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Combining gene prediction methods to improve metagenomic gene annotation
1Genomic Signal Processing Laboratory, Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA. ng39@drexel.edu
BMC Bioinformatics
|January 15, 2011
Summary
Combining gene prediction programs improves metagenomic annotation accuracy, especially for short DNA reads. A consensus approach enhances performance for shorter reads, while specific program intersections work best for longer ones.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Traditional gene annotation struggles with short DNA reads from next-generation sequencing, particularly in complex metagenomic samples.
- Newer programs optimize gene prediction for short reads, but performance varies.
Purpose of the Study:
- To benchmark three metagenomic gene prediction programs.
- To combine predictions from multiple programs to enhance metagenomic gene annotation accuracy.
Main Methods:
- Analyzed program performance across various read lengths (100 bp, 200 bp, etc.).
- Separated analysis for intra- and intergenic regions.
- Evaluated different combination strategies (consensus, intersection) for gene start/stop annotation.
Main Results:
- Combined predictions significantly improved specificity with minimal impact on sensitivity.
- Accuracy improved by 4% for 100 bp reads and ~1% for longer reads.
- Consensus prediction excelled for shorter reads; unanimous agreement for longer reads, boosting accuracy by 1-8%.
Conclusions:
- A consensus of all methods is optimal for reads <= 400 bp.
- Intersection of GeneMark and Orphelia predictions is best for reads >= 500 bp.
- Methods predicted over 80% coding reads on a human gut sample dataset.
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