nf-rnaSeqCount: A Nextflow pipeline for obtaining raw read counts from RNA-seq data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Transcriptomics

Background:

• Exponential growth in Next-Generation Sequencing (NGS) data necessitates efficient analysis pipelines.
• Multi-omics data analysis offers insights into biological systems and disease mechanisms but presents computational challenges.
• RNA sequencing (RNA-seq) generates large datasets requiring robust computational methods for accurate interpretation.

Purpose of the Study:

• To develop a robust, portable, and reproducible bioinformatic pipeline for automated RNA sequencing data analysis.
• To streamline the process of mapping raw RNA-seq reads and quantifying genomic feature abundance.
• To facilitate differential gene expression analyses using standardized read count matrices.

Main Methods:

• Development of the nf-rnaSeqCount pipeline utilizing Nextflow for workflow management.
• Application containerization using Singularity for enhanced portability and reproducibility.
• Pipeline automates read mapping to a reference genome and quantification of genomic features.

Main Results:

• The nf-rnaSeqCount pipeline efficiently generates a matrix of read counts.
• This output is directly compatible with differential expression analysis tools like DESeq2 and edgeR.
• The pipeline ensures a quick and efficient workflow for RNA-seq data analysis.

Conclusions:

• The developed pipeline offers a robust and flexible solution for RNA-seq data analysis, from quality control to alignment.
• Implementation of such pipelines reduces analysis time and increases the accuracy and reproducibility of findings.
• This work promotes further advancements in transcriptome research by providing a reliable computational framework.