IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq...

Area of Science:

• Genomics
• Transcriptomics
• Bioinformatics

Background:

• Long-read RNA sequencing (long-RNA-seq) offers full-length transcript analysis, crucial for understanding isoform diversity.
• Existing tools may have limitations in comprehensively detecting all transcript isoforms and fusions.

Purpose of the Study:

• To develop a robust computational framework, IFDlong, for detecting and quantifying isoform and fusion transcripts from long-RNA-seq data.
• To evaluate IFDlong's performance against existing methods using simulations and diverse real-world datasets.

Main Methods:

• IFDlong utilizes a probabilistic framework to annotate long reads.
• It employs an expectation-maximization algorithm for expression quantification.
• The software identifies novel isoforms and profiles fusion transcripts.

Main Results:

• IFDlong demonstrated superior accuracy and robustness in large-scale simulations.
• Performance was validated across multiple in-house and public datasets, including healthy tissues, cell lines, and disease samples.
• IFDlong outperformed existing transcriptome analysis tools.

Conclusions:

• IFDlong is a highly accurate and robust tool for isoform and fusion transcript detection from long-RNA-seq data.
• This framework significantly advances transcriptome research by enabling comprehensive analysis of transcript diversity.
• IFDlong is applicable to bulk and single-cell data across various biological contexts.