Contrasting and combining transcriptome complexity captured by short and long RNA sequencing reads

Area of Science:

• Genomics
• Transcriptomics
• Bioinformatics

Background:

• RNA sequencing technologies, including short-read and long-read, are crucial for genomic research.
• Long reads excel at capturing full isoforms and navigating repetitive genomic regions.
• Short reads offer superior coverage and accuracy but have limitations in isoform and repetitive region analysis.

Purpose of the Study:

• To quantitatively compare short- and long-read RNA sequencing technologies.
• To investigate the benefits of combining both sequencing technologies for transcriptome analysis.
• To develop a unified computational approach for integrated transcriptome variation analysis.

Main Methods:

• Development of a computational pipeline to assess matched long- and short-read RNA sequencing data.
• Utilized a variety of transcriptome statistics to evaluate data from both technologies.
• Introduced MAJIQ-L, an extension of MAJIQ software, for unified transcriptome analysis.

Main Results:

• Short-read data detected approximately 30% more splice junctions compared to long-read data.
• Long-read sequencing identified a higher number of intron retention events and full isoforms.
• A significant portion (10-30%) of splice junctions detected by short reads were missed by long reads.

Conclusions:

• Combining short- and long-read RNA sequencing provides a more complete transcriptome profile.
• The MAJIQ-L software enables a unified view of transcriptome variations from both technologies.
• Integrated analysis using MAJIQ-L enhances transcriptome analysis and can be applied to future long-read technologies.