HLA‐poll: An ensemble suite of human leukocyte antigen‐prediction tools for whole‐exome and whole‐genome sequencing data

2020 ◽  
Vol 42 (5) ◽  
Author(s):  
Xinming Zhuo ◽  
Kevin Quann ◽  
Kayla Parr ◽  
Rachel Stewart ◽  
Warren Shlomchik ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Human Leukocyte ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Prediction Tools ◽  
Whole Exome

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 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.

scholarly journals Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish

Heredity ◽  
2021 ◽  
Author(s):  
Axel Jensen ◽  
Mette Lillie ◽  
Kristofer Bergström ◽  
Per Larsson ◽  
Jacob Höglund
Keyword(s):  
Genetic Diversity ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Peripheral Populations ◽  
Whole Genome ◽  
Runs Of Homozygosity ◽  
Sequencing Data ◽  
Isolated Populations ◽  
Native Populations

AbstractThe use of genetic markers in the context of conservation is largely being outcompeted by whole-genome data. Comparative studies between the two are sparse, and the knowledge about potential effects of this methodology shift is limited. Here, we used whole-genome sequencing data to assess the genetic status of peripheral populations of the wels catfish (Silurus glanis), and discuss the results in light of a recent microsatellite study of the same populations. The Swedish populations of the wels catfish have suffered from severe declines during the last centuries and persists in only a few isolated water systems. Fragmented populations generally are at greater risk of extinction, for example due to loss of genetic diversity, and may thus require conservation actions. We sequenced individuals from the three remaining native populations (Båven, Emån, and Möckeln) and one reintroduced population of admixed origin (Helge å), and found that genetic diversity was highest in Emån but low overall, with strong differentiation among the populations. No signature of recent inbreeding was found, but a considerable number of short runs of homozygosity were present in all populations, likely linked to historically small population sizes and bottleneck events. Genetic substructure within any of the native populations was at best weak. Individuals from the admixed population Helge å shared most genetic ancestry with the Båven population (72%). Our results are largely in agreement with the microsatellite study, and stresses the need to protect these isolated populations at the northern edge of the distribution of the species.

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

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