Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Abstract

Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

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

“…In several ML studies, DL has proven to be a robust technique for analyzing large-scale datasets (Bengio, Courville & Vincent, 2013). With these advances, DL has achieved cutting-edge performance in a wide range of applications, including bioinformatics and genomics (Min, Lee & Yoon, 2016;Yue & Wang, 2018), analysis of metagenomics samples (Ceballos et al, 2019), identification of somatic transposable elements in ovarian cancer (Tang et al, 2017), identification and classification of retrotransposons in plants (Orozco-Arias, Isaza & Guyot, 2019) and cancer classification using Principal Component Analysis (PCA) (Liu, Cai & Shao, 2011). Recent work by Guillen & Ebalunode (2016) demonstrated promising results for the application of DL in microarray gene expression.…”

Development and Deployment of High-Throughput Retrotransposon-Based Markers Reveal Genetic Diversity and Population Structure of Asian Bamboo

“…Two types of TEs have been identified based on their transposition mechanisms, namely class I retrotransposons and class II DNA transposons [28]. Retrotransposons are RNA-based TEs which duplicate themselves and move within the genome in a semi-conservative manner through a 'copy-and-paste' mechanism of an RNA intermediate [28][29][30]. DNA transposons, on the other hand, use a conservative style of transposition and move directly by a 'cut-and-paste' mechanism [31][32][33].…”

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

“…Also, the repetitive nature of TEs, as well as their structural polymorphism, species specificity, and high divergence rate even among close relative species (Mustafin & Khusnutdinova, 2018), represent significant obstacles and challenges for their analysis (Ou, Chen & Jiang, 2018). Despite of the complexity, a well-curated detection and classification of TEs is important, due to these elements have key roles into genomes, such as in the chromosomal structure, their interaction with genes, and adaptation and evolution processes (Orozco-Arias, Isaza & Guyot, 2019) and their annotation could provide insights into genomic dynamics (Wheeler et al, 2012).…”