Year: 2026
Type: article
Abstract: Background: Bioinformatics pipelines influence putative false-positive calls (technical artifacts) in clinical germline next-generation sequencing; however, the impact of workflow updates on variant detection behavior in clinical panels remains unclear.Methods: We analyzed 114 clinical samples that were sequenced using a 4,815-gene panel.Starting with identical FASTQ files, we compared a legacy workflow (Burrows-Wheeler aligner [BWA] aln+Genome Analysis Toolkit [GATK] UnifiedGenotyper) and a modern workflow (BWA-maximal exact matches [MEM]+GATK HaplotypeCaller), aligners under a fixed caller, and callers under a fixed aligner.Putative technical artifacts were defined as recurrent variants (≥10/114 samples) that were rare (gnomAD minor allele frequency <0.001) or absent from gnomAD and the Korean reference database (K1055E).Concordance with the GIAB HG001 (NA12878) truth set was evaluated within the panel target regions that intersected with GIAB high-confidence regions.Results: The legacy workflow produced 1,691 recurrent rare/novel variants that were not reproduced by the modern workflow.The transition/ transversion ratio shifted from 3.097 to 2.393 for the same TruSight One target design, indicating workflow-dependent differences in the single nucleotide variant spectrum.Indel call counts increased 7.37-fold, reflecting a change in call yield rather than a direct improvement in accuracy.In controlled comparisons, BWA aln and FreeBayes generated more recurrent rare/novel calls than BWA-MEM/Bowtie2 and HaplotypeCaller.In the GIAB benchmark, the pipelines showed comparable concordance with a slightly higher single nucleotide polymorphism concordance in the legacy workflow and increased indel detection in the modern workflow.Conclusions: Updating the aligner-caller workflow markedly reduced recurrent rare/novel calls, consistent with technical artifacts in clinical germline panel testing.This finding highlights the importance of validating workflow updates.
Author: Jaewoong Lee
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7166160226
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