Accuracy of somatic variant detection in multiregional tumor sequencing data

AbstractMultiregional bulk sequencing data is necessary to characterize intratumor genetic heterogeneity. Novel somatic variant calling approaches aim to address the particular characteristics of multiregional data, but it remains unclear to which extent they improve compared to single-sample strategies. Here we compared the performance of 16 single-nucleotide variant calling approaches on multiregional sequencing data under different scenarios with in-silico and real sequencing reads, including varying sequencing coverage and increasing levels of spatial clonal admixture. Under the conditions simulated, methods that use information across multiple samples do not necessarily perform better than some of the standard calling methods that work sample by sample. Nonetheless, our results indicate that under difficult conditions, Mutect2 in multisample mode, in combination with a correction step, seems to perform best. Our analysis provides data-driven guidance for users and developers of somatic variant calling tools.

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SomatoSim: precision simulation of somatic single nucleotide variants

BMC Bioinformatics ◽

10.1186/s12859-021-04024-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marwan A. Hawari ◽

Celine S. Hong ◽

Leslie G. Biesecker

Keyword(s):

High Throughput Sequencing ◽

Variant Calling ◽

Simulated Data ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Somatic Variant ◽

Simulation Tools ◽

Gold Standard Dataset ◽

High Level

Abstract Background Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. Results SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. Conclusions SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim.

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Standard operating procedure for somatic variant refinement of tumor sequencing data

10.1101/266262 ◽

2018 ◽

Cited By ~ 1

Author(s):

Erica K. Barnell ◽

Peter Ronning ◽

Katie M. Campbell ◽

Kilannin Krysiak ◽

Benjamin J. Ainscough ◽

...

Keyword(s):

Massively Parallel Sequencing ◽

Variant Calling ◽

Standard Operating Procedure ◽

Sequencing Data ◽

Optimal Method ◽

Somatic Variant ◽

Standard Operating ◽

Variant Detection ◽

Manual Review

AbstractPurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.MethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.ResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewer’s confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.ConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

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Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples

Nature Communications ◽

10.1038/s41467-021-22466-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Chuanyi Zhang ◽

Mohammed El-Kebir ◽

Idoia Ochoa

Keyword(s):

Cancer Genomics ◽

Low Frequency ◽

Variant Calling ◽

High Sensitivity ◽

Single Sample ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Additional Time ◽

Single Nucleotide ◽

Multiple Samples

AbstractIntra-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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neoepiscope improves neoepitope prediction with multivariant phasing

Bioinformatics ◽

10.1093/bioinformatics/btz653 ◽

2019 ◽

Vol 36 (3) ◽

pp. 713-720 ◽

Cited By ~ 5

Author(s):

Mary A Wood ◽

Austin Nguyen ◽

Adam J Struck ◽

Kyle Ellrott ◽

Abhinav Nellore ◽

...

Keyword(s):

False Negative ◽

Supplementary Information ◽

Supplementary File ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Somatic Variant ◽

Negative Results ◽

Multiple Datasets ◽

False Negative Results

Abstract Motivation The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and implementation neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2–8. Supplementary File 2 contains Supplementary Tables 2–6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary information Supplementary data are available at Bioinformatics online.

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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks

10.1101/2019.12.29.890418 ◽

2019 ◽

Cited By ~ 1

Author(s):

Umair Ahsan ◽

Qian Liu ◽

Li Fang ◽

Kai Wang

Keyword(s):

Deep Neural Network ◽

Deep Neural Networks ◽

Variant Calling ◽

Sequencing Data ◽

Long Reads ◽

Novel Variants ◽

Long Read ◽

Variant Detection ◽

Genomic Regions ◽

Haplotype Information

AbstractVariant (SNPs/indels) detection from high-throughput sequencing data remains an important yet unresolved problem. Long-read sequencing enables variant detection in difficult-to-map genomic regions that short-read sequencing cannot reliably examine (for example, only ~80% of genomic regions are marked as “high-confidence region” to have SNP/indel calls in the Genome In A Bottle project); however, the high per-base error rate poses unique challenges in variant detection. Existing methods on long-read data typically rely on analyzing pileup information from neighboring bases surrounding a candidate variant, similar to short-read variant callers, yet the benefits of much longer read length are not fully exploited. Here we present a deep neural network called NanoCaller, which detects SNPs by examining pileup information solely from other nonadjacent candidate SNPs that share the same long reads using long-range haplotype information. With called SNPs by NanoCaller, NanoCaller phases long reads and performs local realignment on two sets of phased reads to call indels by another deep neural network. Extensive evaluation on 5 human genomes (sequenced by Nanopore and PacBio long-read techniques) demonstrated that NanoCaller greatly improved performance in difficult-to-map regions, compared to other long-read variant callers. We experimentally validated 41 novel variants in difficult-to-map regions in a widely-used benchmarking genome, which cannot be reliably detected previously. We extensively evaluated the run-time characteristics and the sensitivity of parameter settings of NanoCaller to different characteristics of sequencing data. Finally, we achieved the best performance in Nanopore-based variant calling from MHC regions in the PrecisionFDA Variant Calling Challenge on Difficult-to-Map Regions by ensemble calling. In summary, by incorporating haplotype information in deep neural networks, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing data.

