A large-scale evaluation of computational protein function prediction

Radivojac, P. et al. (2013) A large-scale evaluation of computational protein function prediction. Nature Methods, 10(3), pp. 221-227. (doi: 10.1038/nmeth.2340) (PMID:23353650) (PMCID:PMC3584181)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Bryson, Dr Kevin
Authors: 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., Fang, H., Gough, J., Koskinen, P., Törönen, P., Nokso-Koivisto, J., Holm, L., Cozzetto, D., Buchan, D. W. A., Bryson, K., Jones, D. T., Limaye, B., Inamdar, H., Datta, A., Manjari, S. K., Joshi, R., Chitale, M., Kihara, D., Lisewski, A. M., Erdin, S., Venner, E., Lichtarge, O., Rentzsch, R., Yang, H., Romero, A. E., 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, T. A., 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, M. N., Sternberg, M. J. E., Škunca, N., Supek, F., Bošnjak, M., Panov, P., Džeroski, S., Šmuc, T., Kourmpetis, Y. A. I., van Dijk, A. D. J., Braak, C. J. F. t., 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, P. C., Brenner, S. E., Orengo, C., Rost, B., Mooney, S. D., and Friedberg, I.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Nature Methods
Publisher:Nature Publishing Group
ISSN:1548-7091
ISSN (Online):1548-7105
Published Online:27 January 2013
Copyright Holders:Copyright © 2013 Nature America, Inc.
First Published:First published in Nature Methods 10(3): 221-227
Publisher Policy:Reproduced under a Creative Commons License

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