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Comparison of statistical methods to classify environmental genomic fragments
Gail L Rosen1, Steven D Essinger
1Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA. gailr@ece.drexel.edu
IEEE Transactions on Nanobioscience
|September 30, 2010
Summary
Naïve Bayes classifiers (NBC) offer accurate DNA sequence read binning comparable to BLAST. Statistical language models enhance accuracy with limited data, crucial for environmental DNA analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Taxonomic classification of DNA sequence reads, or binning, is essential for analyzing environmental biological samples.
- BLAST, a homology-based tool, is widely used but faces challenges with growing genome databases.
- Alternative methods are needed to maintain classification accuracy and efficiency.
Purpose of the Study:
- To compare the accuracy of a naïve Bayes classifier (NBC) and statistical language models against BLAST for DNA read binning.
- To evaluate the performance of NBC and language models with varying data availability and taxonomic levels.
Main Methods:
- Comparative analysis of taxonomic classification accuracy using BLAST, NBC, and a back-off n-gram language model.
- Fivefold cross-validation was employed to assess performance across different taxonomic ranks (species, genus, phyla).
- Optimization of NBC by adjusting word feature size was explored, particularly for partial training datasets.
Main Results:
- NBC demonstrated good performance with low computational complexity, comparable to BLAST.
- The back-off n-gram language model improved accuracy when using partial training data.
- NBC outperformed BLAST in species-level classification, while BLAST was superior at genus and phyla levels.
Conclusions:
- NBC is a competitive and efficient taxonomic classifier for DNA sequence reads.
- Statistical language models offer a valuable approach to enhance classification accuracy, especially with incomplete datasets.
- The choice of method may depend on the specific taxonomic resolution required for environmental sample analysis.
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