.png){kind=icon}
.png){kind=icon}
Return
Isoformic: a workflow for transcript-level RNA-seq interpretation
.png){kind=icon}
OA
AI
DOI：10.1093/nargab/lqaf176.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Transcriptome analysis is one of the bases of modern biology, yet it is typically performed at the gene level, ignoring the complexity of alternative splicing and differential transcription initiation/termination events. Over 95% of mammalian genes produce multiple transcripts, yet most RNA-seq analyses rely on short-read data, for which transcript-level interpretation remains challenging. Current tools suffer from low accuracy, inconsistency with annotations, and lack quick solutions for downstream biological interpretation. Here, we present Isoformic, a customizable R pipeline for transcript-level analysis of short-read RNA-seq data, available on GitHub. Isoformic processes differential expression results to detect genes with transcript-level changes, visualize exon-intron structures, and perform functional enrichment stratified by transcript type. Validated on diverse datasets, including preeclampsia, SARS-CoV-2 infection, and murine anxiety models, Isoformic reveals biologically relevant transcript variants and their possible phenotypic associations. Compatible with GENCODE reference transcriptome, Isoformic enhances the resolution of RNA-seq studies, enabling researchers to uncover the regulatory roles of alternative transcription events.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
N
IF:
2.8
Papers:
221
Citations:
0
View more organizations