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October 4, 2018

Beacon: The Story so Far

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October 4, 2018

Beacon: The Story so Far

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October 4, 2018

Beacon: The Story so Far

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October 4, 2018

Beacon: The Story so Far

Press Releases
November 20, 2014

These Six Great Neuroscience Ideas Could Make the Leap from Lab to Market

Research labs across Ontario are full of ingenious – and even life-saving – inventions.

Press Releases
November 20, 2014

These Six Great Neuroscience Ideas Could Make the Leap from Lab to Market

Research labs across Ontario are full of ingenious – and even life-saving – inventions.

Research labs across Ontario are full of ingenious – and even life-saving – inventions.

Press Releases
November 20, 2014

These Six Great Neuroscience Ideas Could Make the Leap from Lab to Market

Research labs across Ontario are full of ingenious – and even life-saving – inventions.

Press Releases
November 20, 2014

These Six Great Neuroscience Ideas Could Make the Leap from Lab to Market

Research labs across Ontario are full of ingenious – and even life-saving – inventions.

Press Releases
February 18, 2015

10 Breakthrough Technologies: Internet of DNA

In February MIT Technology Review chose their 10 Breakthrough Technologies of 2015—here’s how they have advanced since.

Press Releases
February 18, 2015

10 Breakthrough Technologies: Internet of DNA

In February MIT Technology Review chose their 10 Breakthrough Technologies of 2015—here’s how they have advanced since.

In February MIT Technology Review chose their 10 Breakthrough Technologies of 2015—here’s how they have advanced since.

Press Releases
February 18, 2015

10 Breakthrough Technologies: Internet of DNA

In February MIT Technology Review chose their 10 Breakthrough Technologies of 2015—here’s how they have advanced since.

Press Releases
February 18, 2015

10 Breakthrough Technologies: Internet of DNA

In February MIT Technology Review chose their 10 Breakthrough Technologies of 2015—here’s how they have advanced since.

Press Releases
December 15, 2015

Canadian Genomic Data-Sharing Program Lands C$3.3M

The Can-Share program seeks to build policies and data tools to share data among Canadian research institutions and with international partners. 

Press Releases
December 15, 2015

Canadian Genomic Data-Sharing Program Lands C$3.3M

The Can-Share program seeks to build policies and data tools to share data among Canadian research institutions and with international partners. 

The Can-Share program seeks to build policies and data tools to share data among Canadian research institutions and with international partners. 

Press Releases
December 15, 2015

Canadian Genomic Data-Sharing Program Lands C$3.3M

The Can-Share program seeks to build policies and data tools to share data among Canadian research institutions and with international partners. 

Press Releases
December 15, 2015

Canadian Genomic Data-Sharing Program Lands C$3.3M

The Can-Share program seeks to build policies and data tools to share data among Canadian research institutions and with international partners. 

News
November 14, 2017

DNAstack Inks Partnership with Sentieon to Offer Faster, Cheaper, More Consistent Bioinformatics in the Cloud

DNAstack, a cloud genomics company, today is announcing a partnership with Sentieon, an award-winning bioinformatics software company. 

News
November 14, 2017

DNAstack Inks Partnership with Sentieon to Offer Faster, Cheaper, More Consistent Bioinformatics in the Cloud

DNAstack, a cloud genomics company, today is announcing a partnership with Sentieon, an award-winning bioinformatics software company. 

DNAstack, a cloud genomics company, today is announcing a partnership with Sentieon, an award-winning bioinformatics software company. 

Through this partnership, a suite of Sentieon’s algorithms will be made available for running through DNAstack’s Workflows app to deliver genomics data analyses pipelines in the cloud that are faster, cheaper, without downsampling, and 100% consistent while using identical mathematics as the industry standard best practice workflows.

Sentieon technologies won precision FDA’s Consistency Challenge as Top Overall Performance and Highest Reproducibility, and Truth Challenge for highest SNP Recall and INDEL Precision. In the most recent precisionFDA Hidden Treasures-Warm Up challenge, along with 36 other submissions, all Sentieons’ submissions caught all injected variants while using default parameters without any special filtering. Last year, Sentieon’s TNscope toolset also ranked #1 in the ICGC-TCGA DREAM Challenge for Somatic Mutation Calling for all 3 categories SNV, Indel, SV. “At DNAstack, our mission is to democratize access to genomics data and best-in-class technologies to analyze it at scale,” said Dr. Marc Fiume, CEO of DNAstack. “The addition of Sentieon’s algorithms to our Workflows marketplace lets anyone with an internet connection run their award-winning software simply and at any scale. From there, they can interpret results privately in the context of the large and growing global network on our platform.” Fiume also co-leads the Discovery Workstream of the Global Alliance for Genomics & Health, which develops industry standards for sharing of genomics data, tools, and services.

Sentieon’s DNAseq pipeline for germline FASTQ-to-VCF running on DNAstack takes around 5 hours and costs less than $15 for a 30X coverage Whole Genome Sequence. “Through the integration with DNAstack, Sentieon technologies will be made more accessible to a global community of scientists to help accelerate breakthrough discoveries and the implementation of precision medicine,” said Dr. Jun Ye, Sentieon’s CEO. “We look forward to leveraging the power and efficiencies of DNAstack’s platform to deliver Sentieon’s accurate and fast tools for read alignment and variant calling to DNAstack’s customers”

“This can serve a very large user base that needs a simple and cost-effective ‘sequencer-to-scientist’ solution,” said Fiume. “Especially as genomics becomes increasingly integrated with clinical care, we see tremendous long term value in having high-speed, low-cost, no downsampling, and 100% reproducible solutions for data analysis.”

