ChimeraTE: a pipeline to detect chimeric transcripts derived from genes and transposable elements.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transposable elements (TEs) are significant drivers of genetic variation and can lead to structural variants.
• TE-gene fusion transcripts, or chimeric transcripts, are implicated in species adaptation.
• Current methods for identifying chimeric transcripts are limited by scale and reliance on reference genomes, overlooking polymorphic TE insertions.

Purpose of the Study:

• To develop a novel computational pipeline, ChimeraTE, for transcriptome-wide identification of chimeric transcripts.
• To enable the detection of chimeric transcripts originating from both reference-guided and reference-free analyses.
• To investigate the role of TEs in generating genetic diversity through chimeric transcript formation.

Main Methods:

• Developed ChimeraTE, a pipeline utilizing paired-end RNA-seq reads for chimeric transcript identification.
• Implemented two modes: a reference-guided approach (Mode 1) and a reference-free approach (Mode 2) for polymorphic TE insertions.
• Validated the pipeline using RNA-seq data from four Drosophila melanogaster wild-type strains.

Main Results:

• Identified that approximately 1.12% of all genes generated chimeric transcripts, predominantly involving TE-exonized sequences.
• Discovered that about 23% of detected chimeras were absent from the reference genome, suggesting recent or polymorphic TE insertions.
• Demonstrated ChimeraTE's capability to uncover chimeric transcripts without a reference genome.

Conclusions:

• ChimeraTE is the first pipeline capable of automatically identifying chimeric transcripts without requiring a reference genome.
• The tool facilitates the investigation of TE contributions to transcriptome plasticity and genetic diversity.
• Findings highlight the importance of polymorphic TEs in generating novel gene fusions and potential adaptations.