Standard operating procedure for somatic variant refinement of tumor sequencing data

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

Genetics in Medicine ◽

10.1038/s41436-018-0278-z ◽

2018 ◽

Vol 21 (4) ◽

pp. 972-981 ◽

Cited By ~ 24

Author(s):

Erica K. Barnell ◽

Peter Ronning ◽

Katie M. Campbell ◽

Kilannin Krysiak ◽

Benjamin J. Ainscough ◽

...

Keyword(s):

Operating Procedure ◽

Standard Operating Procedure ◽

Sequencing Data ◽

Somatic Variant ◽

Standard Operating

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Accuracy of somatic variant detection in multiregional tumor sequencing data

10.1101/655605 ◽

2019 ◽

Cited By ~ 1

Author(s):

Harald Detering ◽

Laura Tomás ◽

Tamara Prieto ◽

David Posada

Keyword(s):

Variant Calling ◽

Sequencing Data ◽

Single Nucleotide ◽

Somatic Variant ◽

Work Sample ◽

Tumor Sequencing ◽

Variant Detection ◽

Correction Step ◽

Multiple Samples ◽

Better Than

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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Numt identification and removal with RtN!

Bioinformatics ◽

10.1093/bioinformatics/btaa642 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5115-5116 ◽

Cited By ~ 2

Author(s):

August E Woerner ◽

Jennifer Churchill Cihlar ◽

Utpal Smart ◽

Bruce Budowle

Keyword(s):

Mitochondrial Genome ◽

Massively Parallel Sequencing ◽

Sequence Similarity ◽

Variant Calling ◽

Supplementary Information ◽

Mitochondrial Genomes ◽

Sequencing Data ◽

Read Mapping ◽

Genome Data ◽

Mitochondrial Sequences

Abstract Motivation Assays in mitochondrial genomics rely on accurate read mapping and variant calling. However, there are known and unknown nuclear paralogs that have fundamentally different genetic properties than that of the mitochondrial genome. Such paralogs complicate the interpretation of mitochondrial genome data and confound variant calling. Results Remove the Numts! (RtN!) was developed to categorize reads from massively parallel sequencing data not based on the expected properties and sequence identities of paralogous nuclear encoded mitochondrial sequences, but instead using sequence similarity to a large database of publicly available mitochondrial genomes. RtN! removes low-level sequencing noise and mitochondrial paralogs while not impacting variant calling, while competing methods were shown to remove true variants from mitochondrial mixtures. Availability and implementation https://github.com/Ahhgust/RtN Supplementary information Supplementary data are available at Bioinformatics online.

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VariFAST: a variant filter by automated scoring based on tagged-signatures

BMC Bioinformatics ◽

10.1186/s12859-019-3226-2 ◽

2019 ◽

Vol 20 (S22) ◽

Author(s):

Hang Zhang ◽

Ke Wang ◽

Juan Zhou ◽

Jianhua Chen ◽

Yizhou Xu ◽

...

Keyword(s):

Best Practices ◽

False Positive ◽

Variant Calling ◽

Sequencing Data ◽

Novel Approach ◽

Variant Filtering ◽

Ngs Data Analysis ◽

High Consistency ◽

Ngs Data ◽

Manual Review

Abstract Background Variant calling and refinement from whole genome/exome sequencing data is a fundamental task for genomics studies. Due to the limited accuracy of NGS sequencing and variant callers, IGV-based manual review is required for further false positive variant filtering, which costs massive labor and time, and results in high inter- and intra-lab variability. Results To overcome the limitation of manual review, we developed a novel approach for Variant Filter by Automated Scoring based on Tagged-signature (VariFAST), and also provided a pipeline integrating GATK Best Practices with VariFAST, which can be easily used for high quality variants detection from raw data. Using the bam and vcf files, VariFAST calculates a v-score by sum of weighted metrics causing false positive variations, and marks tags in the manner of keeping high consistency with manual review, for each variant. We validated the performance of VariFAST for germline variant filtering using the benchmark sequencing data from GIAB, and also for somatic variant filtering using sequencing data of both malignant carcinoma and benign adenomas as well. VariFAST also includes a predictive model trained by XGBOOST algorithm for germline variants refinement, which reveals better MCC and AUC than the state-of-the-art VQSR, especially outcompete in INDEL variant filtering. Conclusion VariFAST can assist researchers efficiently and conveniently to filter the false positive variants, including both germline and somatic ones, in NGS data analysis. The VariFAST source code and the pipeline integrating with GATK Best Practices are available at https://github.com/bioxsjtu/VariFAST.

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The JCVI standard operating procedure for annotating prokaryotic metagenomic shotgun sequencing data

Standards in Genomic Sciences ◽

10.4056/sigs.651139 ◽

2010 ◽

Vol 2 (2) ◽

pp. 229-237 ◽

Cited By ~ 46

Author(s):

David M. Tanenbaum ◽

Johannes Goll ◽

Sean Murphy ◽

Prateek Kumar ◽

Nikhat Zafar ◽

...

Keyword(s):

Operating Procedure ◽

Standard Operating Procedure ◽

Shotgun Sequencing ◽

Sequencing Data ◽

Standard Operating

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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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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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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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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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