About DNAstack

DNAstack develops a cloud-based platform for genomics data analysis and sharing. Through collaborations with Google, Broad Institute, and the Global Alliance for Genomics & Health, DNAstack provides push-button access to state-of-the-art technologies to help researchers, clinical laboratories, and pharmaceutical companies more quickly and cost-effectively make sense of the world’s exponentially accumulating genomics data and break down barriers to data sharing.

Direct any questions to info@dnastack.com.

About Sentieon

Sentieon develops highly optimized and accurate algorithms for bioinformatics applications, using the team’s expertise in algorithm, software, and system optimization. Sentieon is a team of professionals using accumulated expertise in modeling, optimization, machine learning, and high-performance computing, to enable precision data for precision medicine.

News
November 14, 2017

DNAstack Inks Partnership with Sentieon to Offer Faster, Cheaper, More Consistent Bioinformatics in the Cloud

DNAstack, a cloud genomics company, today is announcing a partnership with Sentieon, an award-winning bioinformatics software company. 

News
November 14, 2017

DNAstack Inks Partnership with Sentieon to Offer Faster, Cheaper, More Consistent Bioinformatics in the Cloud

DNAstack, a cloud genomics company, today is announcing a partnership with Sentieon, an award-winning bioinformatics software company. 

DNAstack today announced its participation in a new project to accelerate the development of a national software platform for precision health in Canada.

The project — in which Deloitte, Genome BC, LifeLabs, Microsoft, Molecular You, Provincial Health Services Authority, and the University of British Columbia will also participate — is among the first to be selected and launched as part of Canada’s Digital Technology Supercluster, a federally funded program that recently received over $150M to stimulate the creation of competitive and innovative digital technology solutions for top industries.

With support from the Canadian government, the team is building a powerful new software platform that will make it easier for healthcare organizations, academic researchers, clinical laboratories, pharmaceutical companies, and other innovators to harness exponentially growing volumes of genomic and biomedical data. The platform will help drive new scientific discoveries and inform medical decisions, translating into more personalized and cost-effective healthcare for millions of Canadians.

The DNAstack team
The DNAstack team 

The platform has been designed from the ground up around modern principles of data security, sharing, and analysis, and serves as an alternative path for organizations looking to avoid enormous, ongoing cost burdens associated with purchasing and maintaining local computational infrastructure. The platform is already being piloted with early adopters across the country, where it has proven to be dramatically more powerful, secure, cost-efficient, and accessible compared to other existing solutions. The project team aims to deliver the most advanced platform for precision health in the country, positioning healthcare organizations to roll out new programs that reap significant health and economic benefits for years to come.

“We’re laying the foundation for the future of genomic and biomedical science, where the combination of networked data and powerful technology is used to generate life-saving insights faster than ever before,” said Dr. Marc Fiume, CEO and Co-Founder at DNAstack. “With this platform, we’re empowering scientists to take big data, cloud computing, and machine learning to the fight against the biggest challenges in health.”

 

"We’re empowering scientists to take big data, cloud computing, and machine learning to the fight against the biggest challenges in health." — Marc Fiume, CEO at DNAstack

Dr. Marc Fiume, CEO and Co-Founder of DNAstack
[caption id="" align="aligncenter" width="1400"] Dr. Marc Fiume, CEO and Co-Founder of DNAstack[/caption]

The platform will provide easy to use tools for data producers (e.g. principal investigators, diagnostics laboratories, hospital systems, patient advocacy groups, individuals) to connect and administer the secure sharing of their datasets, and for data consumers (e.g. academic, clinical, pharmaceutical, and industry researchers) to discover and analyze that data using both gold standard and custom applications. Individual users of the platform will be able to perform intense statistical and machine learning analyses with on-demand access to hundreds of thousands of compute cores, more than 10 times the computing power of some of the most equipped research institutions in Canada.

For DNAstack, the project is a continuation of years of global leadership and product innovation in the space. Since 2014, DNAstack has been an active member of the Global Alliance for Genomics & Health (GA4GH), where it contributes to the development of open standards for interoperable data sharing and analysis. This project is integrating key GA4GH protocols for identity, access, discovery, and analysis. In 2018, DNAstack co-founded the Canadian Genomics Cloud, the most computationally powerful public cloud platform for genomics and precision medicine in Canada, which is actively being used by leading scientists across the country to study the genetic causes of autism, adult cancer, pediatric cancer, heart disease, mental health, cystic fibrosis, and other rare diseases. DNAstack is now working in close collaboration with partners of the Digital Technology Supercluster, having diverse and complementary expertise, to introduce entirely new features to the market.

 

Bill Tam, Vice President of Business Development and Partner Relations, Canada’s Digital Technology Supercluster
[caption id="" align="aligncenter" width="300"] Bill Tam, VP of Business Development and Partner Relations, Canada’s Digital Technology Supercluste[/caption]

“We are supporting ambitious opportunities that can’t be tackled by one company alone. Through a collective effort, this project aims to make a global impact and position Canada as a world leader in health,” said Bill Tam, Vice President of Business Development and Partner Relations for Canada’s Digital Technology Supercluster. “We are proud that the Supercluster has created an elevated platform for leading Canadian SMEs like DNAstack to continue to innovate and grow.” — Bill Tam, Vice Presdient of Business Development and Partner Releations, Canada's Digital Technology Supercluster

 

News
August 1, 2018

DNAstack to Co-Develop a National Platform for Precision Health Through Canada’s Digital Technology Supercluster

