Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Original publication

DOI

10.1038/nmeth.2340

Type

Journal article

Journal

Nature methods

Publication Date

03/2013

Volume

10

Pages

221 - 227

Addresses

School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA.

Keywords

Animals, Humans, Exoribonucleases, Proteins, Computational Biology, Molecular Biology, Species Specificity, Algorithms, Forecasting, Databases, Protein, Molecular Sequence Annotation