A systematic review of the application of machine learning in the detection and classification of transposable elements

Abstract

Background Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology We followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields. Conclusions ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

“…In this study, we compare the performance of the most commonly used ML and DL algorithms in bioinformatics (Orozco-Arias et al, 2019a) in the task of classifying by supervised and unsupervised techniques. We used the 11_Tumor database and applied different preprocessing strategies.…”

InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning

“…TE classification is generally performed hierarchically [ 16 ], whereby TEs are first divided into classes according to their replication cycle: Class I or retrotransposons, which follow a copy-and-paste strategy using an RNA intermediate; and Class II or DNA transposons that use a cut-and-paste mobility mechanism through a DNA molecule [ 17 ]. Next, TE levels correspond to orders, superfamilies, lineages (also called families), and sub-families [ 18 ]. Among these, long terminal repeat (LTR) retrotransposons (LTR-RTs, an order of retrotransposons) are the most abundant TEs in plant genomes [ 19 , 20 ] and can account for up to 80% of the plant genome size, such as in wheat, barley, or rubber tree [ 21 ].…”

TIP_finder: An HPC Software to Detect Transposable Element Insertion Polymorphisms in Large Genomic Datasets

“…These mobile elements can accumulate large copy numbers in their host genomes [4] and have been found in all organisms. The majority of the nuclear DNA content of large genomes is composed of TEs, such as in wheat, barley, and maize [5][6][7] for plants. In humans, these elements (or TE-derived sequences) comprise~50-70% of the sequenced genome [8].…”

“…In humans, these elements (or TE-derived sequences) comprise~50-70% of the sequenced genome [8]. Several studies have indicated that TEs play crucial genomic roles involved in chromosome structuring, structural variation, the alteration of gene expression [5,7], evolution, the variation of genomic size, and environmental adaptation [9][10][11][12][13]. Nevertheless, these elements can also be associated with human diseases, such as different types of cancer [14][15][16].…”