Isoformic: a workflow for transcript-level RNA-seq interpretation.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transcriptome analysis is crucial in modern biology but often overlooks alternative splicing and transcription initiation/termination.
• Most mammalian genes (over 95%) produce multiple transcripts, yet short-read RNA sequencing (RNA-seq) analysis struggles with transcript-level interpretation.
• Existing tools lack accuracy, consistency with annotations, and efficient downstream biological interpretation capabilities.

Purpose of the Study:

• To introduce Isoformic, a customizable R pipeline for transcript-level analysis of short-read RNA-seq data.
• To enable detection of transcript-level changes, visualization of exon-intron structures, and functional enrichment analysis stratified by transcript type.
• To enhance the resolution of RNA-seq studies and uncover regulatory roles of alternative transcription events.

Main Methods:

• Development of a customizable R pipeline named Isoformic.
• Processing of differential expression results to identify transcript-level alterations.
• Validation using diverse datasets including preeclampsia, SARS-CoV-2 infection, and murine anxiety models.
• Compatibility with the GENCODE reference transcriptome.

Main Results:

• Isoformic successfully detects genes with transcript-level changes.
• The pipeline visualizes exon-intron structures and performs transcript-type-stratified functional enrichment.
• Validated studies revealed biologically relevant transcript variants and their potential phenotypic associations.
• Isoformic enhances the interpretability of RNA-seq data.

Conclusions:

• Isoformic provides a robust solution for transcript-level analysis of short-read RNA-seq data.
• The pipeline overcomes limitations of existing tools, offering improved accuracy and biological insights.
• Isoformic empowers researchers to explore the functional significance of alternative transcription events.