LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Understanding subcellular RNA distribution is crucial for deciphering biological processes.
• Existing methods often miss low-abundance RNA isoforms due to limited read coverage.

Purpose of the Study:

• To develop and validate a computational pipeline for comprehensive analysis of full-length cytoplasmic and nuclear transcriptomes.
• To improve the detection of low-abundance RNA isoforms using subcellular direct RNA sequencing (DRS).

Main Methods:

• Subcellular direct RNA sequencing (DRS) was performed on four lung cancer cell lines.
• A novel computational pipeline, Low-abundance Aware Full-length Isoform clusTEr (LAFITE), was developed and applied.
• LAFITE's performance was compared against existing methods using DRS and orthogonal datasets.
• Experimental validation was conducted for novel isoforms identified by LAFITE.

Main Results:

• LAFITE significantly outperforms current methods in detecting full-length RNA transcripts, especially low-abundance isoforms.
• Six novel isoforms exclusively identified by LAFITE were experimentally validated, confirming pipeline reliability.
• The study revealed the intricate complexity of the nuclear transcriptome, including isoform diversity, 3'-UTR usage, m6A modification patterns, and intron retention.

Conclusions:

• LAFITE enhances the identification of full-length RNA isoforms from subcellular DRS data.
• The pipeline provides a high-resolution view of the RNA landscape at the isoform level.
• This approach deepens the understanding of RNA biology in lung cancer and potentially other diseases.