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

Research output: Contribution to journalArticle

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

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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    Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., Pandey, G., Yunes, J. M., Talwalkar, A. S., Repo, S., Souza, M. L., Piovesan, D., Casadio, R., Wang, Z., Cheng, J., ... Friedberg, I. (2013). A large-scale evaluation of computational protein function prediction. Nature Methods, 10(3), 221-227. https://doi.org/10.1038/nmeth.2340