A machine learning based framework to identify and classify long terminal repeat retrotransposons.

ABSTRACT: Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution. We introduce TE-Learner, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by having long terminal repeats (LTRs) at their boundaries. We evaluate the predictive performance of our frame

SUBMITTER: Schietgat L 

PROVIDER: S-EPMC5933816 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature