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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Analysis of RNA-Seq Data Using TEtranscripts.

Ying Jin1, Molly Hammell2

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

Methods in Molecular Biology (Clifton, N.J.)
|March 7, 2018
PubMed
Summary
This summary is machine-generated.

Transposable elements (TEs) are mobile genetic sequences often ignored in RNA-seq. We present TEtranscripts, a new tool to include TE-derived reads in differential expression analysis, improving transcriptome insights.

Keywords:
DESeqDifferential expression analysisRNA-seqSTARTEtranscriptsTransposable elements

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Transposable elements (TEs) are mobile genetic sequences comprising a significant portion of eukaryotic genomes.
  • TEs can influence gene expression and cellular transcriptome when not properly silenced.
  • Analyzing TE-derived sequences in RNA-seq is challenging due to repetitive regions and alignment complexities.

Purpose of the Study:

  • To introduce a method for incorporating TE-derived reads into RNA-seq differential expression analysis.
  • To present TEtranscripts, an open-source software package designed for this purpose.
  • To provide a tutorial for using TEtranscripts with a validated dataset.

Main Methods:

  • TEtranscripts assigns both uniquely and ambiguously mapped sequencing reads to gene and TE transcripts.
  • The software statistically infers accurate gene and TE abundances.
  • A detailed tutorial using a published, qPCR-validated dataset is provided.

Main Results:

  • TEtranscripts enables the inclusion of TE-derived reads in differential expression analysis.
  • The method addresses the complexities of aligning reads to repetitive TE regions.
  • The tutorial demonstrates the practical application and validation of the software.

Conclusions:

  • TEtranscripts offers a robust solution for analyzing TE contributions to the transcriptome.
  • This tool enhances RNA-seq data analysis by accounting for mobile genetic elements.
  • The study facilitates a more comprehensive understanding of genomic regulation by TEs.