  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.
