Genetic algorithm learning as a robust approach to RNA editing site prediction

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 L...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: James Thompson, Shuba Gopal
• Keywords: BMCBI 2006, Genetic Algorithms, Genomes, RNA Editing