A large-scale evaluation of computational protein function prediction.
A large-scale evaluation of computational protein function prediction.
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计算蛋白质功能预测的大规模评估
DOI：
10.1038/nmeth.2340
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发表时间：
2013-03
期刊：
影响因子：
48
通讯作者：
Friedberg, Iddo
中科院分区：
文献类型：
--
作者：
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;Goughl, Julian;Koskinen, Patrik;Toronen, 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;Hoenigschmid, Peter;Hopf, Thomas A.;Kaufmann, Stefanie;Kiening, Michael;Krompass, Denis;Landerer, Cedric;Mahlich, Yannick;Roos, Manfred;Bjorne, Jari;Salakoski, Tapio;Wong, Andrew;Shatkay, Hagit;Gatzmann, Fanny;Sommer, Ingolf;Wass, Mark N.;Sternberg, Michael J. E.;Skunca, Nives;Supek, Fran;Bosnjak, Matko;Panov, Pance;Dzeroski, Saso;Smuc, 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
蛋白质功能的自动注释是一个挑战。随着测序基因组数量的快速增长，绝大多数蛋白质产物只能通过计算进行注释。如果要依靠计算预测，这些方法的准确性很高是至关重要的。在这里，我们报告了第一个大规模的基于社区的蛋白质功能注释（CAFA）实验的关键评估结果。代表蛋白质功能预测的最新技术水平的54种方法在来自11种生物体的866种蛋白质的目标集上进行了评价。两个发现突出：（i）今天最好的蛋白质功能预测算法大大优于广泛使用的第一代方法，在所有类型的目标上都有很大的收益;（ii）尽管顶级方法的表现足以指导实验，但目前可用的工具仍有相当大的改进需求。
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.
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