New Intraclass Helitrons Classification Using DNA-Image Sequences and Machine Learning Approaches

Publication date: Available online 3 January 2020Source: IRBMAuthor(s): R. Touati, I. Messaoudi, A.E. Oueslati, Z. Lachiri, M. KharratAbstractHelitrons, eukaryotic transposable elements (TEs) transposed by rolling-circle mechanism, have been found in various species with highly variable copy numbers and sometimes with a large portion of their genomes. The impact of helitrons sequences in the genome is to frequently capture host genes during their transposition. Since their discovery, 18 years ago, by computational analysis of whole genome sequences of Arabidopsis thaliana plant and Caenorhabditis elegans (C. elegans) nematode, the identification and classification of these mobile genetic elements remain a challenge due to the fact that the wide majority of their families are non-autonomous. In C. elegans genome, DNA helitrons sequences possess great variability in terms of length that varies between 11 and 8965 base pairs (bps) from one sequence to another. In this work, we develop a new method to predict helitrons DNA-sequences, which is particularly based on Frequency Chaos Game Representation (FCGR) DNA-images. Thus, we introduce an automatic system in order to classify helitrons families in C. elegans genome, based on a combination between machine learning approaches and features extracted from DNA-sequences. Consequently, the new set of helitrons features (the FCGR images and K-mers) are extracted from DNA sequences. These helitrons features consist of the frequency appa...
Source: IRBM - Category: Biomedical Engineering Source Type: research