Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data

AbstractPathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.One Sentence SummaryWe quantify SARS-CoV-2 spread in Switzerland based on genome sequences from our nation-wide sequencing effort.

Download Full-text

The power and limitations of genomics to track COVID-19 outbreaks: a case study from New Zealand

10.1101/2020.10.28.20221853 ◽

2020 ◽

Author(s):

Jemma L Geoghegan ◽

Jordan Douglas ◽

Xiaoyun Ren ◽

Matthew Storey ◽

James Hadfield ◽

...

Keyword(s):

New Zealand ◽

Genomic Data ◽

Mitigation Strategies ◽

Genomic Sequencing ◽

Primary Role ◽

Sequencing Data ◽

Strategic Innovation ◽

Innovation Fund ◽

Health Research Council ◽

First Time

SummaryBackgroundReal-time genomic sequencing has played a major role in tracking the global spread and local transmission of SARS-CoV-2, contributing greatly to disease mitigation strategies. After effectively eliminating the virus, New Zealand experienced a second outbreak of SARS-CoV-2 in August 2020. During this August outbreak, New Zealand utilised genomic sequencing in a primary role to support its track and trace efforts for the first time, leading to a second successful elimination of the virus.MethodsWe generated the genomes of 80% of the laboratory-confirmed samples of SARS-CoV-2 from New Zealand’s August 2020 outbreak and compared these genomes to the available global genomic data.FindingsGenomic sequencing was able to rapidly identify that the new COVID-19 cases in New Zealand belonged to a single cluster and hence resulted from a single introduction. However, successful identification of the origin of this outbreak was impeded by substantial biases and gaps in global sequencing data.InterpretationAccess to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks.FundingThis work was funded by the Ministry of Health of New Zealand, New Zealand Ministry of Business, Innovation and Employment COVID-19 Innovation Acceleration Fund (CIAF-0470), ESR Strategic Innovation Fund and the New Zealand Health Research Council (20/1018 and 20/1041).

Download Full-text

Investigating the first wave of the COVID-19 pandemic in Ukraine using epidemiological and genomic sequencing data

10.1101/2021.03.05.21253014 ◽

2021 ◽

Author(s):

Yuriy Gankin ◽

Vladimir Koniukhovskii ◽

Alina Nemira ◽

Gerardo Chowell ◽

Thomas A. Weppelmann ◽

...

Keyword(s):

Public Health ◽

Genomic Data ◽

World Health ◽

Viral Population ◽

Genomic Sequencing ◽

Sequencing Data ◽

Population Number ◽

The Public ◽

Health Measures ◽

The Impact

AbstractThe novel coronavirus SARS-CoV-2 emerged in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced with varying degrees of success a variety of social distancing interventions to slow the virus spread. Investigating the role of non-pharmaceutical interventions on COVID-19 transmission in different settings is an important research. While most transmission modeling studies have focused on the dynamics in China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. This study starts to fill this gap by analyzing the characteristics of the first epidemic wave in Ukraine using mathematical and statistical models together with epidemiological and genomic sequencing data. Using an agent-based model, the trajectory of the first wave in terms of cases and deaths and explore the impact of quarantine strategies via simulation studies have been characterized. The implemented stochastic model for epidemic counts suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. The genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic with eight distinct transmission clusters identified. The basic reproduction number for the epidemic has been estimated independently both from case counts data and from genomic data. The findings support the hypothesis that, the public health measures did not have a decreasing effect on the existing viral population number at the time of implementation, since strains were detected after the quarantine date. However, the public health measures did help to prevent the appearance of new (and potentially more virulent) SARS-CoV-2 variants in Ukraine.

Download Full-text

Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study (Preprint)

10.2196/preprints.15040 ◽

2019 ◽

Author(s):

Borim Ryu ◽

Soo-Yong Shin ◽

Rong-Min Baek ◽

Jeong-Whun Kim ◽

Eunyoung Heo ◽

...

Keyword(s):

Electronic Health Record ◽

Technical Specification ◽

Genomic Data ◽

International Standards ◽

Practical Implementation ◽

Genomic Sequencing ◽

Health Record ◽

Sequencing Data ◽

Genomic Test ◽

Electronic Health

BACKGROUND To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. OBJECTIVE This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. METHODS In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. RESULTS We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. CONCLUSIONS To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.

Download Full-text

Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study

Journal of Medical Internet Research ◽

10.2196/15040 ◽

2020 ◽

Vol 22 (8) ◽

pp. e15040 ◽

Cited By ~ 1

Author(s):

Borim Ryu ◽

Soo-Yong Shin ◽

Rong-Min Baek ◽

Jeong-Whun Kim ◽

Eunyoung Heo ◽

...

Keyword(s):

Electronic Health Record ◽

Technical Specification ◽

Genomic Data ◽

International Standards ◽

Practical Implementation ◽

Genomic Sequencing ◽

Health Record ◽

Sequencing Data ◽

Genomic Test ◽

Electronic Health

Background To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. Objective This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. Methods In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. Results We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. Conclusions To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.

Download Full-text

GABAC: an arithmetic coding solution for genomic data

Bioinformatics ◽

10.1093/bioinformatics/btz922 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2275-2277 ◽

Cited By ~ 1

Author(s):

Jan Voges ◽

Tom Paridaens ◽

Fabian Müntefering ◽

Liudmila S Mainzer ◽

Brian Bliss ◽

...

