scholarly journals Large-scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management

2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Andrew J. Page ◽  
Alison E. Mather ◽  
Thanh Le-Viet ◽  
Emma J. Meader ◽  
Nabil-Fareed Alikhan ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Large Scale ◽  
Control Measures ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Outbreak Management ◽  
Infection Control Measures ◽  
Care Facilities ◽  
Surrounding Areas ◽  
Public Health Epidemiology

The COVID-19 pandemic has spread rapidly throughout the world. In the UK, the initial peak was in April 2020; in the county of Norfolk (UK) and surrounding areas, which has a stable, low-density population, over 3200 cases were reported between March and August 2020. As part of the activities of the national COVID-19 Genomics Consortium (COG-UK) we undertook whole genome sequencing of the SARS-CoV-2 genomes present in positive clinical samples from the Norfolk region. These samples were collected by four major hospitals, multiple minor hospitals, care facilities and community organizations within Norfolk and surrounding areas. We combined clinical metadata with the sequencing data from regional SARS-CoV-2 genomes to understand the origins, genetic variation, transmission and expansion (spread) of the virus within the region and provide context nationally. Data were fed back into the national effort for pandemic management, whilst simultaneously being used to assist local outbreak analyses. Overall, 1565 positive samples (172 per 100 000 population) from 1376 cases were evaluated; for 140 cases between two and six samples were available providing longitudinal data. This represented 42.6 % of all positive samples identified by hospital testing in the region and encompassed those with clinical need, and health and care workers and their families. In total, 1035 cases had genome sequences of sufficient quality to provide phylogenetic lineages. These genomes belonged to 26 distinct global lineages, indicating that there were multiple separate introductions into the region. Furthermore, 100 genetically distinct UK lineages were detected demonstrating local evolution, at a rate of ~2 SNPs per month, and multiple co-occurring lineages as the pandemic progressed. Our analysis: identified a discrete sublineage associated with six care facilities; found no evidence of reinfection in longitudinal samples; ruled out a nosocomial outbreak; identified 16 lineages in key workers which were not in patients, indicating infection control measures were effective; and found the D614G spike protein mutation which is linked to increased transmissibility dominates the samples and rapidly confirmed relatedness of cases in an outbreak at a food processing facility. The large-scale genome sequencing of SARS-CoV-2-positive samples has provided valuable additional data for public health epidemiology in the Norfolk region, and will continue to help identify and untangle hidden transmission chains as the pandemic evolves.

scholarly journals Large scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Alison E. Mather ◽  
Thanh Le-Viet ◽  
Emma J. Meader ◽  
Nabil-Fareed Alikhan ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Food Processing ◽  
Large Scale ◽  
Control Measures ◽  
Clinical Samples ◽  
List Type ◽  
Care Facilities ◽  
Processing Facility ◽  
Surrounding Areas ◽  
Public Health Epidemiology

AbstractThe COVID-19 pandemic has spread rapidly throughout the world. In the UK, the initial peak was in April 2020; in the county of Norfolk (UK) and surrounding areas, which has a stable, low-density population, over 3,200 cases were reported between March and August 2020. As part of the activities of the national COVID-19 Genomics Consortium (COG-UK) we undertook whole genome sequencing of the SARS-CoV-2 genomes present in positive clinical samples from the Norfolk region. These samples were collected by four major hospitals, multiple minor hospitals, care facilities and community organisations within Norfolk and surrounding areas. We combined clinical metadata with the sequencing data from regional SARS-CoV-2 genomes to understand the origins, genetic variation, transmission and expansion (spread) of the virus within the region and provide context nationally. Data were fed back into the national effort for pandemic management, whilst simultaneously being used to assist local outbreak analyses. Overall, 1,565 positive samples (172 per 100,000 population) from 1,376 cases were evaluated; for 140 cases between two and six samples were available providing longitudinal data. This represented 42.6% of all positive samples identified by hospital testing in the region and encompassed those with clinical need, and health and care workers and their families. 1,035 cases had genome sequences of sufficient quality to provide phylogenetic lineages. These genomes belonged to 26 distinct global lineages, indicating that there were multiple separate introductions into the region. Furthermore, 100 genetically-distinct UK lineages were detected demonstrating local evolution, at a rate of ∼2 SNPs per month, and multiple co-occurring lineages as the pandemic progressed. Our analysis: identified a discrete sublineage associated with 6 care facilities; found no evidence of reinfection in longitudinal samples; ruled out a nosocomial outbreak; identified 16 lineages in key workers which were not in patients indicating infection control measures were effective; found the D614G spike protein mutation which is linked to increased transmissibility dominates the samples and rapidly confirmed relatedness of cases in an outbreak at a food processing facility. The large-scale genome sequencing of SARS-CoV-2-positive samples has provided valuable additional data for public health epidemiology in the Norfolk region, and will continue to help identify and untangle hidden transmission chains as the pandemic evolves.Major pointsIn Norfolk and surrounding regions:100 distinct UK lineages were identified.16 UK lineages found in key workers were not observed in patients or in community care.172 genomes from SARS-CoV-2 positive samples sequenced per 100,000 population representing 42.6% of all positive cases.SARS-CoV-2 genomes from 1035 cases sequenced to a high quality.Only 5 countries, out of 103, have sequenced more SARS-CoV-2 genomes than have been sequenced in Norfolk for this paper.Samples covered the entire first wave, March to August 2020.Stable evolutionary rate of 2 SNPs per month.D614G mutation is the dominant genotype and associated with increased transmission.No evidence of reinfection in 42 cases with longitudinal samples.WGS identified a sublineage associated with care facilities.WGS ruled out nosocomial outbreaks.Rapid WGS confirmed the relatedness of cases from an outbreak at a food processing facility.

