Pseudoalignment tools as an efficient alternative to detect repeated transposable elements in scRNAseq data

Abstract

Transposable elements (TE) have played a major role in configuring the structures of mammalian genomes through evolution. In normal conditions, expression of these elements is repressed by different epigenetic regulation mechanisms such as DNA methylation, histone modification and regulation by small RNAs. TE re-activation is associated with stemness potential acquisition, regulation of innate immunity, and disease, such as cancer. However, the vast majority of current knowlededge in the field is based on bulk expression studies and very little is known on cell type- or state-specific expression of TE derived transcripts. Therefore, cost-efficient single cell-resolution TE expression analytical approaches are needed. We have implemented an analytical approach based on pseudoalignment to consensus sequences to incorporate TE expression information to scRNAseq data. All the data and code implemented is available as Supplementary data and in: https://github.com/jmzvillarreal/kallisto_TE_scRNAseq. Supplementary data are available at Bioinformatics online.