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SMuRF: Portable and accurate ensemble-based somatic variant calling

10.1101/270413 ◽

2018 ◽

Cited By ~ 2

Author(s):

Weitai Huang ◽

Yu Amanda Guo ◽

Karthik Muthukumar ◽

Probhonjon Baruah ◽

Meimei Chang ◽

...

Keyword(s):

Point Mutations ◽

Variant Calling ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

Somatic Variant ◽

Level Data ◽

Machine Learning Approach ◽

Cancer Types ◽

User Friendly ◽

Improved Accuracy

ABSTARCTSummarySMuRF is an ensemble method for prediction of somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes. The method integrates predictions and auxiliary features from different somatic mutation callers using a Random Forest machine learning approach. SMuRF is trained on community-curated tumor whole genome sequencing data, is robust across cancer types, and achieves improved accuracy for both SNV and indel predictions of genome and exome-level data. The software is user-friendly and portable by design, operating as an add-on to the community-developed bcbio-nextgen somatic variant calling [email protected]

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Comprehensive and accurate genetic variant identification from contaminated and low coverage Mycobacterium tuberculosis whole genome sequencing data

10.1101/2021.09.16.460612 ◽

2021 ◽

Author(s):

Tim H. Heupink ◽

Lennert Verboven ◽

Robin M. Warren ◽

Annelies Van Rie

Keyword(s):

Mycobacterium Tuberculosis ◽

Genetic Variants ◽

Sequence Data ◽

Variant Calling ◽

Sequencing Data ◽

Single Nucleotide ◽

Variant Filtering ◽

Contamination Levels ◽

Genomic Regions ◽

Low Coverage

AbstractImproved understanding of the genomic variants that allow Mycobacterium tuberculosis (Mtb) to acquire drug resistance, or tolerance, and increase its virulence are important factors in controlling the current tuberculosis epidemic. Current approaches to Mtb sequencing however cannot reveal Mtb’s full genomic diversity due to the strict requirements of low contamination levels, high Mtb sequence coverage, and elimination of complex regions.We developed the XBS (compleX Bacterial Samples) bioinformatics pipeline which implements joint calling and machine-learning-based variant filtering tools to specifically improve variant detection in the important Mtb samples that do not meet these criteria, such as those from unbiased sputum samples. Using novel simulated datasets, that permit exact accuracy verification, XBS was compared to the UVP and MTBseq pipelines. Accuracy statistics showed that all three pipelines performed equally well for sequence data that resemble those obtained from high depth coverage and low-level contamination culture isolates. In the complex genomic regions however, XBS accurately identified 9.0% more single nucleotide polymorphisms and 8.1% more single nucleotide insertions and deletions than the WHO-endorsed unified analysis variant pipeline. XBS also had superior accuracy for sequence data that resemble those obtained directly from sputum samples, where depth of coverage is typically very low and contamination levels are high. XBS was the only pipeline not affected by low depth of coverage (5-10×), type of contamination and excessive contamination levels (>50%). Simulation results were confirmed using WGS data from clinical samples, confirming the superior performance of XBS with a higher sensitivity (98.8%) when analysing culture isolates and identification of 13.9% more variable sites in WGS data from sputum samples as compared to MTBseq, without evidence for false positive variants when ribosomal RNA regions were excluded.The XBS pipeline facilitates sequencing of less-than-perfect Mtb samples. These advances will benefit future clinical applications of Mtb sequencing, especially whole genome sequencing directly from clinical specimens, thereby avoiding in vitro biases and making many more samples available for drug resistance and other genomic analyses. The additional genetic resolution and increased sample success rate will improve genome-wide association studies and sequence-based transmission studies.Impact statementMycobacterium tuberculosis (Mtb) DNA is usually extracted from culture isolates to obtain high quantities of non-contaminated DNA but this process can change the make-up of the bacterial population and is time-consuming. Furthermore, current analytic approaches exclude complex genomic regions where DNA sequences are repeated to avoid inference of false positive genetic variants, which may result in the loss of important genetic information.We designed the compleX Bacterial Sample (XBS) variant caller to overcome these limitations. XBS employs joint variant calling and machine-learning-based variant filtering to ensure that high quality variants can be inferred from low coverage and highly contaminated genomic sequence data obtained directly from sputum samples. Simulation and clinical data analyses showed that XBS performs better than other pipelines as it can identify more genetic variants and can handle complex (low depth, highly contaminated) Mtb samples. The XBS pipeline was designed to analyse Mtb samples but can easily be adapted to analyse other complex bacterial samples.Data summarySimulated sequencing data have been deposited in SRA BioProject PRJNA706121. All detailed findings are available in the Supplementary Material. Scripts for running the XBS variant calling core are available on https://github.com/TimHHH/XBS The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

