Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Barnell, Erica K.; Ronning, Peter; Campbell, Katie M.; Krysiak, Kilannin; Ainscough, Benjamin; Sheta, Lana; Pema, Shahil; Schmidt, Alina; Richters, Megan M.; Cotto, Kelsy C.; Danos, Arpad; Ramirez, Cody; Skidmore, Zachary L.; Spies, Nicholas C.; Hundal, Jasreet; Sediqzad, Malik S.; Kunisaki, Jason; Gomez, Felicia; Trani, Lee; Matlock, Matthew K.; Wagner, Alex H.; Swamidass, S. Joshua; Griffith, Malachi; Griffith, Obi L.

doi:10.1038/s41436-018-0278-z

Cited by 72 publications

(66 citation statements)

References 37 publications

(30 reference statements)

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“…Reads in these regions tend to have lower mapping quality due to multiple alignments. Multiple mismatches (MM) [52] are another feature to avoid this effect. For the polishing step, unlike iDES, we found the most best-fitted distribution of stereotypical noise through large samples.…”

Section: Discussionmentioning

confidence: 99%

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

Deng

Xu³

et al. 2020

BMC Bioinformatics

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Background: Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelicfraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based 'liquid biopsy'. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations. Results: Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted. Conclusions: Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

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Section: Discussionmentioning

confidence: 99%

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

Deng

Xu³

et al. 2020

BMC Bioinformatics

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“…The improved calibration of BATCAVE posterior probabilities compared to MuTect provides several advantages. In practice, called variants are often manually reviewed to further reduce false positives [53]. Improved calibration enables users to focus review on the most questionable variants.…”

Section: Tests Using Real Tumor Datamentioning

confidence: 99%

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

Mannakee

Gutenkunst

2019

Preprint

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Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis, and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.

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“…For small deletions/indels we applied the same criteria with more stringent values for minimum VAF=0.01, minimum coverage=200, minimum number of supporting reads=6, signal to noise ratio=25, number of duplexes=2. We also filtered out recurrent variants that fall into high discrepancy genomic regions (HDR) as described in Bernell et al 22 and blacklisted these sites in further analyses.…”

Section: Bioinformatics Pipeline For Ultra-sensitive Detection Of Snvmentioning

confidence: 99%

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Kleftogiannis

Liew

et al. 2019

Preprint

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Analysis of circulating cell-free DNA (cfDNA) data has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with freely-available bioinformatics pipelines that provide ultra-sensitive detection of single nucleotide variants (SNVs), and reliable identification of copy number variations (CNVs) directly from plasma DNA. In a cohort of 35 BC patients, our approach detected actionable driver and clonal SNVs at low (~0.5%) frequency levels in cfDNA that were concordant (83.3%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

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Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Cited by 72 publications

References 37 publications

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

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