Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines
Issue No. 06 - Nov.-Dec. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.116
W. Ashlock , Dept. of Comput. Sci. & Eng., York Univ., Toronto, ON, Canada
S. Datta , Dept. of Comput. Sci. & Eng., York Univ., Toronto, ON, Canada
Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus sequences and genomic sequences for training classifiers and find that more accurate results can be obtained using genomic sequences. Surprisingly, we were even able to build a classifier that distinguished consensus sequences from genomic sequences with high accuracy, suggesting that consensus sequences are not always representative of their genomic counterparts. We apply our techniques to the problem of distinguishing two types of transposable elements, solo LTRs and SINEs. Identifying these sequences is important because they affect gene expression, genome structure, and genetic diversity, and they serve as genetic markers. They are of similar length, neither codes for protein, and both have many nearly identical copies throughout the genome. Being able to efficiently and automatically distinguish them will aid efforts to improve annotations of genomes. Our approach reveals structural characteristics of the sequences of potential interest to biologists.
Bioinformatics, Genomics, Genetic algorithms, DNA, Feature extraction, Machine learning,side effect machines, Endogenous retroviruses, LTR retrotransposons, SINE elements, feature evaluation and selection, machine learning, evolutionary computing and genetic algorithms
W. Ashlock, S. Datta, "Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1676-1689, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.116