU - Ter Braak, Cajo J.F.
AU - Zhou, Yuanpeng
AU - Gong, Qingtian
AU - Dong, Xinran
AU - Tian, Weidong
AU - Falda, Marco
AU - Fontana, Paolo
AU - Lavezzo, Enrico
AU - Di Camillo, Barbara
AU - Toppo, Stefano
AU - Lan, Liang
AU - Djuric, Nemanja
AU - Guo, Yuhong
AU - Vucetic, Slobodan
AU - Bairoch, Amos
AU - Linial, Michal
AU - Babbitt, Patricia C.
AU - Brenner, Steven E.
AU - Orengo, Christine
AU - Rost, Burkhard
AU - Mooney, Sean D.
AU - Friedberg, Iddo
N1 - Funding Information: We gratefully acknowledge I. Landsberg-Halperin for coining the term “CAFA,” T. Theriault for the initial graphical design of Figure 1, G. Schuster for illuminating discussions on hPNPase and A. Facchinetti, R. Velasco, E. Cilia, D.A. Lee, P. Vats, R. Banerjee and A. Bayaskar for their participation in various individual projects. The Automated Function Prediction Special Interest Group meeting at the ISMB 2011 conference was supported by the US National Institutes of Health (NIH) grant R13 HG006079-01A1 (P.R.) and Office of Science (Biological and Environmental Research), US Department of Energy (DOE BER), grant DE-SC0006807TDD (I.F.). Individual projects were partially supported by the following awards: US National Science Foundation (NSF) DBI-0644017 (P.R.), ABI-0965768 (A.B.-H.), DMS0800568 (D. Kihara), CCF-0905536 and DBI-1062455 (O.L.), DBI-0965768 (K.V.) and ABI-1146960 (I.F.); Marie Curie International Outgoing Fellowship PIOF-QA-2009-237751 (S.R.); PRIN 2009 project 009WXT45Y Italian Ministry for University and Research MIUR (R.C.); NIH GM093123 (J.C.), GM075004 and GM097528 (D. Kihara), GM079656 and GM066099 (O.L.), LM00945102 (C.F.), R01 GM071749 (S.E.B.) and LM009722 and HG004028 (S.D.M.); FP7 “Infrastructures” project TransPLANT Award 283496 (A.D.J.v.D.); UK Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/G022771/1 (J.G.), BB/K004131/1 (A.P.) and BB/F020481/1 (M.N.W. and M.J.E.S.); BBSRC (D.T.J.); Marie Curie Intra European Fellowship Award PIEF-GA-2009-237292 (D.T.J.); Department of Information Technology, Government of India (R.J.); EU, BBSRC and NIH Awards (C.O.); Natural Sciences and Engineering Research Council of Canada Discovery Award #298292-2009, Discovery Accelerator Award #380478-2009, Canada Foundation for Innovation New Opportunities Award 10437 and Ontario’s Early Researcher Award #ER07-04-085 (H.S.); Netherlands Genomics Initiative (Y.A.I.K. and C.J.F.t.B.); National Information and Communication Technology Australia (K.V.); National Natural Science Foundation of China grants 31071113 and 30971643 (W.T.); DOE BER KP110201 (S.E.B.); and Alexander von Humboldt Foundation (B.R.). P.R. acknowledges the Indiana University high-performance computing resources (NSF grant CNS-0723054). I.F. acknowledges the assistance of the high-performance computing group at Miami University.
PY - 2013/3
Y1 - 2013/3
N2 - Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
AB - Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
UR - http://www.scopus.com/inward/record.url?scp=84874663959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874663959&partnerID=8YFLogxK
U2 - 10.1038/nmeth.2340
DO - 10.1038/nmeth.2340
M3 - Article
C2 - 23353650
AN - SCOPUS:84874663959
VL - 10
SP - 221
EP - 227
JO - PLoS Medicine
JF - PLoS Medicine
SN - 1549-1277
IS - 3
ER -