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Title | A large-scale evaluation of computational protein function prediction. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Radivojac, P, Clark, WT, Oron, TRonnen, Schnoes, AM, Wittkop, T, Sokolov, A, Graim, K, Funk, C, Verspoor, K, Ben-Hur, A, Pandey, G, Yunes, JM, Talwalkar, AS, Repo, S, Souza, ML, Piovesan, D, Casadio, R, Wang, Z, Cheng, J, Fang, H, Gough, J, Koskinen, P, Törönen, P, Nokso-Koivisto, J, Holm, L, Cozzetto, D, Buchan, DWA, Bryson, K, Jones, DT, Limaye, B, Inamdar, H, Datta, A, Manjari, SK, Joshi, R, Chitale, M, Kihara, D, Lisewski, AM, Erdin, S, Venner, E, Lichtarge, O, Rentzsch, R, Yang, H, Romero, AE, Bhat, P, Paccanaro, A, Hamp, T, Kaßner, R, Seemayer, S, Vicedo, E, Schaefer, C, Achten, D, Auer, F, Boehm, A, Braun, T, Hecht, M, Heron, M, Hönigschmid, P, Hopf, TA, Kaufmann, S, Kiening, M, Krompass, D, Landerer, C, Mahlich, Y, Roos, M, Björne, J, Salakoski, T, Wong, A, Shatkay, H, Gatzmann, F, Sommer, I, Wass, MN, Sternberg, MJE, Škunca, N, Supek, F, Bošnjak, M, Panov, P, Džeroski, S, Šmuc, T, Kourmpetis, YAI, van Dijk, ADJ, Braak, CJF ter, Zhou, Y, Gong, Q, Dong, X, Tian, W, Falda, M, Fontana, P, Lavezzo, E, Di Camillo, B, Toppo, S, Lan, L, Djuric, N, Guo, Y, Vucetic, S, Bairoch, A, Linial, M, Babbitt, PC, Brenner, SE, Orengo, C, Rost, B, Mooney, SD, Friedberg, I |
Journal | Nat Methods |
Volume | 10 |
Issue | 3 |
Pagination | 221-7 |
Date Published | 2013 Mar |
ISSN | 1548-7105 |
Keywords | Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity |
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. |
DOI | 10.1038/nmeth.2340 |
Alternate Journal | Nat. Methods |
PubMed ID | 23353650 |
PubMed Central ID | PMC3584181 |
Grant List | BB/F020481/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom BB/G022771/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom BB/K004131/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom GM066099 / GM / NIGMS NIH HHS / United States GM075004 / GM / NIGMS NIH HHS / United States GM079656 / GM / NIGMS NIH HHS / United States GM093123 / GM / NIGMS NIH HHS / United States GM097528 / GM / NIGMS NIH HHS / United States HG004028 / HG / NHGRI NIH HHS / United States LM00945102 / LM / NLM NIH HHS / United States LM009722 / LM / NLM NIH HHS / United States R01 GM060595 / GM / NIGMS NIH HHS / United States R01 GM066099 / GM / NIGMS NIH HHS / United States R01 GM071749 / GM / NIGMS NIH HHS / United States R01 GM071749 / GM / NIGMS NIH HHS / United States R01 GM075004 / GM / NIGMS NIH HHS / United States R01 GM079656 / GM / NIGMS NIH HHS / United States R01 GM093123 / GM / NIGMS NIH HHS / United States R01 GM097528 / GM / NIGMS NIH HHS / United States R01 LM009722 / LM / NLM NIH HHS / United States R13 HG006079 / HG / NHGRI NIH HHS / United States R13 HG006079-01A1 / HG / NHGRI NIH HHS / United States U54 HG004028 / HG / NHGRI NIH HHS / United States |