Briefings in Bioinformatics Advance Access originally published online on May 23, 2006
Briefings in Bioinformatics 2006 7(3):225-242; doi:10.1093/bib/bbl004
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Automated protein function prediction—the genomic challenge
Iddo Friedberg, Burnham Institute for Medical Research, Program in Bioinformatics and Systems Biology, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA. E-mail: idoerg{at}burnham.org
Overwhelmed with genomic data, biologists are facing the first big post-genomic question—what do all genes do? First, not only is the volume of pure sequence and structure data growing, but its diversity is growing as well, leading to a disproportionate growth in the number of uncharacterized gene products. Consequently, established methods of gene and protein annotation, such as homology-based transfer, are annotating less data and in many cases are amplifying existing erroneous annotation. Second, there is a need for a functional annotation which is standardized and machine readable so that function prediction programs could be incorporated into larger workflows. This is problematic due to the subjective and contextual definition of protein function. Third, there is a need to assess the quality of function predictors. Again, the subjectivity of the term ‘function’ and the various aspects of biological function make this a challenging effort. This article briefly outlines the history of automated protein function prediction and surveys the latest innovations in all three topics.
Keywords: computational function prediction, genomic annotation, gene ontology, phylogenomics
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