The  Beacon Network , where new Clinical Evidence Beacons can be searched for crowdsourcing classification of genomic variants.
DNAstack today announced the launch of Clinical Evidence Beacons on the Beacon Network, a real-time search engine for finding genetic mutations across a global network of genomic datasets.These additions will enable medical laboratories to crowdsource the interpretation of variants through a secure social network.Accurately interpreting DNA variants identified through genetic testing is essential for patients and clinicians to make informed medical decisions, for a growing number of medical use cases. While some of those variants can be confidently predicted to be pathogenic or benign based on previous studies and data accessible through variant interpretation resources, in many cases evidence is missing or inconsistent, resulting in conflicting evaluations or reporting as “variants of unknown significance” (VUS). Clinical Evidence Beacons facilitate faster and more consistent variant classifications by securely sharing variant interpretation evidence between collaborating organizations, accelerating the exchange of critical knowledge and improving support for patients affected by genetic diseases and carriers of variants that have an impact on medical decision making. The Beacon Network, where new Clinical Evidence Beacons can be searched for crowdsourcing classification of genomic variants.Building upon the Global Alliance for Genomic and Health (GA4GH) Beacon API, an open standard that allows researchers to determine whether a given variant exists within a genomic dataset, Clinical Evidence Beacons are an extension of the protocol being piloted by DNAstack, allowing uncurated knowledge about a variant to be shared and discovered in real time.“The Beacon API 1.0, which was approved as a GA4GH standard last year, validates the international community’s willingness to work together to define standards and engage in data sharing in a meaningful way,” said Miro Cupak, VP Engineering at DNAstack. “The original protocol was intentionally simple. We’ve since been exploring more powerful derivatives, and learned that by integrating clinical data in the payload of the Beacon that we can help solve outstanding issues faced by the clinical genomics community. Clinical Evidence Beacons, while not formally approved as a GA4GH standard, demonstrate one potential application being designed for future versions of the protocol.”
Miro Cupak, VP Engineering at DNAstackThe first Clinical Evidence Beacons to join the Beacon Network come from the Canadian Open Genetics Repository (COGR), a network of over 20 laboratories that have come together to share information about variants and clinical cases. Members of the COGR are now able to search controlled access Clinical Evidence Beacons for variants of interest on the Beacon Network. A national effort to improve the quality of variant classification has been led by the COGR, who previously published that the percentage of variants with discordant classifications dropped from 26.7% to 14.2% as a result of crowdsourcing, demonstrating the power of collaboration between clinical genomics labs.“Our understanding of genetic data continues to evolve and there is often not a one-to-one correlation between genetic variation and disease, so international or global data sharing efforts are vital to moving the field forward,” said Dr. Jordan Lerner-Ellis, principle investigator of the COGR and Head of Advanced Molecular Diagnostics at Toronto’s Mount Sinai Hospital, Sinai Health System and Associate Professor at the University of Toronto. “Systems that allow for easily accessible real-time data sharing will be increasingly important to be able to provide the most up-to-date information and to translate it into patient care.”While the cost of genome sequencing has decreased significantly, it is still costly. Software that enables valuable biomedical data to be shared will enable future healthcare systems to draw on distributed collections of data in real time, unconstrained by traditional institutional silos and long publication cycles. As we move toward personalized healthcare, there is a need for such systems to integrate genomic information, clinical data, and real-world evidence to better inform treatment decisions.“There is an enormous need to share genomic information and we have seen worldwide interest in the application of Beacons in healthcare environments,” said Jordi Rambla, European Genome-phenome Archive (EGA) Team Lead at the Centre for Genomic Regulation (CRG). “Working with the clinical community, we are pioneering ideas to improve upon Beacon 1.0, and using this knowledge and experience to inform the next version of this standard. Ultimately, Clinical Evidence Beacons could make sharing genomic information, as well as phenotypic data, easier, faster, and more securely than is possible today, accelerating knowledge exchange, diagnoses, and improvements to patient care.” 
Miro Cupak, VP Engineering at DNAstack
Jordi Rambla, European Genome-phenome Archive Team Lead at the Centre for Genomic Regulation
Jordi Rambla, European Genome-phenome Archive Team Lead at the Centre for Genomic RegulationWhile DNAstack’s support for clinical use cases has been developed as extensions to the current Beacon version, an international effort coordinated by the GA4GH Discovery Work Stream and lead by the ELIXIR Beacon Project currently prepares a major upgrade of the GA4GH Beacon protocol. Since the roadmap of the next version includes changes supporting a variety of stakeholder defined biomedical use cases, incorporation of the upcoming Beacon protocol into software supported by DNAstack will accelerate future applications for genomic variant research and discovery.About DNAstackDNAstack’s mission is to improve the lives of millions of people affected by genetic disease by breaking down barriers to data sharing and discovery. DNAstack develops standards and technologies for scientists to more efficiently find, access, and analyze the world’s exponentially growing volumes of genomic and biomedical data.Photo CreditsCape Canaveral Air Force Station, United States. Photo Credit: SpaceX
News
October 14, 2019

DNAstack Launches Clinical Evidence Beacons to Drive Crowdsourcing for Genetic Disease Discovery

Press Releases
February 18, 2018

Canadian Genomics Cloud to Develop GA4GH Compliant Precision Medicine Platform

DNAstack, Canada’s Genomics Enterprise, Google, the Centre of Genomics and Policy, and more announce the launch of the Canadian Genomics Cloud (CGC): a national cloud-based infrastructure for genomics initiatives to share data across Canada.

Press Releases
February 18, 2018

Canadian Genomics Cloud to Develop GA4GH Compliant Precision Medicine Platform

DNAstack, Canada’s Genomics Enterprise, Google, the Centre of Genomics and Policy, and more announce the launch of the Canadian Genomics Cloud (CGC): a national cloud-based infrastructure for genomics initiatives to share data across Canada.

DNAstack, Canada’s Genomics Enterprise, Google, the Centre of Genomics and Policy, and more announce the launch of the Canadian Genomics Cloud (CGC): a national cloud-based infrastructure for genomics initiatives to share data across Canada.

