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

Schietgat, Leander ; Vens, Celine ; Cerri, Ricardo ; Fischer, Carlos N ; Costa, Eduardo ; Ramon, Jan ; Carareto, Claudia M A ; Blockeel, Hendrik Bromberg, Yana

PLoS computational biology, 2018-04, Vol.14 (4), p.e1006097-e1006097 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
A machine learning based framework to identify and classify long terminal repeat retrotransposons
Autor: Schietgat, Leander ; Vens, Celine ; Cerri, Ricardo ; Fischer, Carlos N ; Costa, Eduardo ; Ramon, Jan ; Carareto, Claudia M A ; Blockeel, Hendrik
Bromberg, Yana
Assuntos: Annotations ; Arabidopsis thaliana ; Artificial Intelligence ; Automation ; Biodiversity ; Bioinformatics ; Biological evolution ; Biology ; Biology and Life Sciences ; Classification ; Computer and Information Sciences ; Computer Science ; Data Analysis, Statistics and Probability ; Deoxyribonucleic acid ; DNA ; Drosophila melanogaster ; Engineering and Technology ; Evolutionary genetics ; Funding ; Gene sequencing ; Genetic diversity ; Genomes ; Homology ; Identification ; Identification methods ; Insects ; Learning algorithms ; Long terminal repeat ; Machine Learning ; Mathematical models ; Mathematics ; Methods ; Performance prediction ; Physics ; Physiological aspects ; Research and Analysis Methods ; Retrotransposons ; Software ; Species classification ; Species diversity ; Statistics
É parte de: PLoS computational biology, 2018-04, Vol.14 (4), p.e1006097-e1006097
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The authors have declared that no competing interests exist.
Descrição: 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 framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: RepeatMasker, Censor and LtrDigest. In contrast to these methods, TE-Learner is the first to incorporate machine learning techniques, outperforming these methods in terms of predictive performance, while able to learn models and make predictions efficiently. Moreover, we show that our method was able to identify TEs that none of the above method could find, and we investigated TE-Learner's predictions which did not correspond to an official annotation. It turns out that many of these predictions are in fact strongly homologous to a known TE.
Editor: United States: Public Library of Science
Idioma: Inglês

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

Schietgat, Leander ; Vens, Celine ; Cerri, Ricardo ; Fischer, Carlos N ; Costa, Eduardo ; Ramon, Jan ; Carareto, Claudia M A ; Blockeel, Hendrik Bromberg, Yana

United States: Public Library of Science

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