Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements

GIASSA, Ilektra-Chara and Panagiotis ALEXIOU. Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements. BIOLOGY-BASEL. BASEL: MDPI, 2021, vol. 10, No 9, p. 896-917. ISSN 2079-7737. Available from: https://dx.doi.org/10.3390/biology10090896.

Basic information

• Original name: Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements
• Authors: GIASSA, Ilektra-Chara (300 Greece, belonging to the institution) and Panagiotis ALEXIOU (300 Greece, guarantor, belonging to the institution).
• Edition: BIOLOGY-BASEL, BASEL, MDPI, 2021, 2079-7737.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Switzerland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 5.168
• RIV identification code: RIV/00216224:14740/21:00124306
• Organization unit: Central European Institute of Technology
• Doi: http://dx.doi.org/10.3390/biology10090896
• UT WoS: 000699039000001
• Keywords in English: transposable elements regulation; mobile genetic elements; machine learning; bioinformatics methods; DNA methylation; small RNAs; PIWI-interacting RNAs; circular RNAs
• Tags: rivok
• Tags: International impact, Reviewed

Abstract

Simple Summary Transposable elements (TEs) are DNA sequences that are, or were, able to move (transpose) within the genome of a single cell. They were first discovered by Barbara McClintock while working on maize, and they make up a large fraction of the genome. Transpositions can result in mutations and they can alter the genome size. Cells regulate the activity of TEs using a variety of mechanisms, such as chemical modifications of DNA and small RNAs. Machine learning (ML) is an interdisciplinary subject that studies computer algorithms that can improve through experience and by the use of data. ML has been successfully applied to a variety of problems in bioinformatics and has exhibited favorable precision and speed. Here, we provide a systematic and guided review on the ML and bioinformatic methods and tools that are used for the analysis of the regulation of TEs. Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.

Links

• 867414, interní kód MU: Name: Using Deep Learning to understand RNA Binding Protein binding characteristics (Acronym: DEEPLEARNRBP)

Investor: European Union, MSCA Marie Skłodowska-Curie Actions (Excellent Science)