Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach.
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ABSTRACT: Background
The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging.Results
Here, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01-0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals.Conclusion
PBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.
SUBMITTER: Dudley JN
PROVIDER: S-EPMC8028235 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
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| S-EPMC7336445 | biostudies-literature
Project description:Intra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor's mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss' improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics.
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| S-EPMC5324109 | biostudies-literature
Project description:BackgroundArtifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.ResultsSimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased.ConclusionsFilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.
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Project description:Linked-read sequencing enables greatly improves haplotype assembly over standard paired-end analysis. The detection of mosaic single-nucleotide variants benefits from haplotype assembly when the model is informed by the mapping between constituent reads and linked reads. Samovar evaluates haplotype-discordant reads identified through linked-read sequencing, thus enabling phasing and mosaic variant detection across the entire genome. Samovar trains a random forest model to score candidate sites using a dataset that considers read quality, phasing, and linked-read characteristics. Samovar calls mosaic single-nucleotide variants (SNVs) within a single sample with accuracy comparable with what previously required trios or matched tumor/normal pairs and outperforms single-sample mosaic variant callers at minor allele frequency 5%-50% with at least 30X coverage. Samovar finds somatic variants in both tumor and normal whole-genome sequencing from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.
| S-EPMC6609817 | biostudies-literature
Project description:Sequence variants in recombinant biopharmaceuticals may have a relevant and unpredictable impact on clinical safety and efficacy. Hence, their sensitive analysis is important throughout bioprocess development. The two stage analytical approach presented here provides a quick multi clone comparison of candidate production cell lines as a first stage, followed by an in-depth analysis including identification and quantitation of aberrant sequence variants of selected clones as a second stage. We show that the differential analysis is a suitable tool for sensitive and fast batch to batch comparison of recombinant proteins. The optimized approach allows for detection of not only single amino acid substitutions in unmodified peptides, but also substitutions in posttranslational modified peptides such as glycopeptides, for detection of truncated or elongated sequence variants as well as double amino acid substitutions or substitution with amino acid structural isomers within one peptide. In two case studies we were able to detect sequence variants of different origin down to a sub percentage level. One of the sequence variants (Thr ? Asn) could be correlated to a cytosine to adenine substitution at DNA (desoxyribonucleic acid) level. In the second case we were able to correlate the sub percentage substitution (Phe ? Tyr) to amino acid limitation in the chemically defined fermentation medium.
| S-EPMC3391300 | biostudies-literature
Project description:MotivationWhole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods.ResultsWe made 10 single nucleotide polymorphism and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10-15 kb, but the error rate of post-filtered calls is reduced to 1 in 100-200 kb without significant compromise on the sensitivity.Availability and implementationBWA-MEM alignment and raw variant calls are available at http://bit.ly/1g8XqRt scripts and miscellaneous data at https://github.com/lh3/varcmp.Contacthengli@broadinstitute.orgSupplementary informationSupplementary data are available at Bioinformatics online.
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