Press Releases
February 18, 2018

Canadian Genomics Cloud to Develop GA4GH Compliant Precision Medicine Platform

DNAstack, Canada’s Genomics Enterprise, Google, the Centre of Genomics and Policy, and more announce the launch of the Canadian Genomics Cloud (CGC): a national cloud-based infrastructure for genomics initiatives to share data across Canada.

Press Releases
February 18, 2018

Canadian Genomics Cloud to Develop GA4GH Compliant Precision Medicine Platform

DNAstack, Canada’s Genomics Enterprise, Google, the Centre of Genomics and Policy, and more announce the launch of the Canadian Genomics Cloud (CGC): a national cloud-based infrastructure for genomics initiatives to share data across Canada.

News
September 25, 2017

Crowd-Sourcing Variant Interpretations to Improve Patient Outcomes

We fundamentally believe that democratization of genomics information through sharing will massively accelerate discoveries that will lead to better treatments and outcomes for patients affected by genetic diseases. 

We can avoid long diagnostic odyssies by connecting patients’ data into networks that leverage — in realtime — large and exponentially growing volumes of information. This is the inspiration behind our work on the Beacon Project for the Global Alliance for Genomics & Health, where we are defining industry standards for sharing genomic variants.

News
September 25, 2017

Crowd-Sourcing Variant Interpretations to Improve Patient Outcomes

We fundamentally believe that democratization of genomics information through sharing will massively accelerate discoveries that will lead to better treatments and outcomes for patients affected by genetic diseases. 

We can avoid long diagnostic odyssies by connecting patients’ data into networks that leverage — in realtime — large and exponentially growing volumes of information. This is the inspiration behind our work on the Beacon Project for the Global Alliance for Genomics & Health, where we are defining industry standards for sharing genomic variants.

We fundamentally believe that democratization of genomics information through sharing will massively accelerate discoveries that will lead to better treatments and outcomes for patients affected by genetic diseases. 

We can avoid long diagnostic odyssies by connecting patients’ data into networks that leverage — in realtime — large and exponentially growing volumes of information. This is the inspiration behind our work on the Beacon Project for the Global Alliance for Genomics & Health, where we are defining industry standards for sharing genomic variants.

Beyond sharing variants themselves, though, it is important to share their classifications — i.e. whether a variant causes disease — because while there are standards and guidelines for interpretation of genetic variants, it is common for different laboratories to classify them differently. One laboratory may consider a variant to be pathogenic while another may consider it benign, leading to discrepent diagnoses and treatment options between patients studied at different clinics.

The purpose of the Canadian Open Genetics Repository, lead by Dr. Jordan Lerner-Ellis, Director of the Advanced Molecular Diagnostics Laboratory at Mount Sinai Hospital, and by Dr. Matthew Lebo, Associate Laboratory Director for Laboratory for Molecular Medicine at Partners Healthcare, was to develop a national program for Canadian genetic diagnostic laboratories to crowd-source variant interpretations and resolve discordant classifications for variants.

The study included over 20 laboratories who classified over 5,000 variant observations in these genes. Using a five-tier classification model, 38.9% of variants were discordant between laboratories; with a three-tier model, 26.7% were discordant. After crowd-sourcing variant classifications — and supporting evidence — discordance decreased to 30.7% under the five-tier model and 14.2% under the three-tier model. This study demonstrates the power of a crowd-sourcing platform to increase the level of consensus in variant classifications.

Learn More

The DNAstack platform is being used to share variants and their classifications as a public resource on opengenetics.ca and as a Beacon on the Beacon Network. The work was recently published in the pages of Genetics in Medicine and the full text is available for download here.

News
September 25, 2017

Crowd-Sourcing Variant Interpretations to Improve Patient Outcomes

We fundamentally believe that democratization of genomics information through sharing will massively accelerate discoveries that will lead to better treatments and outcomes for patients affected by genetic diseases. 

We can avoid long diagnostic odyssies by connecting patients’ data into networks that leverage — in realtime — large and exponentially growing volumes of information. This is the inspiration behind our work on the Beacon Project for the Global Alliance for Genomics & Health, where we are defining industry standards for sharing genomic variants.

News
September 25, 2017

Crowd-Sourcing Variant Interpretations to Improve Patient Outcomes

We fundamentally believe that democratization of genomics information through sharing will massively accelerate discoveries that will lead to better treatments and outcomes for patients affected by genetic diseases. 

We can avoid long diagnostic odyssies by connecting patients’ data into networks that leverage — in realtime — large and exponentially growing volumes of information. This is the inspiration behind our work on the Beacon Project for the Global Alliance for Genomics & Health, where we are defining industry standards for sharing genomic variants.

Press Releases
December 15, 2015

New Pan-Canadian Program to Accelerate Data Sharing in Biomedical Research and Patient Care

Genome Canada and the Canadian Institutes of Health Research (CIHR) today announced a $3.3 million investment in Can-SHARE – a pan-Canadian program that will enable innovation in the use of genomic data for health care for patients in Canada and worldwide. 

Press Releases
December 15, 2015

New Pan-Canadian Program to Accelerate Data Sharing in Biomedical Research and Patient Care

Genome Canada and the Canadian Institutes of Health Research (CIHR) today announced a $3.3 million investment in Can-SHARE – a pan-Canadian program that will enable innovation in the use of genomic data for health care for patients in Canada and worldwide. 

Genome Canada and the Canadian Institutes of Health Research (CIHR) today announced a $3.3 million investment in Can-SHARE – a pan-Canadian program that will enable innovation in the use of genomic data for health care for patients in Canada and worldwide. 

Press Releases
December 15, 2015

New Pan-Canadian Program to Accelerate Data Sharing in Biomedical Research and Patient Care

Genome Canada and the Canadian Institutes of Health Research (CIHR) today announced a $3.3 million investment in Can-SHARE – a pan-Canadian program that will enable innovation in the use of genomic data for health care for patients in Canada and worldwide. 

