scholarly journals GenomeGraphR: A user-friendly open-source web application for foodborne pathogen Whole Genome Sequencing data integration, analysis, and visualization.

2018 ◽  
Author(s):  
Moez Sanaa ◽  
Régis Pouillot ◽  
Francisco J Garces-Vega ◽  
Errol Strain ◽  
Jane M Van Doren
Keyword(s):  
Genome Sequencing ◽  
Pathogen Detection ◽  
Foodborne Pathogen ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
High Quality ◽  
User Friendly ◽  
Integration Analysis

Food safety risk assessments and large-scale epidemiological investigations have the potential to provide better and new types of information when whole genome sequence (WGS) data are effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have grown significantly through improvements in technology, coordination, and collaboration, such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not often coupled with high-quality epidemiological or food chain metadata. We have created a set of tools for cleaning, curation, integration, analysis and visualization of microbial genome sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates). GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a user-friendly interface that allows a user to query a variety of research questions such as, transmission sources and dynamics, global reach, and persistence of genotypes associated with contamination in the food supply and foodborne illness across time or space. The application is freely available (https://fda-riskmodels.foodrisk.org/genomegraphr/).

scholarly journals MutantHuntWGS: A Pipeline for Identifying Saccharomyces cerevisiae Mutations

2020 ◽  
Vol 10 (9) ◽  
pp. 3009-3014 ◽  
Author(s):  
Mitchell A Ellison ◽  
Jennifer L Walker ◽  
Patrick J Ropp ◽  
Jacob D Durrant ◽  
Karen M Arndt
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequence Alignments ◽  
Causal Variants ◽  
User Friendly ◽  
Analyze Data

Abstract MutantHuntWGS is a user-friendly pipeline for analyzing Saccharomyces cerevisiae whole-genome sequencing data. It uses available open-source programs to: (1) perform sequence alignments for paired and single-end reads, (2) call variants, and (3) predict variant effect and severity. MutantHuntWGS outputs a shortlist of variants while also enabling access to all intermediate files. To demonstrate its utility, we use MutantHuntWGS to assess multiple published datasets; in all cases, it detects the same causal variants reported in the literature. To encourage broad adoption and promote reproducibility, we distribute a containerized version of the MutantHuntWGS pipeline that allows users to install and analyze data with only two commands. The MutantHuntWGS software and documentation can be downloaded free of charge from https://github.com/mae92/MutantHuntWGS.

scholarly journals Candida auris Whole-Genome Sequence Benchmark Dataset for Phylogenomic Pipelines

2021 ◽  
Vol 7 (3) ◽  
pp. 214
Author(s):  
Rory M. Welsh ◽  
Elizabeth Misas ◽  
Kaitlin Forsberg ◽  
Meghan Lyman ◽  
Nancy A. Chow
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Epidemiological Data ◽  
Benchmark Dataset ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Candida Auris ◽  
Surveillance Networks

Candida auris is a multidrug-resistant pathogen that represents a serious public health threat due to its rapid global emergence, increasing incidence of healthcare-associated outbreaks, and high rates of antifungal resistance. Whole-genome sequencing and genomic surveillance have the potential to bolster C. auris surveillance networks moving forward. Laboratories conducting genomic surveillance need to be able to compare analyses from various national and international surveillance partners to ensure that results are mutually trusted and understood. Therefore, we established an empirical outbreak benchmark dataset consisting of 23 C. auris genomes to help validate comparisons of genomic analyses and facilitate communication among surveillance networks. Our outbreak benchmark dataset represents a polyclonal phylogeny with three subclades. The genomes in this dataset are from well-vetted studies that are supported by multiple lines of evidence, which demonstrate that the whole-genome sequencing data, phylogenetic tree, and epidemiological data are all in agreement. This C. auris benchmark set allows for standardized comparisons of phylogenomic pipelines, ultimately promoting effective C. auris collaborations.

scholarly journals MutantHuntWGS: A Pipeline for Identifying Saccharomyces cerevisiae Mutations

2020 ◽  
Author(s):  
Mitchell A. Ellison ◽  
Jennifer L. Walker ◽  
Patrick J. Ropp ◽  
Jacob D. Durrant ◽  
Karen M. Arndt
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequence Alignments ◽  
Causal Variants ◽  
User Friendly ◽  
Analyze Data

ABSTRACTMutantHuntWGS is a user-friendly pipeline for analyzing Saccharomyces cerevisiae whole-genome sequencing data. It uses available open-source programs to: (1) perform sequence alignments for paired and single-end reads, (2) call variants, and (3) predict variant effect and severity. MutantHuntWGS outputs a shortlist of variants while also enabling access to all intermediate files. To demonstrate its utility, we use MutantHuntWGS to assess multiple published datasets; in all cases, it detects the same causal variants reported in the literature. To encourage broad adoption and promote reproducibility, we distribute a containerized version of the MutantHuntWGS pipeline that allows users to install and analyze data with only two commands. The MutantHuntWGS software and documentation can be downloaded free of charge from https://github.com/mae92/MutantHuntWGS.

scholarly journals Whole-Genome Sequencing Data for Serotyping Escherichia coli—It's Time for a Change!

2015 ◽  
Vol 53 (8) ◽  
pp. 2402-2403 ◽  
Author(s):  
Claire Jenkins
Keyword(s):  
Escherichia Coli ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Content Type ◽  
Link Type ◽  
Article Type ◽  
User Friendly

The accessibility of whole-genome sequencing (WGS) presents the opportunity for national reference laboratories to provide a state-of-the-art public health surveillance service. The replacement of traditional serology-based typing ofEscherichia coliby WGS is supported by user-friendly, freely available data analysis Web tools. Anarticle in this issueof theJournal of Clinical Microbiology(K. G. Joensen, A. M. M. Tetzschner, A. Iguchi, F. M. Aarestrup, and F. Scheutz, J Clin Microbiol, 53:2410–2426, 2015,http://dx.doi.org/10.1128/JCM.00008-15) describes SerotypeFinder, an essential guide to serotypingE. coliin the 21st century.

scholarly journals A population-specific reference panel for improved genotype imputation in African Americans

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jared O’Connell ◽  
Taedong Yun ◽  
Meghan Moreno ◽  
Helen Li ◽  
Nadia Litterman ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
African Ancestry ◽  
Genotype Imputation ◽  
Whole Genome Sequencing Data ◽  
Specific Reference ◽  
Whole Genome ◽  
Sequencing Data ◽  
High Quality ◽  
Sub Saharan

AbstractThere is currently a dearth of accessible whole genome sequencing (WGS) data for individuals residing in the Americas with Sub-Saharan African ancestry. We generated whole genome sequencing data at intermediate (15×) coverage for 2,294 individuals with large amounts of Sub-Saharan African ancestry, predominantly Atlantic African admixed with varying amounts of European and American ancestry. We performed extensive comparisons of variant callers, phasing algorithms, and variant filtration on these data to construct a high quality imputation panel containing data from 2,269 unrelated individuals. With the exception of the TOPMed imputation server (which notably cannot be downloaded), our panel substantially outperformed other available panels when imputing African American individuals. The raw sequencing data, variant calls and imputation panel for this cohort are all freely available via dbGaP and should prove an invaluable resource for further study of admixed African genetics.

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