Nov 25, 2013

TERL: Classification of Transposable Elements by Convolutional Neural Networks

BioRxiv : the Preprint Server for Biology
IAN S LoganPedro Henrique Bugatti

Abstract

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. They are capable of transpose and generate multiple copies of themselves throughout genomes. These sequences can produce a variety of effects on organisms, such as regulation of gene expression. There are several types of these elements, which are classified in a hierarchical way into classes, subclasses, orders and superfamilies. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology based search, which could be inefficient for classifying non-homologous sequences. Here we propose a pipeline, transposable elements representation learner (TERL), that use four preprocessing steps, a transformation of one-dimensional nucleic acid sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks (CNNs). CNN is used to classify TE sequences because it is a very flexible classification method, given it can be easily retrained to classify different categories...Continue Reading

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Mentioned in this Paper

DNA, Mitochondrial
Vision
Optic Atrophy, Hereditary, Leber
Blind Vision
Mitochondria
Unspecified Visual Loss
LHON gene
Nadh Dehydrogenase Complex
PDLIM7 gene
Sciurus oculatus

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