SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) generates vast amounts of data requiring complex multi-step processing.
• Existing RNA-seq software tools are often specialized, computationally intensive, and lack comprehensive features.
• There is a need for user-friendly, reproducible, and actively maintained RNA-seq analysis workflows.

Purpose of the Study:

• To develop an accessible and comprehensive RNA sequencing data processing pipeline.
• To lower the computational barrier for biologists and clinicians analyzing RNA-seq data.
• To provide a flexible and shareable workflow that integrates with downstream R/Bioconductor analysis.

Main Methods:

• Development of the Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy).
• Ensuring portability across various computational frameworks (SGE, SLURM, local, Docker).
• Designing a user-friendly interface with simple input requirements (sample names and FASTQ files).

Main Results:

• SPEAQeasy is easy to install, share, and use, even for researchers with limited computational expertise.
• The pipeline supports diverse computational environments and facilitates downstream R/Bioconductor analysis.
• SPEAQeasy effectively processes raw RNA-seq reads into valuable gene expression data.

Conclusions:

• SPEAQeasy significantly lowers the entry barrier for RNA-seq data analysis.
• The pipeline provides a user-friendly and flexible solution for biologists and clinicians.
• SPEAQeasy promotes reproducible and efficient RNA-seq data processing across different research settings.