Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

Abstract

Background Next-generation sequencing (NGS) represents a significant advancement in clinical genetics. However, its use creates several technical, data interpretation and management challenges. It is essential to follow a consistent data analysis pipeline to achieve the highest possible accuracy and avoid false variant calls. Herein, we aimed to compare the performance of twenty-eight combinations of NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM, Bowtie2, Stampy), variant calling (GATK-HaplotypeCaller, GATK-UnifiedGenotyper, SAMtools) and interval padding (null, 50 bp, 100 bp) methods, along with a commercially available pipeline (BWA Enrichment, Illumina®). Fourteen germline DNA samples from breast cancer patients were sequenced using a targeted NGS panel approach and subjected to data analysis. Results We highlight that interval padding is required for the accurate detection of intronic variants including spliceogenic pathogenic variants (PVs). In addition, using nearly default parameters, the BWA Enrichment algorithm, failed to detect these spliceogenic PVs and a missense PV in the TP53 gene. We also recommend the BWA-MEM algorithm for sequence alignment, whereas variant calling should be performed using a combination of variant calling algorithms; GATK-HaplotypeCaller and SAMtools for the accurate detection of insertions/deletions and GATK-UnifiedGenotyper for the efficient detection of single nucleotide variant calls. Conclusions These findings have important implications towards the identification of clinically actionable variants through panel testing in a clinical laboratory setting, when dedicated bioinformatics personnel might not always be available. The results also reveal the necessity of improving the existing tools and/or at the same time developing new pipelines to generate more reliable and more consistent data.

Short-read aligner performance in germline variant identification

“…A number of studies have evaluated the performance of variant analysis toolchains using short-read aligners as “black box” tools, without consideration of the effect of aligner runtime configuration and performance on downstream variant analysis (e.g. Chen et al 2019 , Hwang et al 2019 , Schilbert et al 2020 , Zhao et al 2020 , Zanti et al 2021 , Barbitoff et al 2022 , Betschart et al 2022 ). The inconsistent results among such studies imply that default parameter settings are not always optimal in every variant analysis toolchain.…”

Onkopipe: A Snakemake Based DNA-Sequencing Pipeline for Clinical Variant Analysis in Precision Medicine

“…Recent studies [16,17] have shown that BWA-MEM achieved better sensitivity and false positives rate for DNA sequencing data, making it the optimal choice for Onkopipe, which focuses on DNA sequencing with an average read length of more than 70bp, such as FFPE materials that usually have a length larger than 100bp. (Table 1)…”

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

“…equally accurate on simulated data, but fail to achieve the accuracy of alignment-based methods for quantification of human RNA-seq [10]. Various short-read aligners have been tested and compared for many applications [11][12][13][14][15][16][17]. Aligners such as HiSat2 [18,19] and STAR [8,9] specialize in mapping RNA-seq to genome sequences, a task that requires virtual splicing of read sequences into exons and generation of alignments that skip over introns in the reference.…”