BAM-matcher: a tool for rapid NGS sample matching.

Area of Science:

• Genomics
• Bioinformatics

Background:

• High-throughput genome sequencing facilities traditionally use SNP data to detect mislabelled samples.
• The increasing prevalence of multiple samples from the same source (e.g., matched tumor-normal samples) necessitates efficient sample identity verification.
• Existing methods often require additional laboratory testing, adding time and cost.

Purpose of the Study:

• To present BAM-matcher, a novel computational tool for verifying sample identity directly from sequencing data.
• To enable rapid and accurate determination of whether two BAM files originate from the same biological source.
• To bypass the need for external laboratory validation in sample mislabelling detection.

Main Methods:

• BAM-matcher compares genotype information embedded within BAM files.
• The tool analyzes sequence data to infer sample relatedness.
• It is designed for early integration into data processing pipelines.

Main Results:

• BAM-matcher accurately determines if two BAM files represent samples from the same source.
• The tool provides easily interpretable results.
• It offers a rapid method for sample identity verification.

Conclusions:

• BAM-matcher provides an efficient, in-silico solution for detecting mislabelled samples in high-throughput sequencing.
• The tool simplifies sample tracking and quality control in genomic studies.
• Its ease of use and interpretability make it suitable for widespread adoption.