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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

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, Da Chen Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed M. E. 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 Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T. Jones, Samuel Chapman, Dukka BKC, 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, Léon-Charles Tranchevent, Sayoni Das, Natalie L. Dawson, David Lee, Jonathan G. Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E. Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E. Sedeño-Cortés, 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, Damiano Piovesan, Silvio C.E. Tosatto, Angela del Pozo, José M. Fernández, Paolo Maietta, Alfonso Valencia, Michael L. Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W. Bargsten, Aalt D. J. van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C. Almeida-e-Silva, Ricardo Z. N. Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael J. E. Sternberg, Mark N. Wass, Rachael P. Huntley, Maria J. Martin, Claire O’Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C. Babbitt, Steven E. Brenner, Michal Linial, Christine A. Orengo, Burkhard Rost, Casey S. Greene, Sean D. Mooney, Iddo Friedberg, Predrag Radivojac

ABSTRACT

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to 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, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. More... »

PAGES

184

Journal

TITLE

Genome Biology

ISSUE

1

VOLUME

17

Author Affiliations

  • Indiana University Bloomington
  • Buck Institute for Research on Aging
  • Yale University
  • Miami University
  • Sapienza University of Rome
  • University of Colorado Anschutz Medical Campus
  • Colorado State University
  • University of Melbourne
  • New York University
  • University of California, Berkeley
  • University of Bologna
  • University of Missouri
  • Swiss Institute of Bioinformatics
  • European Bioinformatics Institute
  • University of Turku
  • Turku Centre for Computer Science
  • University of Bristol
  • University of Helsinki
  • Academia Sinica
  • University College London
  • North Carolina Agricultural and Technical State University
  • Purdue University
  • Hebrew University of Jerusalem
  • KU Leuven
  • Centre Léon Bérard
  • Molde University College
  • Royal Holloway University of London
  • University of California Los Angeles
  • National University of Ireland, Galway
  • Cold Spring Harbor Laboratory
  • University of British Columbia
  • Technical University Munich
  • Joint Genome Institute
  • Institució Catalana de Recerca i Estudis Avançats
  • Pompeu Fabra University
  • Centre for Genomic Regulation
  • Children's Hospital of Fudan University
  • University of Padua
  • Fondazione Edmund Mach
  • Hospital Universitario La Paz
  • Polytechnic University of Turin
  • National University of Computer and Emerging Sciences
  • University of Milan
  • Wageningen University & Research
  • University of Belgrade
  • University of Sao Paulo
  • University of Würzburg
  • Temple University
  • University of Southern Mississippi
  • Imperial College London
  • University of Kent
  • Charité
  • University of California, San Francisco
  • University of Pennsylvania
  • University of Washington
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-016-1037-6

    DOI

    http://dx.doi.org/10.1186/s13059-016-1037-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1014881435

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27604469


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