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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F21%3A00124306" target="_blank" >RIV/00216224:14740/21:00124306 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-7737/10/9/896" target="_blank" >https://www.mdpi.com/2079-7737/10/9/896</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/biology10090896" target="_blank" >10.3390/biology10090896</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BIOLOGY-BASEL

ISSN

2079-7737

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

9

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

896

UT code for WoS article

000699039000001

EID of the result in the Scopus database

2-s2.0-85114763178