An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Gemović, Branislava S.
Perović, Vladimir R.
Veljković, Nevena V.
(ukupan broj autora: 147)
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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 fi...eld, 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.
Keywords:Protein function prediction / Disease gene prioritization
Source:Genome Biology, 2016, 17
- MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (EU-612944)
- Application of the EIIP/ISM bioinformatics platform in discovery of novel therapeutic targets and potential therapeutic molecules (RS-173001)
- TRANSPLANT - Trans-national Infrastructure for Plant Genomic Science (EU-283496)
- National Science Foundation [DBI-1458477, DBI-1458443, DBI-1458390, DBI-1458359, IIS-1319551, DBI-1262189, DBI-1149224], National Institutes of Health [R01GM093123, R01GM097528, R01GM076990, R01GM071749, R01LM009722, UL1TR000423], National Natural Science Foundation of China [3147124, 91231116], National Basic Research Program of China [2012CB316505], NSERC [RGPIN 371348-11], Microsoft Research/FAPESP grant [2009/53161-6], FAPESP [2010/50491-1], Biotechnology and Biological Sciences Research Council [BB/L020505/1, BB/F020481/1, BB/K004131/1, BB/F00964X/1, BB/L018241/1], Spanish Ministry of Economics and Competitiveness [BIO2012-40205], KU Leuven [CoE PFV/10/016 SymBioSys], Newton International Fellowship Scheme of the Royal Society grant [NF080750], Gordon and Betty Moore Foundations Data-Driven Discovery Initiative grant [GBMF4552], Academy of Finland, British Heart Foundation [RG/13/5/30112], Parkinsons UK [G-1307], Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research grant [DE-AC02-05CH11231], Australian Research Council grant [DP150101550], NIH [T15 LM00945102], FP7 REGPOT grant InnoMol, University of Padova [CPDA138081/13, GRIC13AAI9], Swiss National Science Foundation , UK BBSRC grant [BB/M015009/1], ICREA