Population and Clinical Genetics of Human Transposable Elements in the (post) Genomic Era

Overview

Journal Mob Genet Elements

Date 2017 Feb 24

PMID 28228978

Citations 10

Authors

Lavanya Rishishwar

Lu Wang

Evan A Clayton

Leonardo Marino-Ramirez

John F McDonald

I King Jordan

Affiliations

Soon will be listed here.

Abstract

Recent technological developments-in genomics, bioinformatics and high-throughput experimental techniques-are providing opportunities to study ongoing human transposable element (TE) activity at an unprecedented level of detail. It is now possible to characterize genome-wide collections of TE insertion sites for multiple human individuals, within and between populations, and for a variety of tissue types. Comparison of TE insertion site profiles between individuals captures the germline activity of TEs and reveals insertion site variants that segregate as polymorphisms among human populations, whereas comparison among tissue types ascertains somatic TE activity that generates cellular heterogeneity. In this review, we provide an overview of these new technologies and explore their implications for population and clinical genetic studies of human TEs. We cover both recent published results on human TE insertion activity as well as the prospects for future TE studies related to human evolution and health.

Citing Articles

Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks.

Orozco-Arias S, Lopez-Murillo L, Pina J, Valencia-Castrillon E, Tabares-Soto R, Castillo-Ossa L PLoS One. 2023; 18(9):e0291925.

PMID: 37733731 PMC: 10513252. DOI: 10.1371/journal.pone.0291925.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.

Pina J, Orozco-Arias S, Tobon-Orozco N, Camargo-Forero L, Tabares-Soto R, Guyot R Evol Bioinform Online. 2023; 19:11769343221150585.

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The coevolution between APOBEC3 and retrotransposons in primates.

Modenini G, Abondio P, Boattini A Mob DNA. 2022; 13(1):27.

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-mer-based machine learning method to classify LTR-retrotransposons in plant genomes.

Orozco-Arias S, Candamil-Cortes M, Jaimes P, Pina J, Tabares-Soto R, Guyot R PeerJ. 2021; 9:e11456.

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InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning.

Orozco-Arias S, Jaimes P, Candamil M, Jimenez-Varon C, Tabares-Soto R, Isaza G Genes (Basel). 2021; 12(2).

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