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.

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.L.; Fang, H.; Goughl, J.; Koskinen, P.; Toronen, 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.X.; 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.; Honigschmid, P.; Hopf, T.A.; Kaufmann, S.; Kiening, M.; Krompass, D.; Landerer, C.; Mahlich, Y.; Roos, M.; Bjorne, J.; Salakoski, T.; Wong, A.; Shatkay, H.; Gatzmann, F.; Sommer, I.; Wass, M.N.; Sternberg, M.J.E.; Skunca, N.; Supek, F.; Bosnjak, M.; Panov, P.; Dzeroski, S.; Smuc, T.; Kourmpetis, Y.A.I.; van Dijk, A.D.J.; ter Braak, C.J.F.; Zhou, Y.P.; Gong, Q.T.; Dong, X.R.; Tian, W.D.; Falda, M.; Fontana, P.; Lavezzo, E.; Di Camillo, B.; Toppo, S.; Lan, L.; Djuric, N.; Guo, Y.H.; Vucetic, S.; Bairoch, A.; Linial, M.; Babbitt, P.C.; Brenner, S.E.; Orengo, C.; Rost, B.; Mooney, S.D.; Friedberg, I. (2013). A large-scale evaluation of computational protein function prediction. NATURE METHODS, 10 (3): 221-227. doi: 10.1038/NMETH.2340 handle: http://hdl.handle.net/10449/22010

A large-scale evaluation of computational protein function prediction

Fontana, Paolo;
2013-01-01

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.
Settore BIO/11 - BIOLOGIA MOLECOLARE
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.L.; Fang, H.; Goughl, J.; Koskinen, P.; Toronen, 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.X.; 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.; Honigschmid, P.; Hopf, T.A.; Kaufmann, S.; Kiening, M.; Krompass, D.; Landerer, C.; Mahlich, Y.; Roos, M.; Bjorne, J.; Salakoski, T.; Wong, A.; Shatkay, H.; Gatzmann, F.; Sommer, I.; Wass, M.N.; Sternberg, M.J.E.; Skunca, N.; Supek, F.; Bosnjak, M.; Panov, P.; Dzeroski, S.; Smuc, T.; Kourmpetis, Y.A.I.; van Dijk, A.D.J.; ter Braak, C.J.F.; Zhou, Y.P.; Gong, Q.T.; Dong, X.R.; Tian, W.D.; Falda, M.; Fontana, P.; Lavezzo, E.; Di Camillo, B.; Toppo, S.; Lan, L.; Djuric, N.; Guo, Y.H.; Vucetic, S.; Bairoch, A.; Linial, M.; Babbitt, P.C.; Brenner, S.E.; Orengo, C.; Rost, B.; Mooney, S.D.; Friedberg, I. (2013). A large-scale evaluation of computational protein function prediction. NATURE METHODS, 10 (3): 221-227. doi: 10.1038/NMETH.2340 handle: http://hdl.handle.net/10449/22010
File in questo prodotto:
File Dimensione Formato  
2013 NM Radivojac et al.pdf

accesso aperto

Licenza: Creative commons
Dimensione 624.13 kB
Formato Adobe PDF
624.13 kB Adobe PDF Visualizza/Apri

Questo articolo è pubblicato sotto una Licenza Licenza Creative Commons Creative Commons

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/22010
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 580
  • ???jsp.display-item.citation.isi??? 114
social impact