Automated extraction of typing information for bacterial pathogens from whole genome sequence data: Neisseria meningitidis as an exemplar

Whole genome sequence (WGS) data are increasingly used to characterise bacterial pathogens. These data provide detailed information on the genotypes and likely phenotypes of aetiological agents, enabling the relationships of samples from potential disease outbreaks to be established precisely. However, the generation of increasing quantities of sequence data does not, in itself, resolve the problems that many microbiological typing methods have addressed over the last 100 years or so; indeed, providing large volumes of unstructured data can confuse rather than resolve these issues. Here we review the nascent field of storage of WGS data for clinical application and show how curated sequence-based typing schemes on websites have generated an infrastructure that can exploit WGS for bacterial typing efficiently. We review the tools that have been implemented within the PubMLST website to extract clinically useful, strain-characterisation information that can be provided to physicians and public health professionals in a timely, concise and understandable way. These data can be used to inform medical decisions such as how to treat a patient, whether to instigate public health action, and what action might be appropriate. The information is compatible both with previous sequence-based typing data and also with data obtained in the absence of WGS, providing a flexible infrastructure for WGS-based clinical microbiology.

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Rapid statistical methods for inferring intra- and inter-hospital transmission of nosocomial pathogens from whole genome sequence data

10.1101/442319 ◽

2018 ◽

Cited By ~ 1

Author(s):

Marianne Aspbury ◽

James Sciberras ◽

Jukka Corander ◽

Sion C. Bayliss ◽

Tjibbe Donker ◽

...

Keyword(s):

Statistical Methods ◽

Genome Sequence ◽

Phylogenetic Trees ◽

Sequence Data ◽

Bacterial Pathogens ◽

Whole Genome Sequence ◽

Whole Genome ◽

Transmission Networks ◽

Genome Sequence Data ◽

Hospital Transmission

AbstractWhole genome sequence (WGS) data for bacterial pathogens can provide evidence as to the source of nosocomial infection, and more specifically the ability to distinguish between intra- and inter-hospital transmission. This is currently achieved either through using SNP thresholds, which can lack statistical robustness, or by constructing phylogenetic trees, which can be computationally expensive and difficult to interpret. Here we compare two alternative statistical approaches using 1022 genomes of methicillin resistantStaphylococcus aureus(MRSA) clone ST22. In 71% of cases both methods predict the same hospital origin, which is also supported by the ML tree. Robust assignments are divided approximately equally between intra-hospital transmission and inter-hospital transmission. Our approaches are rapid and produce intuitive output that could inform on immediate infection control priorities, as well as providing long-term data on inter-hospital transmission networks. We discuss the strengths and weakness of our methods, and the generalisability of this approach.One Sentence SummaryWe present rapid statistical methods for distinguishing intra- versus inter-hospital transmission of bacterial pathogens using whole genome sequence data; these methods do not require the use of SNP thresholds or the generation and interpretation of phylogenetic trees.

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Epidemiology and whole genome sequencing of an ongoing point-sourceSalmonellaAgona outbreak associated with sushi consumption in western Sydney, Australia 2015

Epidemiology and Infection ◽

10.1017/s0950268817000693 ◽

2017 ◽

Vol 145 (10) ◽

pp. 2062-2071 ◽

Cited By ~ 12

Author(s):

C. K. THOMPSON ◽

Q. WANG ◽

S. K. BAG ◽

N. FRANKLIN ◽

C. T. SHADBOLT ◽

...

Keyword(s):

Public Health ◽

Case Series ◽

Food Samples ◽

Clinical Isolates ◽

Whole Genome Sequence ◽

Whole Genome ◽

Public Health Action ◽

South Wales ◽

Health Action ◽

Sydney Australia

SUMMARYDuring May 2015, an increase inSalmonellaAgona cases was reported from western Sydney, Australia. We examine the public health actions used to investigate and control this increase. A descriptive case-series investigation was conducted. Six outbreak cases were identified; all had consumed cooked tuna sushi rolls purchased within a western Sydney shopping complex. Onset of illness for outbreak cases occurred between 7 April and 24 May 2015.Salmonellawas isolated from food samples collected from the implicated premise and a prohibition order issued. No further cases were identified following this action. Whole genome sequence (WGS) analysis was performed on isolates recovered during this investigation, with additionalS.Agona isolates from sporadic-clinical cases and routine food sampling in New South Wales, January to July 2015. Clinical isolates of outbreak cases were indistinguishable from food isolates collected from the implicated sushi outlet. Five additional clinical isolates not originally considered to be linked to the outbreak were genomically similar to outbreak isolates, indicating the point-source contamination may have started before routine surveillance identified an increase. This investigation demonstrated the value of genomics-guided public health action, where near real-time WGS enhanced the resolution of the epidemiological investigation.