Press Releases
December 15, 2015

New Pan-Canadian Program to Accelerate Data Sharing in Biomedical Research and Patient Care

Genome Canada and the Canadian Institutes of Health Research (CIHR) today announced a $3.3 million investment in Can-SHARE – a pan-Canadian program that will enable innovation in the use of genomic data for health care for patients in Canada and worldwide. 

Despite advances in sequencing and analysis tools, calling variants in whole-genome sequencing (WGS) data is not trivial, even when dealing with only a few dozen samples.

When the number of samples reaches into the thousands, the time, computational resources, and file storage required for analysis can quickly become overwhelming. This was the challenge faced by the MSSNG team when they sought to joint-call the largest autism cohort yet sequenced — how could they process nearly 10,000 samples in a way that would be quick, reproducible, and allow for future expansion, all without breaking the bank?

The pipeline

One of the key directives of the initiative was to allow for painless future expansion of the dataset — namely, adding new samples without full reprocessing of the entire cohort. In addition, these outputs should be reproducible and consistent across sequencing technologies and analysis tools, so data from multiple experiments across time, labs, and experimental conditions could be combined and jointly analyzed. To that end, MSSNG researchers chose to analyze their WGS data using standards defined by the Centers for Common Disease Genomics (CCDG). The CCDG provides a set of standardized data processing steps for WGS data with a focus on producing functionally equivalent results (Regier et al., 2018). These steps cover the alignment, duplicate marking, and base quality score recalibration (BQSR) tasks that convert the raw FASTQ data to CRAM-format alignment files that may be used for long-term storage and future reanalysis (Figure 1).

 

Figure 1 : Pipeline outline. Paired FASTQ files from each sample are aligned to the reference genome to produce CRAM files. Variants are called for each CRAM to produce gVCFs for each sample, which are then combined and joint-genotyped to produce a VCF file. VQSR is performed to produce a final recalibrated VCF file.
Figure 1: Pipeline outline. Paired FASTQ files from each sample are aligned to the reference genome to produce CRAM files. Variants are called for each CRAM to produce gVCFs for each sample, which are then combined and joint-genotyped to produce a VCF file. VQSR is performed to produce a final recalibrated VCF file. 

Though not part of the CCDG pipeline itself, the CRAM output from this upstream pipeline is used to call variants (SNPs and small indels) on a per-sample basis, outputting genomic VCF (gVCF) files. Finally, the gVCF files for all samples are combined and joint-called to produce a single VCF file. Optionally, variant quality scores are then recalibrated (variant quality score recalibration, VQSR). See Figure 1 for an overview of the pipeline steps.

The tools

After extensive testing of concordance, cost, and speed, MSSNG chose to use Sentieon to process their WGS samples. Sentieon provides a licensed toolset that implements computationally-optimized versions of common variant-calling tools, providing results up to 10x faster than GATK’s best-practices pipeline while maintaining high concordance with GATK’s results (Freed et al., 2017). Sentieon publishes comprehensive documentation outlining how to run a CCDG-compliant upstream pipeline, as well as information on common downstream analysis steps such as per-sample SNP and indel calling using HaplotypeCaller, and joint genotyping and VQSR using their GVCFtyper and VarCal algorithms.

Challenges and optimizations

Upstream Pipeline: FASTQ -> CRAM, GVCF

In the upstream part of the pipeline, raw FASTQ files are processed to per-sample CRAMs and gVCFs. This segment ran smoothly using Sentieon, taking an average of 4 hours per sample (64 core virtual machine (VM), 55 GB of RAM). In rare cases (~30/9,625 total samples) the alignment step ran out of memory and RAM was increased for these samples. Since many of the Sentieon algorithms are I/O-bound (that is, they are bottlenecked by the speed of reading and writing to the disk, rather than by CPU or memory usage), we also chose to use local SSDs for storage, which provide very fast I/O speeds.

We were able to run the upstream pipeline using preemptible VMs, a machine type that is provided at a much lower cost by Google but which may be shut down at any time if the resources are needed elsewhere. If a VM is shut down in this way, all progress on a task is lost and the task will be automatically restarted on another VM. If a VM is preempted frequently enough, the cost and time lost from running and rerunning the task can outweigh the savings of using a preemptible VM. Out of 8,377 successful runs that we inspected, we found that 7,163 runs were not preempted in any step. The average raw compute cost for these runs was $2.43 USD including storage, CPU, and RAM costs (not including the price of the Sentieon licence itself). We also observed that larger VMs (such as the 64 CPU/55GB RAM VMs used for the Sentieon steps) showed far less preemption events than smaller ones. The upstream pipeline was run in parallel, with ~500 samples run concurrently.

Downstream Pipeline: Joint Genotyping

The majority of complications occurred during the joint genotyping step, which requires merging and joint genotyping all gVCF files generated using the upstream pipeline (1 per sample). Whereas the upstream pipeline can be run massively in parallel with each sample in a separate VM, joint genotyping requires the presence of all of the data in a single VM. This raises two issues: 1) disk size required, and 2) the runtime of the pipeline.

Disk Size Requirements

With each gzipped input gVCF file taking 15–25GB of space, the disk space required for analysis runs into the tens of terabytes for input files alone. The size of the merged output file, which is around the same as the sum of all the input files, must also be considered. While the Google disk size limit of 64 TB per VM should be enough to accommodate this, the size of the output file would make it unwieldy.

Pipeline Runtime

Despite the optimizations implemented by Sentieon, the speed of many processes is limited by the speed of the zip/unzip process (which by default runs on a single core) as both input and output files are gzipped to save space. This reality dramatically slows down the analysis, especially given the size of the files involved.

