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Background: Treating cancer depends in part on identifying the mutations driving each patient's disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients' tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed.
Methods: Because ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illumina's TruSight 170 hybrid-capture panel and the amplification-based Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple replicates of each assay, to test reproducibility. Finally, we applied four different freely-available somatic single-nucleotide variant (SNV) callers to the data, along with the vendor-recommended callers for each sequencing platform.
Results: We did not observe major differences in variant calling success within the regions that each panel covers, but there were substantial differences between callers. All had high sensitivity for true SNVs, but numerous and non-overlapping false positives. Overriding certain default parameters to make them consistent between callers substantially reduced discrepancies, but still resulted in high false positive rates. Intersecting results from multiple replicates or from different variant callers eliminated most false positives, while maintaining sensitivity.
Conclusions: Reproducibility and accuracy of targeted clinical sequencing results depend less on sequencing platform and panel than on variability between replicates and downstream bioinformatics. Differences in variant callers' default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend employing multiple variant calling pipelines and/or analyzing replicate samples, as this greatly decreases false positive calls.
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http://dx.doi.org/10.1186/s12920-020-00803-z | DOI Listing |
Brief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
View Article and Find Full Text PDFMicrobiol Spectr
September 2025
Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.
is a commensal bacterium that colonizes the gut of humans and animals and is a major opportunistic pathogen, known for causing multidrug-resistant healthcare-associated infections (HAIs). Its ability to thrive in diverse environments and disseminate antimicrobial resistance genes (ARGs) across ecological niches highlights the importance of understanding its ecological, evolutionary, and epidemiological dynamics. The CRISPR2 locus has been used as a valuable marker for assessing clonality and phylogenetic relationships in .
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
Advances in Oxford Nanopore Technologies (ONT) with the introduction of the r10.4.1 flow cell have reduced the sequencing error rates to <1%.
View Article and Find Full Text PDFMol Genet Genomics
September 2025
Human Phenome Institute, MOE Key Laboratory of Contemporary Anthropology, Zhangjiang Fudan International Innovation Center, Fudan University, 825 Zhangheng Road, Shanghai, 201203, China.
Accurate variant calling is essential for next-generation sequencing (NGS)-based diagnosis of rare diseases, yet most benchmarking studies have focused on standard cell lines or trio-based samples, with limited relevance to sporadic cases. Here, we systematically compared the performance of DeepVariant and GATK HaplotypeCaller in two Chinese cohorts of patients with sporadic epilepsy (EP) and autism spectrum disorder (ASD). DeepVariant exhibited higher precision and sensitivity in detecting single nucleotide variants (SNVs), while GATK showed a distinct advantage in identifying rare variants, which are often key to understanding the genetic basis of rare diseases.
View Article and Find Full Text PDFOral pre-exposure prophylaxis (PrEP) denotes an effective strategy to reduce the risk of HIV infection. However, many individuals encounter difficulties adhering to the once-daily regimen, which highlights the need for a broader portfolio of PrEP options. The novel HIV capsid inhibitor lenacapavir (LEN), when injected every six month, has shown potential in the recently completed clinical trials.
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