scholarly journals The role of genomics in understanding COVID-19 outbreaks in long term care facilities

2020 ◽  
Author(s):  
Dinesh Aggarwal ◽  
Richard Myers ◽  
William L. Hamilton ◽  
Tehmina Bharucha ◽  
Niamh Tumelty ◽  
...  
Keyword(s):  
Long Term Care ◽  
Control Measures ◽  
Sequencing Data ◽  
Term Care ◽  
Genomic Epidemiology ◽  
Infection Control Measures ◽  
Care Facilities ◽  
Epidemiology Studies

A review was undertaken of all genomic epidemiology studies on COVID-19 in long term care facilities (LTCF) that have been published to date. It was found that staff and residents were usually infected with identical, or near identical, SARS-CoV-2 genomes. Outbreaks usually involved one predominant lineage, and the same lineages persisted in LTCFs despite infection control measures. Outbreaks were most commonly due to single or few introductions followed by spread rather than a series of seeding events from the community into LTCFs. Sequencing of samples taken consecutively from the same cases showed persistence of the same genome sequence indicating that the sequencing technique was robust over time. When combined with local epidemiology, genomics facilitated likely transmission sources to be better characterised. Transmission between LTCFs was detected in multiple studies. The mortality rate amongst residents was high in all cases, regardless of the lineage. Bioinformatics methods were inadequate in one third of the studies reviewed, and reproducing the analyses was difficult as sequencing data were not available in many cases.

scholarly journals Universal Masking to Control Healthcare-Associated Transmission of SARS-CoV-2

2021 ◽  
pp. 1-24
Author(s):  
Eliza R. Thompson ◽  
Faith S. Williams ◽  
Pat A. Giacin ◽  
Shay Drummond ◽  
Eric Brown ◽  
...  
Keyword(s):  
Large Scale ◽  
Infection Prevention ◽  
Index Patient ◽  
Field Investigation ◽  
Point Prevalence ◽  
Control Measures ◽  
Prevention Measures ◽  
Infection Control Measures ◽  
Va Health Care System ◽  
Healthcare Associated

Abstract Objective: To assess extent of a healthcare-associated outbreak of SARS-CoV-2 and evaluate effectiveness of infection control measures, including universal masking Design: Outbreak investigation including 4 large-scale point-prevalence surveys Setting: Integrated VA Health Care System with 2 facilities and 330 beds Participants: Index patient and 250 exposed patients and staff Methods: We identified exposed patients and staff and classified them as probable and confirmed cases based on symptoms and testing. We performed a field investigation and assessment of patient and staff interactions to develop probable transmission routes. Infection prevention interventions implemented included droplet and contact precautions, employee quarantine, and universal masking with medical and cloth facemasks. Four point-prevalence surveys of patient and staff subsets were conducted using real-time reverse-transcriptase polymerase chain reaction for SARS-CoV-2. Results: Among 250 potentially exposed patients and staff, 14 confirmed cases of Covid-19 were identified. Patient roommates and staff with prolonged patient contact were most likely to be infected. The last potential date of transmission from staff to patient was day 22, the day universal masking was implemented. Subsequent point-prevalence surveys in 126 patients and 234 staff identified 0 patient cases and 5 staff cases of Covid-19, without evidence of healthcare-associated transmission. Conclusions: Universal masking with medical facemasks was effective in preventing further spread of SARS-CoV-2 in our facility in conjunction with other traditional infection prevention measures.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