Solutions

Splitting Up Joint-Genotyping By Region 

Sentieon provides a number of built-in solutions that help manage both the size of the final VCF file, as well as the speed of the analysis. First, joint genotyping may be split up to operate independently on different regions of the genome (much like many of GATK’s tools, which allow the analysis to be split up over intervals). This means that 1) the joint genotyping analysis may be run in parallel across intervals, and 2) we do not need to localize the full gVCF file for every sample in every shard — only the region corresponding to the interval we are joint calling in that shard (Figure 2a).

Figure 2 : Solutions for joint genotyping large cohorts using Sentieon. Compare these steps to the progression from gVCFs -> Recalibrated VCF in Figure 1. a) Parallelization of joint-calling. gVCFs are broken up by region and joint genotyping is run in parallel on small regions to produce a series of partial VCFs. Partial VCFs covering a chromosome are then merged to produce a ‘main’ and a ‘samples’ file for each chromosome. b) Structure of the ‘main’ and ‘samples’ files produced by merging joint-genotyped partial VCFs. The ‘main’ file is small, containing only columns 1–9 of a normal VCF file. The ‘samples’ file contains all sample columns (rows 10-the end of the file) — 9621 total columns in our case. c) VQSR and extraction of the final VCF files. The ‘main’ files from each chromosome are merged and VQSR is performed. The recalibrated VCF is split by chromosome to generate ‘recalibrated main’ files, which are combined with the ‘samples’ files for each chromosome to produce a single full recalibrated VCF file for each chromosome.
Figure 2: Solutions for joint genotyping large cohorts using Sentieon. Compare these steps to the progression from gVCFs -> Recalibrated VCF in Figure 1. a) Parallelization of joint-calling. gVCFs are broken up by region and joint genotyping is run in parallel on small regions to produce a series of partial VCFs. Partial VCFs covering a chromosome are then merged to produce a ‘main’ and a ‘samples’ file for each chromosome. b) Structure of the ‘main’ and ‘samples’ files produced by merging joint-genotyped partial VCFs. The ‘main’ file is small, containing only columns 1–9 of a normal VCF file. The ‘samples’ file contains all sample columns (rows 10-the end of the file) — 9621 total columns in our case. c) VQSR and extraction of the final VCF files. The ‘main’ files from each chromosome are merged and VQSR is performed. The recalibrated VCF is split by chromosome to generate ‘recalibrated main’ files, which are combined with the ‘samples’ files for each chromosome to produce a single full recalibrated VCF file for each chromosome. 

In order to read in only the required regions of the gVCFs without localizing the full files, we took advantage of a feature of htslib which allows bcftools to read directly from Google Cloud Storage locations. bcftools accesses Google credentials using the environment variable GCS_OAUTH_TOKEN, which can be defined as follows (assuming the user has authenticated with Google Cloud):

export GCS_OAUTH_TOKEN=$(gcloud auth application-default print-access-token)

To localize only the desired region of each gVCF file, the following command is used for each sample’s gVCF URL:

bcftools view -R ${region}.bed -Oz -o ${sample}_${region}.g.vcf.gz ${gvcf_url}

Each region.bed should specify a different region of the genome, e.g. chr1:1–50000000. The result of running this command for each gVCF URL is a smaller gVCF file that only includes calls for the region specified in the region.bed file. All of these partial gVCF files are then joint-genotyped together to output a partial VCF that has calls only for the specified region (Figure 2a). Since joint-genotyping is in this way split up into many smaller jobs that can be run in parallel for each region, the process is made considerably faster.

Merging Joint-Genotyped Files by Chromosome

Once the partial VCFs for each region are produced, they must be merged together to form a final, complete VCF file that includes all regions. After some trial and error it was decided that rather than merging all regions of the genome together to form one large final VCF file, genomic regions would be merged on a per-chromosome basis in order to output 26 final VCF files (22 autosomes, chrX, chrY, chrM, and contig regions) (Figure 2a). Although the VCFs for each chromosome are still quite large, they are individually much more manageable than a single VCF containing all regions.

Since both the partial VCFs produced by joint-calling and the merged output file are gzipped, the process of merging these partial VCFs into a single file takes several days to a week to process even a single chromosome — once again, the speed of analysis is bound by the speed of the gzipping process. Had the merge step been run for the full dataset to produce a single output file, we predicted that this step would have taken upwards of a month to complete. By merging only the partial VCFs that made up each chromosome, we were able to run the process in parallel, meaning that the merge step took a little over a week to complete.

Sentieon's " Split_By_Sample" Option For Large VCF'S 

It should be noted that Sentieon provides a different method of reducing the size of the final VCF file — they allow the output VCF to be split by sample, rather than by chromosome. This would result in a single ‘main’ file that contains only the first nine columns of the VCF (CHROM, START, STOP, etc.), and then a number of ‘samples’ files that each contain the calls for n samples (n could be 100, 500, 1000, etc. — the smaller the number of samples per file, the smaller the size of each file). Both the ‘main’ file and each of the ‘samples’ files have the data for all chromosomes present, but since each only contains a subset of the samples, each file is quite a bit smaller (Figure 2b). Since none of these files are valid VCF files, an ‘extraction’ step must be performed in order to produce a valid VCF file by combining the first 9 columns from the ‘main’ file along with the desired sample columns from the various ‘samples’ files. Although here we chose to split the final VCF by chromosome rather than by sample to reduce final file size (since we required final VCFs that included every sample), we still took advantage of Sentieon’s ‘split_by_sample’ option because of the implications for VQSR runtime.