Keyword(s):

Genomic Data ◽

International Organization ◽

Arithmetic Coding ◽

Supplementary Information ◽

Genomic Sequencing ◽

Command Line ◽

Supplementary Data ◽

Sequencing Data ◽

Binary Arithmetic ◽

Straightforward Solution

Abstract Motivation In an effort to provide a response to the ever-expanding generation of genomic data, the International Organization for Standardization (ISO) is designing a new solution for the representation, compression and management of genomic sequencing data: the Moving Picture Experts Group (MPEG)-G standard. This paper discusses the first implementation of an MPEG-G compliant entropy codec: GABAC. GABAC combines proven coding technologies, such as context-adaptive binary arithmetic coding, binarization schemes and transformations, into a straightforward solution for the compression of sequencing data. Results We demonstrate that GABAC outperforms well-established (entropy) codecs in a significant set of cases and thus can serve as an extension for existing genomic compression solutions, such as CRAM. Availability and implementation The GABAC library is written in C++. We also provide a command line application which exercises all features provided by the library. GABAC can be downloaded from https://github.com/mitogen/gabac. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

Genetics in Medicine ◽

10.1038/s41436-020-01084-8 ◽

2021 ◽

Author(s):

Shilpa Nadimpalli Kobren ◽

◽

Dustin Baldridge ◽

Matt Velinder ◽

Joel B. Krier ◽

...

Keyword(s):

Online Survey ◽

Variant Calling ◽

Theoretical Method ◽

The United States ◽

Genomic Sequencing ◽

Biomedical Data ◽

Sequencing Data ◽

Multimodal Data ◽

Undiagnosed Diseases ◽

Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

Download Full-text

Complete Genome Sequences of Six Listeria monocytogenes Sequence Type 9 Isolates from Meat Processing Plants in Norway

Genome Announcements ◽

10.1128/genomea.00016-18 ◽

2018 ◽

Vol 6 (7) ◽

Cited By ~ 3

Author(s):

Annette Fagerlund ◽

Solveig Langsrud ◽

Birgitte Moen ◽

Even Heir ◽

Trond Møretrø

Keyword(s):

Listeria Monocytogenes ◽

Complete Genome ◽

Foodborne Pathogen ◽

Sequence Type ◽

Nanopore Sequencing ◽

Meat Processing ◽

Sequencing Data ◽

Genome Sequences ◽

Fatal Disease ◽

Processing Plants

ABSTRACT Listeria monocytogenes is a foodborne pathogen that causes the often-fatal disease listeriosis. We present here the complete genome sequences of six L. monocytogenes isolates of sequence type 9 (ST9) collected from two different meat processing facilities in Norway. The genomes were assembled using Illumina and Nanopore sequencing data.

Download Full-text

Mutations in emerging variant of concern lineages disrupt genomic sequencing of SARS-CoV-2 clinical specimens

10.1101/2021.06.01.21258181 ◽

2021 ◽

Author(s):

Kevin S Kuchinski ◽

Jason Nguyen ◽

Tracy D Lee ◽

Rebecca Hickman ◽

Agatha N Jassem ◽

...

Keyword(s):

British Columbia ◽

Laboratory Data ◽

Genomic Sequencing ◽

Genome Sequences ◽

High Quality ◽

Laboratory Methods ◽

Clinical Specimens ◽

Recent Outbreak ◽

High Quality Genome

Mutations in emerging SARS-CoV-2 lineages can interfere with the laboratory methods used to generate high-quality genome sequences for COVID-19 surveillance. Here, we identify 46 mutations in current variant of concern lineages affecting the widely used laboratory protocols for SARS-CoV-2 genomic sequencing by Freed et al. and the ARTIC network. We provide laboratory data showing how three of these mutations disrupted sequencing of P.1 lineage specimens during a recent outbreak in British Columbia, Canada, and we also demonstrate how we modified the Freed et al. protocol to restore performance.

Download Full-text

GenomeDiver: A platform for phenotype-guided medical genomic diagnosis

10.1101/2020.11.22.20236430 ◽

2020 ◽

Author(s):

Nathaniel Pearson ◽

Christian Stolte ◽

Kevin Shi ◽

Faygel Beren ◽

Noura S. Abul-Husn ◽

...

Keyword(s):

Diagnostic Testing ◽

Diagnostic Process ◽

Genomic Sequencing ◽

Sequencing Data ◽

Clinical Diagnostic Laboratory ◽

Diagnostic Laboratory ◽

Phenotypic Data ◽

Human Phenotype ◽

Phenotype Data

ABSTRACTPurposeMaking a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process.MethodsGenomeDiver is designed to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation.ResultsWe show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and an example of the performance of GenomeDiver using simulated genomic testing data.ConclusionsGenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory-clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

Download Full-text

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

10.1101/544536 ◽

2019 ◽

Cited By ~ 3

Author(s):

Kate Chkhaidze ◽

Timon Heide ◽

Benjamin Werner ◽

Marc J. Williams ◽

Weini Huang ◽

...

Keyword(s):

Next Generation Sequencing ◽

Tumour Growth ◽

Evolutionary Dynamics ◽

Clonal Selection ◽

Genomic Data ◽

Confounding Factors ◽

Data Generation ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

AbstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

Download Full-text