scholarly journals ANTIMICROBIAL SUSCEPTIBILITY AND DETECTION METHODS FOR THE EXTENDED‑SPECTRUM β-LACTAMASES PRODUCING ENTEROBACTERIACEAE FROM CLINICAL SAMPLES

2018 ◽  
Vol 11 (5) ◽  
pp. 139
Author(s):  
Kaviyarasan G ◽  
Rajamanikandan Kcp ◽  
Sabarimuthu M ◽  
Ramya S ◽  
Arvind Prasanth D
Keyword(s):  
Infection Control ◽  
Bacterial Infections ◽  
Control Measures ◽  
Detection Methods ◽  
Clinical Samples ◽  
Healthcare Settings ◽  
Infection Control Measures ◽  
Extended Spectrum ◽  
Drug Choice ◽  
Synergy Test

Objectives: Detection of extended-spectrum β-lactamases (ESBLs) is crucial for the infection control and antibiotic choice in healthcare settings. The aim of this study is to develop a standardized, inexpensive, and simple approach that is able to detect ESBL-producing Enterobacteriaceae isolates.Methods: Isolates those were resistant to at least one of the three indicator cephalosporins (cefotaxime, cefpodoxime, and ceftazidime) were tested for ESBL production using the double disc synergy test (DDST), combined disc synergy test (CDST) test and genotypic detection of the responsible gene for the ESBL.Result: From 64 isolates, 28 were resistant to cephalosporins. In 28 isolates, 23 were positive in CDST but in the DDST 18 were showing ESBL positive. 10 were positive in both CDST and DDST.Conclusion: Resistance to cephalosporins, which are the drug choice to treat mixed bacterial infections by the Enterobacteriaceae of which disseminate rapidly being plasmid mediated. Hence, it is necessary that rapid detection of ESBL should be done and immediate infection control measures should be implemented to prevent their dissemination.

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

A heuristic platform for clinical interpretation of cancer genome sequencing data.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10502-10502
Author(s):  
Eliezer Mendel Van Allen ◽  
Nikhil Wagle ◽  
Gregory Kryukov ◽  
Alexis Ramos ◽  
Gad Getz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genome Sequencing ◽  
Heuristic Algorithm ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
Clinical Samples ◽  
Whole Genome ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Clinical Interpretation

10502 Background: The ability to identify and effectively sort the full spectrum of biologically and therapeutically relevant genetic alterations identified by massively parallel sequencing may improve cancer care. A major challenge involves rapid and rational categorization of data-intensive output, including somatic mutations, insertions/deletions, copy number alterations, and rearrangements into ranked categories for clinician review. Methods: A database of clinically actionable alterations was created, consisting of over 100 annotated genes known to undergo somatic genomic alterations in cancer that may impact clinical decision-making. A heuristic algorithm was developed, which selectively identifies somatic alterations based on the clinically actionable alterations database. Remaining variants are sorted based on additional heuristics, including high priority alterations based on presence in the Cancer Gene Census, biologically significant cancer genes based on presence in COSMIC or MSigDB, and low priority alterations in the same gene family as biologically significant cancer genes. The heuristic algorithm was applied to whole exome sequencing data of clinical samples and whole genome sequencing data from a cohort of prostate cancer samples processed using established Broad Institute pipelines. Results: Application of the heuristic algorithm to the prostate cancer whole genome rearrangement data identified 172 (out of 5978) rearrangements involving actionable genes (averaging 2-3 events per tumor). Furthermore, two clinical samples processed prospectively were analyzed, yielding three potentially actionable alterations for clinical review. Conclusions: The heuristic model for clinical interpretation of next generation sequencing data may facilitate rapid analysis of tumor genomic information for clinician review by identifying and prioritizing alterations that can directly impact care. Our platform can also be applied to research data to prospectively explore clinically relevant findings from existing cohorts. Future analytical approaches using heuristic or probabilistic algorithms should underpin a robust prospective assessment of clinical cancer genome data.

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