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BY-NC-ND 4.0 license Open Access Published by De Gruyter October 18, 2016

Improving the performance of Transposable Elements detection tools

Tiago Loureiro , Rui Camacho EMAIL logo , Jorge Vieira and Nuno A. Fonseca

Summary

Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single tool achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning constructed classifiers.

Published Online: 2016-10-18
Published in Print: 2013-12-1

© 2013 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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