Running VQSR On A Large DataSet 

It has been mentioned that one of our biggest problems in dealing with a cohort of this size is the bottleneck introduced by the speed of unzipping/zipping large input and output files. We were able to improve the speed of our analysis by processing smaller regions of the genome in parallel rather than trying to read the entire genome sequentially. Coming up to the VQSR step however, we realized that in order to perform this step, which recalibrates variant quality scores across the entire dataset, information from the entire genome must be read in — that is, a single VCF including all chromosomes must be used as input, and a single VCF with all chromosomes would be output. Not only would we lose the benefit of having our VCF files split by chromosome and therefore more manageable in size since we would need the full VCF, this step would again take potentially weeks to read and write these massive gzipped VCF files.

This is where Sentieon’s ‘split_by_sample’ option came in handy. Although we wanted all samples together in the final VCF and so should not have needed this option, by using it to output all sample information in a single ‘samples’ file (containing only the sample IDs and call information, that is, all columns 10-onwards in the VCF), we were also able to produce a ‘main’ file for each chromosome. The ‘samples’ file for each chromosome is quite large since it contains all calls for all samples, however the ‘main’ file is at most a few hundred MBs and contains only columns 1–9 (Figure 2b). This file is all that is needed to perform VQSR, and since it is so much smaller, a process that may have taken weeks to complete could be performed in less than a day.

The ‘main’ file for all chromosomes was combined into a single VCF-like file that contained columns 1–9 for the entire genome; VQSR was performed on this file; finally, the full-genome ‘recalibrated main’ VCF file was split once more by chromosome, to output one ‘recalibrated main’ file per chromosome. These ‘recalibrated main’ files were combined with their respective ‘samples’ files for each chromosome using Sentieon’s extraction script in order to produce a single recalibrated VCF for each chromosome, each of which includes all samples (Figure 2c). Although this extraction process was still bound by the speed of the gzip/gunzip process, it was again able to be performed in parallel across chromosomes to reduce the total runtime needed.

 

Joint-genotyping the MSSNG cohort was an intensive effort that involved a collaboration between DNAstack, Sentieon, and MSSNG researchers. Sentieon’s CCDG-compliant algorithms allowed for quick, reproducible results that will support straightforward expansion in the future. The speed of the gzip process, as well as the size of output files, required creative solutions to common problems in order to improve speed and workflow costs; we look forward to further improvements in optimizing these processes for large cohorts to help accelerate research.

References

  • Freed, D., Aldana, R., Weber, J.A. and Edwards, J.S. 2017. The Sentieon Genomics Tools — A fast and accurate solution to variant calling from next-generation sequence data. bioRxiv. doi: https://doi.org/10.1101/115717
  • Regier, A.A., Farjoun, Y., Larson, D.E., Krasheninina, O., Kang, H.M., Howrigan, D.P., Chen, B-J., Kher, M., Banks, E., Ames, D.C., English, A.C., Li, H., Xing, J., Zhang, Y., Matise, T., Abecasis, G.R., Salerno, W., Zody, M.C., Neale, B.M. & Hall, I.M. 2018. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nature Communications. 9: 4038. doi: https://doi.org/10.1038/s41467-018-06159-4
  • htslib: https://github.com/samtools/htslib
  • bcftools: https://github.com/samtools/bcftools

Photo Credits

Microscopic image of crystallized DNA from autism genes. Photo Credit: Bianca Guimarae

News
October 17, 2020

Joint Genotyping 10K Whole Genome Sequences Using Sentieon on Google: Strategies for Analyzing Large Sample Sets

News
August 11, 2020

COVID-19 Researchers Get a Boost From AI-Powered Genomics Cloud

DNAstack is helping scientists around the globe better understand COVID-19, so they can develop treatments and vaccines.

News
August 11, 2020

COVID-19 Researchers Get a Boost From AI-Powered Genomics Cloud

DNAstack is helping scientists around the globe better understand COVID-19, so they can develop treatments and vaccines.

DNAstack is helping scientists around the globe better understand COVID-19, so they can develop treatments and vaccines.

News
August 11, 2020

COVID-19 Researchers Get a Boost From AI-Powered Genomics Cloud

DNAstack is helping scientists around the globe better understand COVID-19, so they can develop treatments and vaccines.

News
August 11, 2020

COVID-19 Researchers Get a Boost From AI-Powered Genomics Cloud

DNAstack is helping scientists around the globe better understand COVID-19, so they can develop treatments and vaccines.

Press Releases
August 15, 2015

Beacon Project Cracks the Door for Genomic Data Sharing

Through its Data Working Group, GA4GH wants to replace many existing standards, conventions, and file formats with new ones that will scale to searching through genomes at the level of whole populations ― and, crucially, make it easier for separate organizations to share data.

Press Releases
August 15, 2015

Beacon Project Cracks the Door for Genomic Data Sharing

Through its Data Working Group, GA4GH wants to replace many existing standards, conventions, and file formats with new ones that will scale to searching through genomes at the level of whole populations ― and, crucially, make it easier for separate organizations to share data.

Through its Data Working Group, GA4GH wants to replace many existing standards, conventions, and file formats with new ones that will scale to searching through genomes at the level of whole populations ― and, crucially, make it easier for separate organizations to share data.

Press Releases
August 15, 2015

Beacon Project Cracks the Door for Genomic Data Sharing

Through its Data Working Group, GA4GH wants to replace many existing standards, conventions, and file formats with new ones that will scale to searching through genomes at the level of whole populations ― and, crucially, make it easier for separate organizations to share data.

Press Releases
August 15, 2015

Beacon Project Cracks the Door for Genomic Data Sharing

Through its Data Working Group, GA4GH wants to replace many existing standards, conventions, and file formats with new ones that will scale to searching through genomes at the level of whole populations ― and, crucially, make it easier for separate organizations to share data.

