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An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Created on 3rd January 2016

Yuxiang Jiang; Tal Ronnen Oron; Wyatt T Clark; Asma R Bankapur; Daniel D' Andrea; Rosalba Lepore; Christopher S Funk; Indika Kahanda; Karin M Verspoor; Asa Ben-Hur; Emily Koo; Duncan Penfold-Brown; Dennis Shasha; Noah Youngs; Richard Bonneau; Alexandra Lin; Sayed ME Sahraeian; Pier Luigi Martelli; Giuseppe Profiti; Rita Casadio; Renzhi Cao; Zhaolong Zhong; Jianlin Cheng; Adrian Altenhoff; Nives Skunca; Christophe Dessimoz; Tunca Dogan; Kai Hakala; Suwisa Kaewphan; Farrokh Mehryary; Tapio Salakoski; Filip Ginter; Hai Fang; Ben Smithers; Matt Oates; Julian Gough; Petri Tö rö nen; Patrik Koskinen; Liisa Holm; Ching-Tai Chen; Wen-Lian Hsu; Kevin Bryson; Domenico Cozzetto; Federico Minneci; David T Jones; Samuel Chapman; Dukka B K. C.; Ishita K Khan; Daisuke Kihara; Dan Ofer; Nadav Rappoport; Amos Stern; Elena Cibrian-Uhalte; Paul Denny; Rebecca E Foulger; Reija Hieta; Duncan Legge; Ruth C Lovering; Michele Magrane; Anna N Melidoni; Prudence Mutowo-Meullenet; Klemens Pichler; Aleksandra Shypitsyna; Biao Li; Pooya Zakeri; Sarah ElShal; Lé on-Charles Tranchevent; Sayoni Das; Natalie L Dawson; David Lee; Jonathan G Lees; Ian Sillitoe; Prajwal Bhat; Tamá s Nepusz; Alfonso E Romero; Rajkumar Sasidharan; Haixuan Yang; Alberto Paccanaro; Jesse Gillis; Adriana E Sedeñ o-Corté s; Paul Pavlidis; Shou Feng; Juan M Cejuela; Tatyana Goldberg; Tobias Hamp; Lothar Richter; Asaf Salamov; Toni Gabaldon; Marina Marcet-Houben; Fran Supek; Qingtian Gong; Wei Ning; Yuanpeng Zhou; Weidong Tian; Marco Falda; Paolo Fontana; Enrico Lavezzo; Stefano Toppo; Carlo Ferrari; Manuel Giollo;

Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction. Comments: Submitted to Genome Biology Subjects: Quantitative Methods (q-bio.QM) Cite as: arXiv:1601.00891 [q-bio.QM]   (or arXiv:1601.00891v1 [q-bio.QM] for this version)

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