Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements.
DNA methylation
PIWI-interacting RNAs
bioinformatics methods
circular RNAs
machine learning
mobile genetic elements
small RNAs
transposable elements regulation
Journal
Biology
ISSN: 2079-7737
Titre abrégé: Biology (Basel)
Pays: Switzerland
ID NLM: 101587988
Informations de publication
Date de publication:
10 Sep 2021
10 Sep 2021
Historique:
received:
30
06
2021
revised:
06
09
2021
accepted:
07
09
2021
entrez:
28
9
2021
pubmed:
29
9
2021
medline:
29
9
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34571773
pii: biology10090896
doi: 10.3390/biology10090896
pmc: PMC8465862
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Horizon 2020
ID : H2020-WF-01-2018: 867414
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