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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

Additional files

Additional file 1:

Taxon-specific effects on annotation. The GO annotations used as evaluation targets were used (not predictions). For each sequence, a binary vector of GO annotations was created (1= sequence is annotated), and the correlation among these vectors is plotted, with lighter shades indicating high correlations. The sequences are organized by taxon, with the E. coli sequences indicated. It is evident that the E. coli sequences have very high correlations in their annotations in BP (A), very low correlations in MF (B) and consistently high depth (number of terms assigned per sequence within the MFO; C). Depth of coverage exhibits no visually clear trend for E. coli within BPO, but is significantly depressed relative to other species (p<10-6, ranksum test).

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