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Automated methods of predicting the function of biological sequences using GO and BLAST
Craig E Jones1, Ute Baumann, Alfred L Brown
1Australian Centre for Plant Functional Genomics, University of Adelaide, South Australia, 5064, Australia. craig@cs.adelaide.edu.au
BMC Bioinformatics
|November 18, 2005
Summary
Automated biological function prediction for genomic sequences is crucial. Accuracy benchmarking effectively compares prediction methods, with data mining yielding the most accurate results for Gene Ontology (GO) annotation.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Increasing genomic data necessitates automated, accurate methods for predicting biological functions of novel sequences.
- Gene Ontology (GO) provides a structured vocabulary for annotating sequences with biological context.
- Evaluating competing function prediction designs requires robust accuracy benchmarking.
Purpose of the Study:
- To demonstrate accuracy benchmarking for evaluating biological function predictors.
- To assess the impact of term distance on accuracy scores.
- To compare various sequence annotation methods using accuracy benchmarking.
Main Methods:
- Utilized Gene Ontology (GO) for functional annotation of sequences.
- Employed Basic Local Alignment Search Tool (BLAST) to find similar annotated sequences.
- Developed and compared several annotation methods based on terms from top BLAST matches against a 'best BLAST' benchmark.
Main Results:
- Precision and recall increase with allowed distance between predicted and true GO terms.
- Accuracy benchmarking effectively compares annotation methods.
- A discriminant function method showed superior precision and recall compared to other tested approaches, including the 'best BLAST' method.
Conclusions:
- Requiring exact term matches for accuracy improves reliability over methods allowing related terms.
- Accuracy benchmarking is effective for comparing BLAST-based GO annotator designs.
- Data mining techniques produced the most accurate annotation method, highlighting its utility for developing high-quality annotators.
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