Dataset Information

TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets.

ABSTRACT: Most RNA-seq data analysis software packages are not designed to handle the complexities involved in properly apportioning short sequencing reads to highly repetitive regions of the genome. These regions are often occupied by transposable elements (TEs), which make up between 20 and 80% of eukaryotic genomes. They can contribute a substantial portion of transcriptomic and genomic sequence reads, but are typically ignored in most analyses.Here, we present a method and software package for including both gene- and TE-associated ambiguously mapped reads in differential expression analysis. Our method shows improved recovery of TE transcripts over other published expression analysis methods, in both synthetic data and qPCR/NanoString-validated published datasets.The source code, associated GTF files for TE annotation, and testing data are freely available at http://hammelllab.labsites.cshl.edu/software.mhammell@cshl.edu.Supplementary data are available at Bioinformatics online.

SUBMITTER: Jin Y

PROVIDER: S-EPMC4757950 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets.

Jin Ying Y Tam Oliver H OH Paniagua Eric E Hammell Molly M

Bioinformatics (Oxford, England) 20150723 22

<h4>Motivation</h4>Most RNA-seq data analysis software packages are not designed to handle the complexities involved in properly apportioning short sequencing reads to highly repetitive regions of the genome. These regions are often occupied by transposable elements (TEs), which make up between 20 and 80% of eukaryotic genomes. They can contribute a substantial portion of transcriptomic and genomic sequence reads, but are typically ignored in most analyses.<h4>Results</h4>Here, we present a meth ...[more]

PMID: 26206304

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Project description:Transposable elements (TEs), also known as "jumping genes", are repetitive sequences with the capability of changing their location within the genome. They are key players in many different biological processes in health and disease. Therefore, a reliable quantification of their expression as transcriptional units is crucial to distinguish between their independent expression and the transcription of their sequences as part of canonical transcripts. TEs quantification faces difficulties of different types, the most important one being low reads mappability due to their repetitive nature preventing an unambiguous mapping of reads originating from their sequences. A large fraction of TEs fragments localizes within introns, which led to the hypothesis that intron retention (IR) can be an additional source of bias, potentially affecting accurate TEs quantification. IR occurs when introns, normally removed from the mature transcript by the splicing machinery, are maintained in mature transcripts. IR is a widespread mechanism affecting many different genes with cell type-specific patterns. We hypothesized that, in an RNA-seq experiment, reads derived from retained introns can introduce a bias in the detection of overlapping, independent TEs RNA expression. In this study we performed meta-analysis using public RNA-seq data from lymphoblastoid cell lines and show that IR can impact TEs quantification using established tools with default parameters. Reads mapped on intronic TEs were indeed associated to the expression of TEs and influence their correct quantification as independent transcriptional units. We confirmed these results using additional independent datasets, demonstrating that this bias does not appear in samples where IR is not present and that differential TEs expression does not impact on IR quantification. We concluded that IR causes the over-quantification of intronic TEs and differential IR might be confused with differential TEs expression. Our results should be taken into account for a correct quantification of TEs expression from RNA-seq data, especially in samples in which IR is abundant.

Dataset Information

TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets.

Publications

TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets.

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