smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers

Xu, Chang; Gu, Xiujing; Padmanabhan, Raghavendra; Wu, Zhong; Quan, Peng; DiCarlo, John; Wang, Yexun

doi:10.1093/bioinformatics/bty790

Cited by 75 publications

(75 citation statements)

References 30 publications

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“…The smCounter2 pipeline, specially designed for the accurate calling of low‐frequency variants from QIAseq‐based targeted sequencing data, was employed for data processing and variant calling. UMI tags enabled error correction for most of the sequencing and PCR errors, and a refining algorithm was used to further amend those errors that were not correctable with UMI.…”

Section: Methodsmentioning

confidence: 99%

Noninvasive prenatal paternity testing by means of SNP‐based targeted sequencing

Tam¹,

Chan²,

Tsang³

et al. 2020

Prenatal Diagnosis

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Objective To develop a method for noninvasive prenatal paternity testing based on targeted sequencing of single nucleotide polymorphisms (SNPs). Method SNPs were selected based on population genetics data. Target‐SNPs in cell‐free DNA extracted from maternal blood (maternal cfDNA) were analyzed by targeted sequencing wherein target enrichment was based on multiplex amplification using QIAseq Targeted DNA Panels with Unique Molecular Identifiers. Fetal SNP genotypes were called using a novel bioinformatics algorithm, and the combined paternity indices (CPIs) and resultant paternity probabilities were calculated. Results Fetal SNP genotypes obtained from targeted sequencing of maternal cfDNA were 100% concordant with those from amniotic fluid‐derived fetal genomic DNA. From an initial panel of 356 target‐SNPs, an average of 148 were included in paternity calculations in 15 family trio cases, generating paternity probabilities of greater than 99.9999%. All paternity results were confirmed by short‐tandem‐repeat analysis. The high specificity of the methodology was validated by successful paternity discrimination between biological fathers and their siblings and by large separations between the CPIs calculated for the biological fathers and those for 60 unrelated men. Conclusion The novel method is highly effective, with substantial improvements over similar approaches in terms of reduced number of target‐SNPs, increased accuracy, and reduced costs.

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Section: Methodsmentioning

confidence: 99%

Noninvasive prenatal paternity testing by means of SNP‐based targeted sequencing

Tam¹,

Chan²,

Tsang³

et al. 2020

Prenatal Diagnosis

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“…We sequenced reference material NA12878 in the 11 panels using the standard QIAseq targeted DNA workflow on Illumina platforms. The reads were processed by the GeneGlobe QIAseq DNA data analysis pipeline [12]. After BWA-MEM mapping (GRCh37/hg19), we grouped reads from each primer by the 5' mapping location (or binding site) and counted the number of unique molecular identifiers (UMI) that were attached during library preparation (Fig.…”

Section: Data Preparationmentioning

confidence: 99%

Predicting primer and panel off-target rate in QIAseq targeted DNA panels using convolutional neural networks

Padmanabhan

Reinecke

et al. 2020

Preprint

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In QIAseq targeted DNA panels, synthetic primers (short single-strand DNA sequences) are used for target enrichment via complementary DNA binding. Off-target priming could occur in this process when a primer binds to some loci where the DNA sequences are identical or very similar to the target template. These off-target DNA segments go through the rest of the workflow, wasting sequencing resources in unwanted regions. Off-target cannot be avoided if some segments of the target region are repetitive throughout the genome, nor can it be quantified until after sequencing. But if off-target rates can be prospectively predicted, scientists can make informed decisions about investment on high off-target panels. We developed pordle (predicting off-target rate with deep learning and epcr07), a convolutional neural network (CNN) model to predict off-target binding events of a given primer. The neural network was trained using 10 QIAseq DNA panels with 29,274 unique primers and then tested on an independent QIAseq panel with 7,576 primers. The model predicted a 10.5% off-target rate for the test panel, a -0.1% bias from the true value of 10.6%. The model successfully selected the better primer (in terms of off-target rate) for 89.2% of 3,835 pairs of close-by primers in the test panel whose off-target rates differ by at least 10%. The order-preserving property may help panel developers select the optimal primer from a group of candidates, which is a common task in panel design.

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“…Another UMI-based dataset used for our evaluation is the physical mixture (mixed at 1:99 ratio) of the reference materials NA12878 and NA24385 which are both well-characterized for germline polymorphisms 45 . Xu et al 45 have shown that smCounter2 outperformed all other applicable methods on this dataset. Therefore, we compared UVC with only smCounter2.…”

Section: Performance Comparison On Tumor-only Sequencing Datasets Witmentioning

confidence: 99%

“…Meanwhile, UVC was not aware of any such error profiles before running with its default parameters. Figure 2 shows that UVC outperformed smCounter2 for calling SNVs and InDels, even though smCounter2 was enhanced with UMI-specific repetitive region filters 45 . Notably, without the hard filter by EROR, UVC would generate approximately 10 times more false positive SNV calls, even though the hard filter by EROR rejects approximately 10% true positive SNV calls (Fig.…”

Section: Performance Comparison On Tumor-only Sequencing Datasets Witmentioning

confidence: 99%

“…2: Evaluation of UVC and smCounter2 on the samples N13532 and N0261 which are both 1%/99% mixtures of the reference materials NA12878/NA24385. This dataset was described, used, and published by smCounter2 and is publicly available under the accession SRP153933 45 . Homozygous and heterozygous germline polymorphisms present in NA12878 but not in NA24385 are considered to be tumor variants with fractions of 1% and 0.5%, respectively.…”

Section: Performance Comparison On Wgs Datasets Of Tumor-normal Pairsmentioning

confidence: 99%

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Calling small variants with universality and Bayesian-frequentist hybridism

Zhao

Wang

et al. 2020

Preprint

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We describe UVC (https://github.com/genetronhealth/uvc), an open-source method for calling small somatic variants. UVC is aware of both unique molecular identifiers (UMIs) and the tumor-matched normal sample. UVC utilizes the following power-law universality that we discovered: allele fraction is inversely proportional to the cubic root of variant-calling error rate. Moreover, UVC utilizes pseudo-neural network (PNN). PNN is similar to deep neural network but does not require any training data. UVC outperformed Mageri and smCounter2, the state-of-the-art UMI-aware variant callers, on the tumor-only datasets used for publishing these two variant callers. Also, UVC outperformed Mutect2 and Strelka2, the state-of-the-art variant callers for tumor-normal pairs, on the Genome-in-a-Bottle somatic truth sets. UVC outperformed Mutect2 and Strelka2 on 21 in silico mixtures simulating 21 combinations of tumor purity and normal purity. Performance is measured by using sensitivity-specificity trade off for all called variants. The improved variant calls generated by UVC from previously published UMI-based sequencing data are able to provide additional biological insight about DNA damage repair. The versatility and robustness of UVC makes it a useful tool for variant calling in clinical settings.

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smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 75 publications

References 30 publications

Noninvasive prenatal paternity testing by means of SNP‐based targeted sequencing

Noninvasive prenatal paternity testing by means of SNP‐based targeted sequencing

Predicting primer and panel off-target rate in QIAseq targeted DNA panels using convolutional neural networks

Calling small variants with universality and Bayesian-frequentist hybridism

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