A large-scale evaluation of computational protein function prediction

Predrag Radivojac*, Wyatt T. Clark, Tal Ronnen Oron, Alexandra M. Schnoes, Tobias Wittkop, Artem Sokolov, Kiley Graim, Christopher Funk, Karin Verspoor, Asa Ben-Hur, Gaurav Pandey, Jeffrey M. Yunes, Ameet S. Talwalkar, Susanna Repo, Michael L. Souza, Damiano Piovesan, Rita Casadio, Zheng Wang, Jianlin Cheng, Hai FangJulian Gough, Patrik Koskinen, Petri Törönen, Jussi Nokso-Koivisto, Liisa Holm, Domenico Cozzetto, Daniel W.A. Buchan, Kevin Bryson, David T. Jones, Bhakti Limaye, Harshal Inamdar, Avik Datta, Sunitha K. Manjari, Rajendra Joshi, Meghana Chitale, Daisuke Kihara, Andreas M. Lisewski, Serkan Erdin, Eric Venner, Olivier Lichtarge, Robert Rentzsch, Haixuan Yang, Alfonso E. Romero, Prajwal Bhat, Alberto Paccanaro, Tobias Hamp, Rebecca Kaßner, Stefan Seemayer, Esmeralda Vicedo, Christian Schaefer, Dominik Achten, Florian Auer, Ariane Boehm, Tatjana Braun, Maximilian Hecht, Mark Heron, Peter Hönigschmid, Thomas A. Hopf, Stefanie Kaufmann, Michael Kiening, Denis Krompass, Cedric Landerer, Yannick Mahlich, Manfred Roos, Jari Björne, Tapio Salakoski, Andrew Wong, Hagit Shatkay, Fanny Gatzmann, Ingolf Sommer, Mark N. Wass, Michael J.E. Sternberg, Nives Škunca, Fran Supek, Matko Bošnjak, Panče Panov, Sašo Džeroski, Tomislav Šmuc, Yiannis A.I. Kourmpetis, Aalt D.J. Van Dijk, Cajo J.F. Ter Braak, Yuanpeng Zhou, Qingtian Gong, Xinran Dong, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Barbara Di Camillo, Stefano Toppo, Liang Lan, Nemanja Djuric, Yuhong Guo, Slobodan Vucetic, Amos Bairoch, Michal Linial, Patricia C. Babbitt, Steven E. Brenner, Christine Orengo, Burkhard Rost, Sean D. Mooney, Iddo Friedberg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

664 Scopus citations

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 languageAmerican English
Pages (from-to)221-227
Number of pages7
JournalNature Methods
Volume10
Issue number3
DOIs
StatePublished - Mar 2013

Bibliographical note

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.

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