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

Open Access Proceedings

In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment

Meghana Chitale1, Ishita K Khan1 and Daisuke Kihara12*

Author Affiliations

1 Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, Indiana, 47907, USA

2 Department of Biological Sciences, Purdue University, 915 W. State Street, West Lafayette, Indiana, 47907, USA

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BMC Bioinformatics 2013, 14(Suppl 3):S2  doi:10.1186/1471-2105-14-S3-S2

Published: 28 February 2013

Abstract

Background

Many Automatic Function Prediction (AFP) methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA) is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA.

Results

We evaluate PFP and ESG using four different measures in comparison with BLAST, Prior, and GOtcha. In addition to the predictions submitted to CAFA, we further investigate performance of a different scoring function to rank order predictions by PFP as well as PFP/ESG predictions enriched with Priors that simply adds frequently occurring Gene Ontology terms as a part of predictions. Prediction accuracies of each method were also evaluated separately for different functional categories. Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST.

Conclusion

The in-depth analysis discussed here will complement the overall assessment by the CAFA organizers. Since PFP and ESG are based on sequence database search results, our analyses are not only useful for PFP and ESG users but will also shed light on the relationship of the sequence similarity space and functions that can be inferred from the sequences.