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

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.

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

• Originální 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
• WWW: 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

Anotace

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)