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This article is part of the supplement: Selected proceedings from the Automated Function Prediction Meeting 2011

Open Access Proceedings

Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)

Jesse Gillis1 and Paul Pavlidis2*

Author Affiliations

1 Stanley Institute for Cognitive Genomic, Cold Spring Harbor Laboratory, 196 Genome Research Center, 500 Sunnyside Boulevard Woodbury, NY, 11797, USA

2 Centre for High-Throughput Biology and Department of Psychiatry, University of British Columbia, 177 Michael Smith Laboratories 2185 East Mall, Vancouver, Canada, V6T1Z4

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BMC Bioinformatics 2013, 14(Suppl 3):S15  doi:10.1186/1471-2105-14-S3-S15

Published: 22 April 2013

Abstract

The assignment of gene function remains a difficult but important task in computational biology. The establishment of the first Critical Assessment of Functional Annotation (CAFA) was aimed at increasing progress in the field. We present an independent analysis of the results of CAFA, aimed at identifying challenges in assessment and at understanding trends in prediction performance. We found that well-accepted methods based on sequence similarity (i.e., BLAST) have a dominant effect. Many of the most informative predictions turned out to be either recovering existing knowledge about sequence similarity or were "post-dictions" already documented in the literature. These results indicate that deep challenges remain in even defining the task of function assignment, with a particular difficulty posed by the problem of defining function in a way that is not dependent on either flawed gold standards or the input data itself. In particular, we suggest that using the Gene Ontology (or other similar systematizations of function) as a gold standard is unlikely to be the way forward.