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

J 2021

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

GIASSA, Ilektra-Chara a Panagiotis ALEXIOU

Základní údaje

Originální název

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

Autoři

GIASSA, Ilektra-Chara (300 Řecko, domácí) a Panagiotis ALEXIOU (300 Řecko, garant, domácí)

Vydání

BIOLOGY-BASEL, BASEL, MDPI, 2021, 2079-7737

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.168

Kód RIV

RIV/00216224:14740/21:00124306

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.3390/biology10090896

UT WoS

000699039000001

Klíčová slova anglicky

transposable elements regulation; mobile genetic elements; machine learning; bioinformatics methods; DNA methylation; small RNAs; PIWI-interacting RNAs; circular RNAs

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 26. 2. 2022 14:48, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

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.

Návaznosti

867414, interní kód MU

Název: Using Deep Learning to understand RNA Binding Protein binding characteristics (Akronym: DEEPLEARNRBP)

Investor: Evropská unie, Using Deep Learning to understand RNA Binding Protein binding characteristics, MSCA Marie Skłodowska-Curie Actions (Excellent Science)

Citovat

GIASSA, Ilektra-Chara a Panagiotis ALEXIOU. Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements. BIOLOGY-BASEL. BASEL: MDPI, 2021, roč. 10, č. 9, s. 896-917. ISSN 2079-7737. Dostupné z: https://dx.doi.org/10.3390/biology10090896.

@article{1837291,
   author = {Giassa, IlektraandChara and Alexiou, Panagiotis},
   article_location = {BASEL},
   article_number = {9},
   doi = {http://dx.doi.org/10.3390/biology10090896},
   keywords = {transposable elements regulation; mobile genetic elements; machine learning; bioinformatics methods; DNA methylation; small RNAs; PIWI-interacting RNAs; circular RNAs},
   language = {eng},
   issn = {2079-7737},
   journal = {BIOLOGY-BASEL},
   title = {Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements},
   url = {https://www.mdpi.com/2079-7737/10/9/896},
   volume = {10},
   year = {2021}
}

TY  - JOUR
ID  - 1837291
AU  - Giassa, Ilektra-Chara - Alexiou, Panagiotis
PY  - 2021
TI  - Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements
JF  - BIOLOGY-BASEL
VL  - 10
IS  - 9
SP  - 896
EP  - 896
PB  - MDPI
SN  - 20797737
KW  - transposable elements regulation
KW  - mobile genetic elements
KW  - machine learning
KW  - bioinformatics methods
KW  - DNA methylation
KW  - small RNAs
KW  - PIWI-interacting RNAs
KW  - circular RNAs
UR  - https://www.mdpi.com/2079-7737/10/9/896
N2  - 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.
ER  -

GIASSA, Ilektra-Chara a Panagiotis ALEXIOU. Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements. \textit{BIOLOGY-BASEL}. BASEL: MDPI, 2021, roč.~10, č.~9, s.~896-917. ISSN~2079-7737. Dostupné z: https://dx.doi.org/10.3390/biology10090896.

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