Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Next-generation RNA-sequencing (RNA-seq) provides critical transcriptomic insights but faces challenges with complex data processing.
• Existing RNA-seq workflows require simplification, efficiency, and interoperability for large-scale datasets.

Purpose of the Study:

• To develop a scalable, user-friendly analysis suite for efficient RNA-sequencing data processing.
• To address the growing need for simplified and robust RNA-seq workflows for researchers handling large datasets.

Main Methods:

• Developed RMTA (Read Mapping, Transcript Assembly), a containerized (Docker) analysis suite for RNA-seq data.
• Integrated RMTA into CyVerse's Discovery Environment for accessibility and ease of deployment.
• Created a high-throughput, parallelized version (OSG-RMTA) for massive datasets on the Open Science Grid (OSG).

Main Results:

• RMTA offers automated quality analysis, transcript filtering, and read counting for differential expression.
• The suite supports diverse computing environments (cloud, local, HPC) and educational use.
• OSG-RMTA enables efficient, distributed high-throughput computing for tens of thousands of FASTq files.

Conclusions:

• RMTA provides a scalable and reproducible solution for analyzing large RNA-seq datasets.
• The tool enhances accessibility for researchers of all skill levels, including undergraduates.
• RMTA and OSG-RMTA streamline transcriptomic data analysis, facilitating complex biological discoveries.