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rRNASelector: a computer program for selecting ribosomal RNA encoding sequences from metagenomic and
Jae-Hak Lee1, Hana Yi, Jongsik Chun
1Interdisciplinary Graduate Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea.
Journal of Microbiology (Seoul, Korea)
|September 3, 2011
Summary
rRNASelector is a new computer program that uses Hidden Markov Models to efficiently identify ribosomal RNA (rRNA) genes in large metagenomic and metatranscriptomic datasets, improving phylogenetic analysis.
Area of Science:
- Bioinformatics
- Genomics
- Microbial Ecology
Background:
- Metagenomic and metatranscriptomic sequencing are increasingly cost-effective.
- Identifying ribosomal RNA (rRNA) genes is a crucial first step in analyzing these massive datasets.
- Existing methods for rRNA gene identification can be computationally intensive.
Purpose of the Study:
- To introduce rRNASelector, a novel computational tool for identifying rRNA genes.
- To leverage Hidden Markov Models (HMMs) for accurate rRNA gene detection.
- To facilitate downstream protein-based analysis by efficiently filtering rRNA sequences.
Main Methods:
- Development of rRNASelector, a computer program utilizing HMMs.
- Training HMMs on curated rRNA sequence databases.
- Application of rRNASelector to prokaryotic metagenomic and metatranscriptomic data.
Main Results:
- rRNASelector successfully identified prokaryotic 5S, 26S, and 23S rRNA genes.
- The program demonstrated effectiveness on data from Roche 454 FLX Titanium sequencing.
- rRNASelector provides a robust method for selecting rRNA genes from large sequence datasets.
Conclusions:
- rRNASelector is an efficient and accurate tool for identifying rRNA genes in metagenomic and metatranscriptomic data.
- The program aids in the bioinformatic analysis of microbial communities.
- rRNASelector is publicly available for research use.
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