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Published on: November 12, 2012
Automatic assignment of prokaryotic genes to functional categories using literature profiling
Raul Torrieri1, Francislon S Oliveira, Guilherme Oliveira
1Center for Excellence in Bioinformatics, FIOCRUZ-Minas, Belo Horizonte, Brasil.
Plos One
|October 19, 2012
Summary
Scientists developed a new classifier to automatically assign functions to genes using their associated literature. This tool successfully categorized thousands of previously unclassified genes, improving genome annotation accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Exponential growth in publicly available genomes has outpaced manual annotation review.
- Current gene annotation relies heavily on sequence similarity, leading to potential inaccuracies and unassigned genes.
- A significant number of predicted genes lack functional categorization despite available literature evidence.
Purpose of the Study:
- To develop an automated method for assigning functional categories to genes based on their associated scientific literature.
- To address the challenge of unassigned genes in large genomic datasets.
- To improve the accuracy and efficiency of genome annotation.
Main Methods:
- A classifier was trained using term-frequency vectors derived from text corpora of genes within the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR) ontology.
- The classifier utilized literature associated with genes to predict functional categories.
- Performance was evaluated on an independent set of 2,220 genes from 13 bacterial species.
Main Results:
- The classifier achieved 84% precision and 68% recall (F-measure 0.76) on an independent test set.
- Over 5,000 previously unclassified genes with MEDLINE literature were assigned functional categories with high confidence (≥0.7).
- Manual review by biologists confirmed the accuracy of the automated functional assignments for a subset of genes.
Conclusions:
- Automated literature profiling is a viable and effective method for assigning functions to genes.
- The developed classifier significantly enhances the functional annotation of genomic data.
- This approach can help overcome limitations in genome project funding for manual annotation review.
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