L-RAPiT: A Cloud-Based Computing Pipeline for the Analysis of Long-Read RNA Sequencing Data

Area of Science:

• Genomics and Transcriptomics
• Bioinformatics and Computational Biology

Background:

• Long-read sequencing (LRS) offers advanced RNA analysis capabilities, including detailed splicing pattern insights.
• Exponential growth in LRS datasets presents a wealth of research potential.
• Current LRS data analysis tools often require command-line expertise, limiting accessibility.

Purpose of the Study:

• To develop a user-friendly, accessible pipeline for analyzing long-read sequencing transcriptomic data.
• To overcome the technical barriers associated with current LRS analysis software.
• To enable standardized and efficient analysis of both public and new LRS datasets.

Main Methods:

• Development of the Long-Read Analysis Pipeline for Transcriptomics (L-RAPiT).
• Implementation via Google Colaboratory, requiring no specialized computational resources.
• Direct analysis of transcriptomic reads from Oxford Nanopore and PacBio LRS platforms.

Main Results:

• L-RAPiT provides a free, user-friendly interface for LRS data analysis.
• The pipeline eliminates the need for bioinformatics expertise or command-line proficiency.
• Enables rapid, convenient, and standardized analysis of LRS transcriptomic data.

Conclusions:

• L-RAPiT democratizes the analysis of long-read sequencing data.
• Facilitates broader research applications by simplifying complex genomic analyses.
• Promotes reproducible and comparable results across studies using standardized LRS analysis.