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Bioinformatic Analyses of Whole-Genome Sequence Data in a Public Health Laboratory

Emerging Infectious Diseases ◽

10.3201/eid2309.170416 ◽

2017 ◽

Vol 23 (9) ◽

pp. 1441-1445 ◽

Cited By ~ 20

Author(s):

Kelly F. Oakeson ◽

Jennifer Marie Wagner ◽

Michelle Mendenhall ◽

Andreas Rohrwasser ◽

Robyn Atkinson-Dunn

Keyword(s):

Public Health ◽

Genome Sequence ◽

Sequence Data ◽

Public Health Laboratory ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Faculty Opinions recommendation of Optimal algorithms for haplotype assembly from whole-genome sequence data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13339986.14707085 ◽

2011 ◽

Author(s):

Alejandro Schaffer

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Optimal Algorithms ◽

Genome Sequence Data ◽

Haplotype Assembly

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TIGER: inferring DNA replication timing from whole-genome sequence data

Bioinformatics ◽

10.1093/bioinformatics/btab166 ◽

2021 ◽

Cited By ~ 1

Author(s):

Amnon Koren ◽

Dashiell J Massey ◽

Alexa N Bracci

Keyword(s):

Dna Replication ◽

Genome Sequence ◽

Genomic Dna ◽

Sequence Data ◽

Replication Timing ◽

Whole Genome Sequence ◽

Supplementary Information ◽

Whole Genome ◽

Genome Sequence Data ◽

Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

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Whole genome sequence data of Bacillus australimaris strain B28A, isolated from Marine Water in India

Data in Brief ◽

10.1016/j.dib.2021.107240 ◽

2021 ◽

pp. 107240

Author(s):

Wael Ali Mohammed Hadi ◽

Boby T Edwin ◽

A Jayakumaran Nair

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Marine Water ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Whole genome sequence data of Mycobacterium tuberculosis XDR strain, isolated from patient in Kazakhstan

Data in Brief ◽

10.1016/j.dib.2020.106416 ◽

2020 ◽

Vol 33 ◽

pp. 106416

Author(s):

Asset Daniyarov ◽

Askhat Molkenov ◽

Saule Rakhimova ◽

Ainur Akhmetova ◽

Zhannur Nurkina ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Elucidating the genetic basis of an oligogenic birth defect using whole genome sequence data in a non-model organism, Bubalus bubalis

Scientific Reports ◽

10.1038/srep39719 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 10

Author(s):

Lynsey K. Whitacre ◽

Jesse L. Hoff ◽

Robert D. Schnabel ◽

Sara Albarella ◽

Francesca Ciotola ◽

...

Keyword(s):

Genome Sequence ◽

Birth Defect ◽

Genetic Basis ◽

Sequence Data ◽

Model Organism ◽

Bubalus Bubalis ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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46 Footprints of Selection in Angus and Hanwoo Beef Cattle Using Imputed Whole Genome Sequence Data

Journal of Animal Science ◽

10.1093/jas/skab235.042 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 25-25

Author(s):

Muhammad Yasir Nawaz ◽

Rodrigo Pelicioni Savegnago ◽

Cedric Gondro

Keyword(s):

Beef Cattle ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Fixation Index ◽

Whole Genome ◽

Extended Haplotype Homozygosity ◽

Extended Haplotype ◽

Genome Sequence Data ◽

Genomic Regions

Abstract In this study, we detected genome wide footprints of selection in Hanwoo and Angus beef cattle using different allele frequency and haplotype-based methods based on imputed whole genome sequence data. Our dataset included 13,202 Angus and 10,437 Hanwoo animals with 10,057,633 and 13,241,550 imputed SNPs, respectively. A subset of data with 6,873,624 common SNPs between the two populations was used to estimate signatures of selection parameters, both within (runs of homozygosity and extended haplotype homozygosity) and between (allele fixation index, extended haplotype homozygosity) the breeds in order to infer evidence of selection. We observed that correlations between various measures of selection ranged between 0.01 to 0.42. Assuming these parameters were complementary to each other, we combined them into a composite selection signal to identify regions under selection in both beef breeds. The composite signal was based on the average of fractional ranks of individual selection measures for every SNP. We identified some selection signatures that were common between the breeds while others were independent. We also observed that more genomic regions were selected in Angus as compared to Hanwoo. Candidate genes within significant genomic regions may help explain mechanisms of adaptation, domestication history and loci for important traits in Angus and Hanwoo cattle. In the future, we will use the top SNPs under selection for genomic prediction of carcass traits in both breeds.

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