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

Abstract

High-throughput processing of patient biosamples by next-generation sequencing and the comparison of molecular data with patient-level and sample-level clinical data require precise tracking and matching of sample identifiers throughout the biospecimen chain of custody and are critical to enabling robust interpretation of biomarker trial results. In addition to tracing individual steps in the sample and data processing workflows, bioinformatics solutions can be used to confirm that samples originate from the same patient. Here, the use of a bioinformatics workflow to identify matched samples originating from the same individual is showcased. The analysis workflow is suitable for any two or more pairs of NGS datasets to be compared and verified for patient sample origin. A scoring algorithm based on genome-wide comparisons of samples enables the user to determine whether two samples stem from the same individual. Specifically, single-nucleotide polymorphisms (SNPs) within selected linkage disequilibrium blocks are used to identify and compare samples. Threshold combinations for permissive and stringent selection of matched and mismatched samples were identified. The utility of this protocol was demonstrated through its application to the quality control and validation of clinical tumor tissue and blood samples, encompassing multiple omics modalities from over 2,000 patients.