A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis

Methods that use a linear genome reference for genome sequencing data analysis are reference biased. In the field of clinical genetics for rare diseases, a resulting reduction in genotyping accuracy in some regions has likely prevented the resolution of some cases. Pangenome graphs embed population variation into a reference structure. While pangenome graphs have helped to reduce reference mapping bias, further performance improvements are possible. We introduce VG-Pedigree, a pedigree-aware workflow based on the pangenome-mapping tool of Giraffe (Sirén et al. 2021) and the variant-calling tool DeepTrio (Kolesnikov et al. 2021) using a specially-trained model for Giraffe-based alignments. We demonstrate mapping and variant calling improvements in both single-nucleotide variants (SNVs) and insertion and deletion (INDEL) variants over those produced by alignments created using BWA MEM to a linear-reference and Giraffe mapping to a pangenome graph containing data from the 1000 Genomes Project. We have also adapted and upgraded the deleterious-variant (DV) detecting methods and programs of Gu et al. into a streamlined workflow (Gu et al. 2019). We used these workflows in combination to detect small lists of candidate DVs among 15 family quartets and quintets of the Undiagnosed Diseases Program (UDP). All candidate DVs that were previously diagnosed using the mendelian models covered by the previously published Gu et al. methods were recapitulated by these workflows. The results of these experiments indicate a slightly greater absolute count of DVs are detected in the proband population than in their matched unaffected siblings.

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SvABA: Genome-wide detection of structural variants and indels by local assembly

10.1101/105080 ◽

2017 ◽

Cited By ~ 9

Author(s):

Jeremiah Wala ◽

Pratiti Bandopadhayay ◽

Noah Greenwald ◽

Ryan O’Rourke ◽

Ted Sharpe ◽

...

Keyword(s):

Variant Calling ◽

Accurate Method ◽

Structural Variants ◽

Sequencing Data ◽

Cancer Driver ◽

Insertion And Deletion ◽

Genome Wide ◽

Cancer Genomes ◽

Local Assembly ◽

Genomic Regions

AbstractStructural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at-scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA’s performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in ~4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.

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Whisper: Read sorting allows robust mapping of sequencing data

10.1101/240358 ◽

2017 ◽

Author(s):

Sebastian Deorowicz ◽

Agnieszka Debudaj-Grabysz ◽

Adam Gudyś ◽

Szymon Grabowski

Keyword(s):

Reference Genome ◽

Variant Calling ◽

Real Data ◽

Supplementary Information ◽

Sequencing Data ◽

Suffix Arrays ◽

Link Type ◽

Mapping Tool ◽

Reverse Complement ◽

Comparable Accuracy

AbstractMotivationMapping reads to a reference genome is often the first step in a sequencing data analysis pipeline. Mistakes made at this computationally challenging stage cannot be recovered easily.ResultsWe present Whisper, an accurate and high-performant mapping tool, based on the idea of sorting reads and then mapping them against suffix arrays for the reference genome and its reverse complement. Employing task and data parallelism as well as storing temporary data on disk result in superior time efficiency at reasonable memory requirements. Whisper excels at large NGS read collections, in particular Illumina reads with typical WGS coverage. The experiments with real data indicate that our solution works in about 15% of the time needed by the well-known Bowtie2 and BWA-MEM tools at a comparable accuracy (validated in variant calling pipeline).AvailabilityWhisper is available for free from https://github.com/refresh-bio/Whisper or http://sun.aei.polsl.pl/REFRESH/Whisper/[email protected] informationSupplementary data are available at publisher Web site.

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Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutations

10.1101/2020.06.20.163113 ◽

2020 ◽

Author(s):

HoJoon Lee ◽

Ahmed Shuaibi ◽

John M. Bell ◽

Dmitri S. Pavlichin ◽

Hanlee P. Ji

Keyword(s):

Genome Sequencing ◽

Somatic Mutations ◽

Variant Calling ◽

Cancer Genome ◽

Sequencing Data ◽

Deletion Mutations ◽

Insertion And Deletion ◽

Cancer Genome Sequencing ◽

Significant Difference ◽

Model Tuning

ABSTRACTThe cancer genome sequencing has led to important discoveries such as identifying cancer gene. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome 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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xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

10.1101/295071 ◽

2018 ◽

Cited By ~ 7

Author(s):

Jesse Farek ◽

Daniel Hughes ◽

Adam Mansfield ◽

Olga Krasheninina ◽

Waleed Nasser ◽

...

Keyword(s):

Next Generation Sequencing ◽

Rapid Development ◽

Variant Calling ◽

Supplementary Information ◽

Data Generation ◽

Next Generation ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Homogeneous Sample ◽

Generation Sequencing

AbstractMotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.ResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30× short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.AvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/[email protected] informationSupplementary data are available at Bioinformatics online.

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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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Bivartect: accurate and memory-saving breakpoint detection by direct read comparison

Bioinformatics ◽

10.1093/bioinformatics/btaa059 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2725-2730

Author(s):

Keisuke Shimmura ◽

Yuki Kato ◽

Yukio Kawahara

Keyword(s):

Genome Editing ◽

High Throughput Sequencing ◽

Variant Calling ◽

Simulated Data ◽

Supplementary Information ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Node ◽

Single Nucleotide ◽

Target Sites

Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online.

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dv-trio: a family-based variant calling pipeline using DeepVariant

Bioinformatics ◽

10.1093/bioinformatics/btaa116 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3549-3551 ◽

Cited By ~ 1

Author(s):

Eddie K K Ip ◽

Clinton Hadinata ◽

Joshua W K Ho ◽

Eleni Giannoulatou

Keyword(s):

Genetic Model ◽

Variant Calling ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Mutation Discovery ◽

Family Trio ◽

Sequencing Studies ◽

Family Based

Abstract Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. Results To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. Availability and implementation dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo

Nature Communications ◽

10.1038/s41467-021-26938-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David Lähnemann ◽

Johannes Köster ◽

Ute Fischer ◽

Arndt Borkhardt ◽

Alice C. McHardy ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Single Cells ◽

Variant Calling ◽

Sequencing Data ◽

Computationally Efficient ◽

Single Nucleotide Variants ◽

Efficient Manner ◽

Single Nucleotide ◽

Amplification Bias

AbstractAccurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable—because computationally efficient—manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo

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Variant calling tool evaluation for variable size indel calling from next generation whole genome and targeted sequencing data

10.1101/2021.07.15.452444 ◽

2021 ◽

Author(s):

Ning Wang ◽

Vladislav Lysenkov ◽

Katri Orte ◽

Veli Kairisto ◽

Juhani Aakko ◽

...

Keyword(s):

Variant Calling ◽

Read Length ◽

Next Generation ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

High Coverage ◽

Wide Range ◽

Long Read ◽

Indel Calling ◽

Next Generation Sequencing Ngs

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools on indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage, coupled with specific variant calling tools.

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