scholarly journals VPMBench: a test bench for variant prioritization methods

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Andreas Ruscheinski ◽  
Anna Lena Reimler ◽  
Roland Ewald ◽  
Adelinde M. Uhrmacher
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Test Bench ◽  
Clinical Diagnostics ◽  
Tool Support ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Variant Prioritization ◽  
Whole Exome

Abstract Background Clinical diagnostics of whole-exome and whole-genome sequencing data requires geneticists to consider thousands of genetic variants for each patient. Various variant prioritization methods have been developed over the last years to aid clinicians in identifying variants that are likely disease-causing. Each time a new method is developed, its effectiveness must be evaluated and compared to other approaches based on the most recently available evaluation data. Doing so in an unbiased, systematic, and replicable manner requires significant effort. Results The open-source test bench “VPMBench” automates the evaluation of variant prioritization methods. VPMBench introduces a standardized interface for prioritization methods and provides a plugin system that makes it easy to evaluate new methods. It supports different input data formats and custom output data preparation. VPMBench exploits declaratively specified information about the methods, e.g., the variants supported by the methods. Plugins may also be provided in a technology-agnostic manner via containerization. Conclusions VPMBench significantly simplifies the evaluation of both custom and published variant prioritization methods. As we expect variant prioritization methods to become ever more critical with the advent of whole-genome sequencing in clinical diagnostics, such tool support is crucial to facilitate methodological research.

scholarly journals A Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0157718 ◽  
Author(s):  
Martin Christen Frølund Thomsen ◽  
Johanne Ahrenfeldt ◽  
Jose Luis Bellod Cisneros ◽  
Vanessa Jurtz ◽  
Mette Voldby Larsen ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clinical Diagnostics ◽  
Integrated System ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Analysis Platform

scholarly journals Harnessing the 100,000 Genomes Project whole genome sequencing data - an unbiased systematic tool to filter by biologically validated regions of functionality

2020 ◽  
Author(s):  
Sihao Xiao ◽  
Zhentian Kai ◽  
David Brown ◽  
Claire L Shovlin ◽  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clinical Diagnostics ◽  
Reference Sequence ◽  
Whole Genome Sequencing Data ◽  
Disease Genes ◽  
Full Potential ◽  
Hereditary Haemorrhagic Telangiectasia ◽  
Whole Genome ◽  
Sequencing Data

SUMMARYWhole genome sequencing (WGS) is championed by the UK National Health Service (NHS) to identify genetic variants that cause particular diseases. The full potential of WGS has yet to be realised as early data analytic steps prioritise protein-coding genes, and effectively ignore the less well annotated non-coding genome which is rich in transcribed and critical regulatory regions. To address, we developed a filter, which we call GROFFFY, and validated in WGS data from hereditary haemorrhagic telangiectasia patients within the 100,000 Genomes Project. Before filter application, the mean number of DNA variants compared to human reference sequence GRCh38 was 4,867,167 (range 4,786,039-5,070,340), and one-third lay within intergenic areas. GROFFFY removed a mean of 2,812,015 variants per DNA. In combination with allele frequency and other filters, GROFFFY enabled a 99.56% reduction in variant number. The proportion of intergenic variants was maintained, and no pathogenic variants in disease genes were lost. We conclude that the filter applied to NHS diagnostic samples in the 100,000 Genomes pipeline offers an efficient method to prioritise intergenic, intronic and coding gDNA variants. Reducing the overwhelming number of variants while retaining functional genome variation of importance to patients, enhances the near-term value of WGS in clinical diagnostics.

scholarly journals Detection of structural mosaicism from targeted and whole-genome sequencing data

2016 ◽  
Author(s):  
Daniel A. King ◽  
Alejandro Sifrim ◽  
Tomas W. Fitzgerald ◽  
Raheleh Rahbari ◽  
Emma Hobson ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Developmental Disorders ◽  
Large Fraction ◽  
Clinical Diagnostics ◽  
Next Generation Sequencing Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

ABSTRACTStructural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structural mosaic abnormalities using deviations in allele fraction and read coverage from next generation sequencing data. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) simulations were used to calculate detection performance across a range of mosaic event sizes, types, clonalities, and sequencing depths. The tool was applied to 4,911 patients with undiagnosed developmental disorders, and 11 events in 9 patients were detected. In 8 of 11 cases, mosaicism was observed in saliva but not blood, suggesting that assaying blood alone would miss a large fraction, possibly more than 50%, of mosaic diagnostic chromosomal rearrangements.

scholarly journals Comparison of three variant callers for human whole genome sequencing

2018 ◽  
Author(s):  
Anna Supernat ◽  
Oskar Valdimar Vidarsson ◽  
Vidar M. Steen ◽  
Tomasz Stokowy
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Single Gene ◽  
Reference Sample ◽  
Variant Calling ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Exome ◽  
Indel Calling

ABSTRACTTesting of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample.According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively.We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

scholarly journals Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew H. Bailey ◽  
◽  
William U. Meyerson ◽  
Lewis Jonathan Dursi ◽  
Liang-Bo Wang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Gc Content ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Exome ◽  
Cancer Genome Atlas

AbstractThe Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

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