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

Ilektra-Chara Giassa, Panagiotis Alexiou
<span title="2021-09-10">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">Biology</a> </i> &nbsp;
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
more &raquo; ... cal 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/biology10090896</a> <a target="_blank" rel="external noopener" href="">pmid:34571773</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC8465862</a> <a target="_blank" rel="external noopener" href="">fatcat:5a5osyvzfjhc3jogue42lgrtyy</a> </span>
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