Genetic Algorithm Learning As a Robust Approach to Rna Editing Site Prediction.
From: Department of Biological Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. tex@u.washington.edu
BMC bioinformatics
- Publish Date: 2006
- ISSN: 1471-2105
- Volume: 7
- Issue:
- Pages: 145
- Medium: Internet
- Language: English
- Citation (JAMA): Thompson James, Gopal Shuba, et al. Genetic Algorithm Learning As a Robust Approach to Rna Editing Site Prediction.. BMC Bioinformatics 2006;7:145
Abstract
BACKGROUND: RNA editing is one of several post-transcriptional modifications that may contribute to organismal complexity in the face of limited gene complement in a genome. One form, known as C — > U editing, appears to exist in a wide range of organisms, but most instances of this form of RNA editing have been discovered serendipitously. With the large amount of genomic and transcriptomic data now available, a computational analysis could provide a more rapid means of identifying novel sites of C — > U RNA editing. Previous efforts have had some success but also some limitations. We present a computational method for identifying C — > U RNA editing sites in genomic sequences that is both robust and generalizable. We evaluate its potential use on the best data set available for these purposes: C — > U editing sites in plant mitochondrial genomes. RESULTS: Our method is derived from a machine learning approach known as a genetic algorithm. REGAL (RNA Editing site prediction by Genetic Algorithm Learning) is 87% accurate when tested on three mitochondrial genomes, with an overall sensitivity of 82% and an overall specificity of 91%. REGAL’s performance significantly improves on other ab initio approaches to predicting RNA editing sites in this data set. REGAL has a comparable sensitivity and higher specificity than approaches which rely on sequence homology, and it has the advantage that strong sequence conservation is not required for reliable prediction of edit sites. CONCLUSION: Our results suggest that ab initio methods can generate robust classifiers of putative edit sites, and we highlight the value of combinatorial approaches as embodied by genetic algorithms. We present REGAL as one approach with the potential to be generalized to other organisms exhibiting C — > U RNA editing.
Mesh Headings (Keywords): Algorithms, Arabidopsis, Binding Sites, Brassica napus, Computational Biology, DNA, Mitochondrial, DNA, Plant, Genes, Plant, Genome, Plant, Models, Genetic, Oryza sativa, Predictive Value of Tests, RNA, RNA Editing, RNA Processing, Post-Transcriptional, ROC Curve, Reproducibility of Results, Software
Check for Full Text / PubMed Unique Identifier (PMID): 16542417
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