An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Creators: name: Jiang, Yuxiang
name: Oron, Tal Ronnen
name: Clark, Wyatt T
name: Bankapur, Asma R
name: D'Andrea, Daniel
name: Lepore, Rosalba
name: Funk, Christopher S
name: Kahanda, Indika
name: Verspoor, Karin M
name: Ben-Hur, Asa
name: Koo, Emily
name: Penfold-Brown, Duncan
name: Shasha, Dennis
name: Youngs, Noah
name: Bonneau, Richard
name: Lin, Alexandra
name: Sahraeian, Sayed ME
name: Martelli, Pier Luigi
name: Profiti, Giuseppe
name: Casadio, Rita
name: Cao, Renzhi
name: Zhong, Zhaolong
name: Cheng, Jianlin
name: Altenhoff, Adrian
name: Skunca, Nives
name: Dessimoz, Christophe
name: Dogan, Tunca
name: Hakala, Kai
name: Kaewphan, Suwisa
name: Mehryary, Farrokh
name: Salakoski, Tapio
name: Ginter, Filip
name: Fang, Hai
name: Smithers, Ben
name: Oates, Matt
name: Gough, Julian
name: Törönen, Petri
name: Koskinen, Patrik
name: Holm, Liisa
name: Chen, Ching-Tai
name: Hsu, Wen-Lian
name: Bryson, Kevin
name: Cozzetto, Domenico
name: Minneci, Federico
name: Jones, David T
name: Chapman, Samuel
name: C., Dukka B K.
name: Khan, Ishita K
name: Kihara, Daisuke
name: Ofer, Dan
name: Rappoport, Nadav
name: Stern, Amos
name: Cibrian-Uhalte, Elena
name: Denny, Paul
name: Foulger, Rebecca E
name: Hieta, Reija
name: Legge, Duncan
name: Lovering, Ruth C
name: Magrane, Michele
name: Melidoni, Anna N
name: Mutowo-Meullenet, Prudence
name: Pichler, Klemens
name: Shypitsyna, Aleksandra
name: Li, Biao
name: Zakeri, Pooya
name: ElShal, Sarah
name: Tranchevent, Léon-Charles
name: Das, Sayoni
name: Dawson, Natalie L
name: Lee, David
name: Lees, Jonathan G
name: Sillitoe, Ian
name: Bhat, Prajwal
name: Nepusz, Tamás
name: Romero, Alfonso E
name: Sasidharan, Rajkumar
name: Yang, Haixuan
name: Paccanaro, Alberto
name: Gillis, Jesse
name: Sedeño-Cortés, Adriana E
name: Pavlidis, Paul
name: Feng, Shou
name: Cejuela, Juan M
name: Goldberg, Tatyana
name: Hamp, Tobias
name: Richter, Lothar
name: Salamov, Asaf
name: Gabaldon, Toni
name: Marcet-Houben, Marina
name: Supek, Fran
name: Gong, Qingtian
name: Ning, Wei
name: Zhou, Yuanpeng
name: Tian, Weidong
name: Falda, Marco
name: Fontana, Paolo
name: Lavezzo, Enrico
name: Toppo, Stefano
name: Ferrari, Carlo
name: Giollo, Manuel
name: Piovesan, Damiano
name: Tosatto, Silvio
name: del Pozo, Angela
name: Fernández, José M
name: Maietta, Paolo
name: Valencia, Alfonso
name: Tress, Michael L
name: Benso, Alfredo
name: Di Carlo, Stefano
name: Politano, Gianfranco
name: Savino, Alessandro
name: Rehman, Hafeez Ur
name: Re, Matteo
name: Mesiti, Marco
name: Valentini, Giorgio
name: Bargsten, Joachim W
name: van Dijk, Aalt DJ
name: Gemovic, Branislava
name: Glisic, Sanja
name: Perovic, Vladmir
name: Veljkovic, Veljko
name: Veljkovic, Nevena
name: Almeida-e-Silva, Danillo C
name: Vencio, Ricardo ZN
name: Sharan, Malvika
name: Vogel, Jörg
name: Kansakar, Lakesh
name: Zhang, Shanshan
name: Vucetic, Slobodan
name: Wang, Zheng
name: Sternberg, Michael JE
name: Wass, Mark N
name: Huntley, Rachael P
name: Martin, Maria J
name: O'Donovan, Claire
name: Robinson, Peter N
name: Moreau, Yves
name: Tramontano, Anna
name: Babbitt, Patricia C
name: Brenner, Steven E
name: Linial, Michal
name: Orengo, Christine A
name: Rost, Burkhard
name: Greene, Casey S
name: Mooney, Sean D
name: Friedberg, Iddo
name: Radivojac, Predrag
Publisher: name: arXiv
Year: 2016
Subjects: subject: Quantitative Methods (q-bio.QM)
subject: FOS: Biological sciences
subject: FOS: Biological sciences

description: 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.
description: Submitted to Genome Biology