Detecting Inter-Individual Contamination and Mismatches in Multiomics Next-Generation Sequencing Data

Area of Science:

• Genomics
• Bioinformatics
• Clinical Trials

Background:

• High-throughput sequencing of patient biosamples generates vast molecular data.
• Accurate sample tracking and matching are crucial for interpreting clinical trial results.
• Bioinformatics solutions can verify sample origin and improve data integrity.

Purpose of the Study:

• To present a bioinformatics workflow for identifying matched samples from the same individual.
• To enable robust verification of patient sample origin in next-generation sequencing (NGS) datasets.
• To support precise sample tracking throughout the biospecimen chain of custody.

Main Methods:

• Development of a genome-wide comparison scoring algorithm for sample verification.
• Utilizing single-nucleotide polymorphisms (SNPs) within linkage disequilibrium blocks for sample identification.
• Establishing threshold combinations for stringent and permissive matching of samples.

Main Results:

• Demonstrated utility in quality control and validation of clinical tumor and blood samples.
• Successfully applied to multi-omics data from over 2,000 patients.
• Identified specific SNP-based criteria for accurate sample matching.

Conclusions:

• The presented bioinformatics workflow reliably confirms sample origin, enhancing data accuracy.
• This method is applicable to various NGS datasets and clinical sample types.
• It provides a critical tool for robust interpretation of biomarker trial results and quality control.