TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets
Y Jin, OH Tam, E Paniagua, M Hammell - Bioinformatics, 2015 - academic.oup.com
Y Jin, OH Tam, E Paniagua, M Hammell
Bioinformatics, 2015•academic.oup.comMotivation: 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. Results: Here, we present a method and software package for including …
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. Results: Here, we present a method and software package for including …
Abstract
Motivation: 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.
Results: 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.
Availability and implementation: The source code, associated GTF files for TE annotation, and testing data are freely available at http://hammelllab.labsites.cshl.edu/software.
Contact: mhammell@cshl.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.
Oxford University Press