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2-kupl: mapping-free variant detection from DNA-seq data of matched samples

BMC Bioinformatics ◽

10.1186/s12859-021-04185-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yunfeng Wang ◽

Haoliang Xue ◽

Christine Pourcel ◽

Yang Du ◽

Daniel Gautheret

Keyword(s):

Dna Sequences ◽

Reference Genome ◽

Point Mutations ◽

Variant Calling ◽

Low Complexity ◽

Structural Variants ◽

Sequencing Data ◽

Bacterial Strains ◽

Two Samples ◽

Variant Detection

Abstract Background The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Results We introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves higher accuracy than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome sequencing data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease. Conclusions We developed a mapping-free protocol for variant calling between matched DNA-seq samples. Our protocol is suitable for variant detection in unmappable genome regions or in the absence of a reference genome.

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A Method to Evaluate the Quality of Clinical Gene-Panel Sequencing Data for Single-Nucleotide Variant Detection

Journal of Molecular Diagnostics ◽

10.1016/j.jmoldx.2017.06.001 ◽

2017 ◽

Vol 19 (5) ◽

pp. 651-658 ◽

Cited By ~ 11

Author(s):

Chung Lee ◽

Joon S. Bae ◽

Gyu H. Ryu ◽

Nayoung K.D. Kim ◽

Donghyun Park ◽

...

Keyword(s):

Single Nucleotide Variant ◽

Gene Panel ◽

Sequencing Data ◽

Single Nucleotide ◽

Variant Detection ◽

Gene Panel Sequencing ◽

Panel Sequencing

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Comparison of single-nucleotide variants identified by Illumina and Oxford Nanopore technologies in the context of a potential outbreak of Shiga toxin–producing Escherichia coli

GigaScience ◽

10.1093/gigascience/giz104 ◽

2019 ◽

Vol 8 (8) ◽

Cited By ~ 8

Author(s):

David R Greig ◽

Claire Jenkins ◽

Saheer Gharbia ◽

Timothy J Dallman

Keyword(s):

Escherichia Coli ◽

Shiga Toxin ◽

Variant Calling ◽

Reference Database ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Oxford Nanopore ◽

Variant Filtering ◽

Oxford Nanopore Technologies

Abstract Background We aimed to compare Illumina and Oxford Nanopore Technology sequencing data from the 2 isolates of Shiga toxin–producing Escherichia coli (STEC) O157:H7 to determine whether concordant single-nucleotide variants were identified and whether inference of relatedness was consistent with the 2 technologies. Results For the Illumina workflow, the time from DNA extraction to availability of results was ∼40 hours, whereas with the ONT workflow serotyping and Shiga toxin subtyping variant identification were available within 7 hours. After optimization of the ONT variant filtering, on average 95% of the discrepant positions between the technologies were accounted for by methylated positions found in the described 5-methylcytosine motif sequences, CC(A/T)GG. Of the few discrepant variants (6 and 7 difference for the 2 isolates) identified by the 2 technologies, it is likely that both methodologies contain false calls. Conclusions Despite these discrepancies, Illumina and Oxford Nanopore Technology sequences from the same case were placed on the same phylogenetic location against a dense reference database of STEC O157:H7 genomes sequenced using the Illumina workflow. Robust single-nucleotide polymorphism typing using MinION-based variant calling is possible, and we provide evidence that the 2 technologies can be used interchangeably to type STEC O157:H7 in a public health setting.

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