SNAPR: a bioinformatics pipeline for efficient and accurate RNA-seq alignment and analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Raw RNA sequencing (RNA-seq) data analysis is complex and time-consuming.
• Existing methods require multiple steps, hindering efficiency and scalability.

Purpose of the Study:

• To present SNAPR, a novel RNA-seq mapping algorithm designed to streamline data processing.
• To demonstrate SNAPR's efficiency, accuracy, and high-throughput capacity for large-scale analyses.

Main Methods:

• Developed an RNA-seq mapping algorithm utilizing a hash table approach for high-memory machines.
• Implemented SNAPR to accept compressed/uncompressed FASTQ and BAM files, performing Phred score filtering natively.
• Designed SNAPR for single or thousands of libraries and compatibility with future long-read sequencing platforms.

Main Results:

• SNAPR successfully analyzes hundreds of TCGA samples in hours.
• The algorithm identifies gene fusions and exogenous RNA species in a single step.
• SNAPR outputs sorted BAM files, individual read counts, and detects viral events.

Conclusions:

• SNAPR significantly streamlines RNA-seq data analysis, offering efficiency and accuracy.
• The algorithm provides high-throughput capacity essential for modern, large-volume genomic studies.
• SNAPR addresses the need for uniform parameters in integrating diverse RNA-seq datasets.