SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA

Koçkan, Can; Hach, Faraz; Sarrafi, Iman; Bell, Robert H.; McConeghy, Brian; Beja, Kevin; Haegert, Anne; Wyatt, Alexander W.; Volik, Stanislav V.; N., Kim; Collins, Colin C.; Şahinalp, S. Cenk

doi:10.1093/bioinformatics/btw536

Cited by 54 publications

(48 citation statements)

References 22 publications

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“…Thus, it is a cost-effective strategy for screening large cohorts of patients and it is particularly suited for point-of-care clinical applications [1], for example in conjunction with the Ion AmpliSeq Cancer Hotspot Panel. Given its translational potential, there is a real need to improve the variant calling workflow and recently a number of methods have been developed to deal specifically with Ion Torrent data [12,13,14].…”

Section: Conclusion Backgroundmentioning

confidence: 99%

“…On WGS and ctDNA sample, we compare AmpliSolve to SiNVICT [12], a tool that has been shown to be effective in detecting mutations at very low VAF in Ion Torrent data. We run…”

Section: Snv Callers Tested For Comparisonmentioning

confidence: 99%

“…we test a=2, 4,5,8,12,16. We then apply the Poisson models previously trained on the 120 normal samples to predict variants at each position and for each alternative allele and consider only AmpliSolve calls with a 'PASS' quality flag.…”

Section: Synthetic Variants Test For Tpr Estimationmentioning

confidence: 99%

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Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Kleftogiannis

Punta

Jayaram

et al. 2018

Preprint

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AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well.AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

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Section: Conclusion Backgroundmentioning

confidence: 99%

“…On WGS and ctDNA sample, we compare AmpliSolve to SiNVICT [12], a tool that has been shown to be effective in detecting mutations at very low VAF in Ion Torrent data. We run…”

Section: Snv Callers Tested For Comparisonmentioning

confidence: 99%

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Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Kleftogiannis

Punta

Jayaram

et al. 2018

Preprint

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“…Then we give it a list of 15 variants at different positions and at different frequencies to introduce them in the final reads. Finally, we used 2 raw-reads-based variant callers: SiNVICT (Kockan et al (2017)) and OutLyzer (Muller et al (2016)) and two UMI-based variant callers: DeepSNVMiner (Andrews et al (2016)) and UMI-VarCal (Sater et al (2020)) in order to compare the 4 tools performance and demonstrate that UMI-Gen correctly inserts the given variants at their respective positions and at the correct frequencies.…”

Section: Introductionmentioning

confidence: 99%

UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries

Sater

Viailly

Lecroq

et al. 2020

Preprint

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Motivation: With Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. A lot of different variant callers are publicly available and usually do a good job at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-reads based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy. Results: We present UMI-Gen, a UMI-based reads simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user. Availability: The entire pipeline is available at https://gitlab.com/vincent-sater/umigen-master under MIT

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“…Instead, thanks to an innovative homemade pileup algorithm specifically designed to treat the UMI tags present in the reads, UMI-VarCal is faster than both raw-reads-based and UMI-based variant callers. To test our tool, we compare it against two of the best raw-reads-based variant callers that only need the tumor sample to call variants, SiNVICT Kockan et al (2017) and outLyzer Muller et al (2016) and specifically designed to detect lowfrequency variants. We also demonstrate that it can be as -if not more -sensitive as other UMI-based variant callers by comparing it against DeepSNVMiner.…”

Section: Introductionmentioning

confidence: 99%

UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries

Sater

Viailly

Lecroq

et al. 2019

Preprint

View full text Add to dashboard Cite

Motivation: Next Generation Sequencing (NGS) has become the go-to standard method for the detection of Single Nucleotide Variants (SNV) in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies.These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of Unique Molecular Identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artifactual variants and accurately call low frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. Results: We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that don't rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions.

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SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA

Abstract: cenk@sfu.ca.

Cited by 54 publications

References 22 publications

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries

UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries

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