Abstract

Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent research highlights the important role of TEs in the embryogenesis, neurodevelopment, and immune functions. However, there is a lack of a one-stop and easy to use computational pipeline for expression analysis of both genes and locus-specific TEs from RNA-Seq data. Here, we present GeneTEFlow, a fully automated, reproducible and platform-independent workflow, for the comprehensive analysis of gene and locus-specific TEs expression from RNA-Seq data employing Nextflow and Docker technologies. This application will help researchers more easily perform integrated analysis of both gene and TEs expression, leading to a better understanding of roles of gene and TEs regulation in human diseases. GeneTEFlow is freely available at https://github.com/zhongw2/GeneTEFlow.

Highlights

  • Transposable elements (TEs) are mobile DNA sequences which have the capacity to move from one location to another on the genome [1]

  • Retrotransposons are made of Long Terminal Repeats (LTRs) and non-LTRs that include long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) that mobilize via a RNA intermediate, while DNA transposons mobilize and function through a DNA intermediate [4,5,6]

  • Taking advantage of the advanced functionalities provided by Nextflow and Docker, GeneTEFlow allows users to run analysis reproducibly on different computing platforms without the need for individual tool installation and manual version tracking

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Summary

Introduction

Transposable elements (TEs) are mobile DNA sequences which have the capacity to move from one location to another on the genome [1]. The funder provided support in the form of salaries for authors XL, JRB and WZ., but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of Transposable elements (TEs) are mobile DNA sequences which have the capacity to move from one location to another on the genome [1]. Genome-wide analysis of TEs expression from high throughput RNA sequencing data has been a challenging computational problem.

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