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Analysis of RNA-Seq Data Using TEtranscripts

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Transcriptome Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1751))

Abstract

Transposable elements (TE) are mobile genetic elements that can readily change their genomic position. When not properly silenced, TEs can contribute a substantial portion to the cell’s transcriptome, but are typically ignored in most RNA-seq data analyses. One reason for leaving TE-derived reads out of RNA-seq analyses is the complexities involved in properly aligning short sequencing reads to these highly repetitive regions. Here we describe a method for including TE-derived reads in RNA-seq differential expression analysis using an open source software package called TEtranscripts. TEtranscripts is designed to assign both uniquely and ambiguously mapped reads to all possible gene and TE-derived transcripts in order to statistically infer the correct gene/TE abundances. Here, we provide a detailed tutorial of TEtranscripts using a published qPCR validated dataset.

Abstract

Barbara McClintock laid the foundation for TE research with her discoveries in maize of mobile genetic elements capable of inserting into novel locations in the genome, altering the expression of nearby genes [1]. Since then, our appreciation of the contribution of repetitive TE-derived sequences to eukaryotic genomes has vastly increased. With the publication of the first human genome draft by the Human Genome Project, it was determined that nearly half of the human genome is derived from TE sequences [2, 3], with varying levels of repetitive DNA present in most plant and animal species. More recent studies looking at distantly related TE-like sequences have estimated that up to two thirds of the human genome might be repeat-derived [4], with the vast majority of these sequences attributed to retrotransposons that require transcription as part of the mobilization process, as discussed below.

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Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Ying Jin & Molly Hammell

Authors
  1. Ying Jin
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  2. Molly Hammell
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Correspondence to Molly Hammell .

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Editors and Affiliations

  1. Department of Cell Biology and Genetics, School of Basic Medicine, Shenzhen University Health, Science Center, Shenzhen, China

    Yejun Wang

  2. Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, Virginia, USA

    Ming-an Sun

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Jin, Y., Hammell, M. (2018). Analysis of RNA-Seq Data Using TEtranscripts. In: Wang, Y., Sun, Ma. (eds) Transcriptome Data Analysis. Methods in Molecular Biology, vol 1751. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7710-9_11

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  • DOI: https://doi.org/10.1007/978-1-4939-7710-9_11

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7709-3

  • Online ISBN: 978-1-4939-7710-9

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