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

Chitale M, Khan IK, Kihara D - BMC Bioinformatics (2013)

Bottom Line: The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011.Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST.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.

View Article: PubMed Central - HTML - PubMed

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

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.

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Related in: MedlinePlus

Prediction accuracy evaluated for each functional category. Each row represents a GO term category and each column represents a prediction method. Count refers to the number of target proteins that were annotated by the given a GO term in the category. The F1 measure was used for evaluation. The color ranges from white (minimum) to red (maximum score). A, the BP domain. Results of a sample of 20 terms are shown, which are taken out of the 77 BP terms annotating 25 or more targets. B, the MF domain. Results for 11 MF terms each annotating 25 or more targets are shown.
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Figure 4: Prediction accuracy evaluated for each functional category. Each row represents a GO term category and each column represents a prediction method. Count refers to the number of target proteins that were annotated by the given a GO term in the category. The F1 measure was used for evaluation. The color ranges from white (minimum) to red (maximum score). A, the BP domain. Results of a sample of 20 terms are shown, which are taken out of the 77 BP terms annotating 25 or more targets. B, the MF domain. Results for 11 MF terms each annotating 25 or more targets are shown.

Mentions: In Figure 4, we analyze the prediction accuracy for different GO terms. Only GO terms and their child terms that are used for annotating 25 or more targets are considered. This results in 77 BP terms and 11 MF terms for this analysis.


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

Chitale M, Khan IK, Kihara D - BMC Bioinformatics (2013)

Prediction accuracy evaluated for each functional category. Each row represents a GO term category and each column represents a prediction method. Count refers to the number of target proteins that were annotated by the given a GO term in the category. The F1 measure was used for evaluation. The color ranges from white (minimum) to red (maximum score). A, the BP domain. Results of a sample of 20 terms are shown, which are taken out of the 77 BP terms annotating 25 or more targets. B, the MF domain. Results for 11 MF terms each annotating 25 or more targets are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3584938&req=5

Figure 4: Prediction accuracy evaluated for each functional category. Each row represents a GO term category and each column represents a prediction method. Count refers to the number of target proteins that were annotated by the given a GO term in the category. The F1 measure was used for evaluation. The color ranges from white (minimum) to red (maximum score). A, the BP domain. Results of a sample of 20 terms are shown, which are taken out of the 77 BP terms annotating 25 or more targets. B, the MF domain. Results for 11 MF terms each annotating 25 or more targets are shown.
Mentions: In Figure 4, we analyze the prediction accuracy for different GO terms. Only GO terms and their child terms that are used for annotating 25 or more targets are considered. This results in 77 BP terms and 11 MF terms for this analysis.

Bottom Line: The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011.Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST.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.

View Article: PubMed Central - HTML - PubMed

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

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.

Show MeSH
Related in: MedlinePlus