Improving the performance of Transposable Elements detection tools

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Loureiro, T (Autor) Outra; Rui Camacho (Autor) FEUP; Vieira, J (Autor) FCUP; Nuno A Fonseca (Autor) Outra

Revista

Título: Journal of integrative bioinformatics

Vol. 10

Página Final: 231

ISSN: 1613-4516

Editora: Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.)

Indexação

Scopus - 12 Citações

Outras Informações

ID Authenticus: P-009-RQB

DOI: 10.2390/biecoll-jib-2013-231

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica