Abstract
BackgroundA 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.ResultsWe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1037-6) contains supplementary material, which is available to authorized users.
Highlights
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins
Case study: ADAM-TS12 To illustrate some of the challenges and accomplishments of critical assessment of functional annotation (CAFA), we provide an in-depth examination of the prediction of the functional terms of one protein, human ADAM-TS12 [16]
Advances are valuable: accurate predictions allow biologists to rapidly generate testable hypotheses about how proteins fit into processes and pathways
Summary
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. Accurate computer-generated functional annotations of biological macromolecules allow biologists to rapidly generate testable hypotheses about the roles that newly identified proteins play in processes or pathways. They allow them to reason about new species based on the observed functional repertoire associated with their genes. In 2010–2011, we organized the first critical assessment of functional annotation (CAFA) challenge to evaluate methods for the automated annotation of protein function and to assess the progress in method development in the first decade of the 2000s [2]. The challenge used a time-delayed evaluation of predictions for a large set of target proteins without any experimental functional annotation. The estimated performance was subsequently used to draw conclusions about the status of the field
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