ExplorATE: a new pipeline to explore active transposable elements from RNA-seq data

• Published in: Bioinformatics Vol. 38; no. 13; pp. 3361 - 3366
• Main Authors: Femenias, Martin M, Santos, Juan C, Sites, Jack W, Avila, Luciano J, Morando, Mariana
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27-06-2022
• Subjects: DNA Transposable Elements; Exome Sequencing; RNA-Seq; Software; Transcriptome
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btac354

Abstract
Motivation
Transposable elements (TEs) are ubiquitous in genomes and many remain active. TEs comprise an important fraction of the transcriptomes with potential effects on the host genome, either by generating deleterious mutations or promoting evolutionary novelties. However, their functional study is limited by the difficulty in their identification and quantification, particularly in non-model organisms.
Results
We developed a new pipeline [explore active transposable elements (ExplorATE)] implemented in R and bash that allows the quantification of active TEs in both model and non-model organisms. ExplorATE creates TE-specific indexes and uses the Selective Alignment (SA) to filter out co-transcribed transposons within genes based on alignment scores. Moreover, our software incorporates a Wicker-like criteria to refine a set of target TEs and avoid spurious mapping. Based on simulated and real data, we show that the SA strategy adopted by ExplorATE achieved better estimates of non-co-transcribed elements than other available alignment-based or mapping-based software. ExplorATE results showed high congruence with alignment-based tools with and without a reference genome, yet ExplorATE required less execution time. Likewise, ExplorATE expands and complements most previous TE analyses by incorporating the co-transcription and multi-mapping effects during quantification, and provides a seamless integration with other downstream tools within the R environment.
Availability and implementation
Source code is available at https://github.com/FemeniasM/ExplorATEproject and https://github.com/FemeniasM/ExplorATE_shell_script. Data available on request.
Supplementary information
Supplementary data are available at Bioinformatics online.