SNPAAMapper: An efficient genome-wide SNP variant analysis pipeline for next-generation sequencing data

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Molecular Genetics

Background:

• Existing next-generation sequencing (NGS) analysis tools primarily focus on read alignment and variant calling for exome data.
• Publicly available tools for downstream genome-wide variant analysis are limited in functionality.

Purpose of the Study:

• To develop a novel variant analysis pipeline, SNPAAMapper, to address the limitations in downstream genome-wide variant analysis.
• To provide enhanced functionality for variant classification, amino acid change prediction, and mutation effect prioritization.

Main Methods:

• Developed SNPAAMapper, a bioinformatics pipeline for variant analysis.
• Implemented functionality for classifying variants by genomic region (CDS, UTRs, introns, etc.).
• Included prediction of amino acid change types (synonymous, non-synonymous) and prioritization of mutation effects.
• Added features for checking exon/intron junction variants, custom cutoff parameters, and dbSNP annotation.
• Output results in a spreadsheet format detailing variant information and prioritized effects.

Main Results:

• SNPAAMapper effectively classifies variants by genomic region and predicts amino acid change types.
• The pipeline prioritizes mutation effects, distinguishing between CDS and UTR variants.
• Additional features include exon/intron junction analysis, customizable filters, and dbSNP annotation.

Conclusions:

• SNPAAMapper offers a novel and functional solution for the downstream analysis of genome-wide variants.
• The pipeline provides comprehensive variant information and prioritized effects in an accessible format for researchers.