News
July 13, 2020

SARS-CoV-2: Biology Origins, and How Open Science is Accelerating the Search for Therapeutic Answers

DNAstack's bioinformatician Heather Ward breaks down the biology of the novel coronavirus responsible for the COVID-19 outbreak.

News
July 13, 2020

SARS-CoV-2: Biology Origins, and How Open Science is Accelerating the Search for Therapeutic Answers

DNAstack's bioinformatician Heather Ward breaks down the biology of the novel coronavirus responsible for the COVID-19 outbreak.

DNAstack's bioinformatician Heather Ward breaks down the biology of the novel coronavirus responsible for the COVID-19 outbreak.

Introduction

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the novel coronavirus responsible for the COVID-19 outbreak that first emerged in early December 2019 in Wuhan, China. As of March 20, 2020 SARS-CoV-2 has resulted in nearly 250,000 cases worldwide, claiming the lives of over 10,000 people.

Here, I’ll briefly break down the potential origins and viral life cycle of SARS-CoV-2, how it differs from the virus responsible for the 2002 outbreak, and how genomics and open science can be used to explore and develop therapeutics that will help mitigate this global threat.

SARS-CoV-2 and related coronaviruses

SARS-CoV-2 is a coronavirus, members of a class of positive-sense single-stranded RNA (ssRNA) viruses so named due to their resemblance to solar coronas. Other ssRNA viruses cause diseases which range in severity, including HIV, West Nile, and the common cold.

There are several coronaviruses known to infect humans, with the most well-known being SARS-CoV (responsible for the 2002 outbreak) and MERS-CoV (Middle Eastern Respiratory Syndrome Coronavirus). Both of these coronaviruses, as well as the current SARS-CoV-2, are believed to have originated in bats, which act as a natural reservoir for a number of coronaviruses. The virus is postulated to pass to humans via an intermediary host (civet cats in the case of SARS-CoV, and dromedary camels for MERS-CoV). Several potential hosts have been suggested as the intermediary for the current SARS-CoV-2, including snakes and pangolins.

It’s important to note that the majority of these bat-endemic coronaviruses are not able to infect humans, and mutation is required for a coronavirus to be able to transition to a new host organism. To obtain insight into which parts of the genome require mutation to allow a virus the ability to target a new host first requires an understanding of the basics of the coronavirus viral life cycle.

[caption id="attachment_3926" align="aligncenter" width="701"]

Figure 1: SARS-CoV-2 virion. [/caption]

The SARS-CoV-2 viral life cycle

The major steps of the viral life cycle of SARS-CoV-2 as well as other coronaviruses include:

  1. Binding of the virus to a receptor on a target host cell
  2. Membrane fusion between the viral envelope and the host cell, which releases the viral genome into the host cell
  3. Replication of the viral genome
  4. Transcription and translation of viral structural proteins
  5. Assembly and export of mature virions

Mature virions (packaged viral particles including the viral genome and structural proteins, see the SARS-CoV-2 virion pictured in figure 1) released from an infected host cell may infect other cells and continue the infection cycle.

If virions are unable to bind to host cell receptors or if membrane fusion does not occur, infection will not take place. These key steps are both mediated by a particular viral protein — the spike protein.

The Spike protein

The spike protein is a homotrimeric (made up of three identical peptides) transmembrane protein found studded around the exterior of the mature virion. Each monomer (one of the three identical peptides) is comprised of two subunits: the S1 subunit, which is responsible for recognizing and binding to a host cell receptor, and the S2 subunit, which facilitates membrane fusion and release of the viral genome into the host cell (see figure 2).

Because the virus can only infect host cells that it is able to bind to, the S1 subunit of the spike protein is responsible for host specificity — the range of hosts that the virus is able to infect. In order for a virus to be able to infect a new organism — e.g. in the transition between bat and human hosts — the receptor binding domain of the S1 subunit must gain the ability to bind to a receptor found in that new host. In both SARS-CoV and SARS-CoV-2, the human receptor appears to be the protein angiotensin converting enzyme 2 (ACE2), which is found on the surface of cells in the human respiratory tract. Interestingly, despite targeting the same receptor protein, many of the key amino acids that interact with the ACE2 receptor and that were previously thought to be essential for binding to ACE2 appear to be almost completely distinct between the SARS-CoV and SARS-CoV-2 receptor binding domains, implying that specificity for the same receptor may have evolved independently in each strain.

[caption id="attachment_3928" align="aligncenter" width="587"]

Figure 2: Structure of the SARS-CoV spike protein monomer (blue and green) bound to the ACE2 receptor (yellow). The spike protein is comprised of the S1 (blue) and S2 (green) subunits. S1/S2 and S2' cleavage sites are labelled in red. Generated using open-source PyMOL™ from the cryo-EM structure.[/caption]

Activation of the spike protein following receptor binding

Receptor binding alone is not sufficient for viral infection. Binding initiates conformational changes in the spike protein that lead to membrane fusion and infection, but another step is required before fusion can take place: cleavage of the spike protein.

There are at least two cleavage sites on the spike protein that must be cut prior to viral entry; one between the S1 and S2 subunits (S1/S2 site) and one internal to the S2 subunit (S2' site) (see figure 2, red). Cleavage at the S1/S2 site primes the protein and leads to cleavage of the S2' site, which is necessary for membrane fusion. The specific proteases (proteins that cut other proteins) that are able to perform the cleavage steps depend on the amino acid sequence that is present at each cleavage site; in many cases, several different proteases are able to cut the same site with greater or lesser efficiency.

Similar to the host-specificity of the receptor binding domain, if cleavage sites are not recognized by host proteases, cleavage and therefore infection will not be able to occur in that host. This means that both a receptor binding domain that recognizes a host target as well as cleavage sites that can be cut by host proteases are required for transmission of the virus to a novel host. For example, some bat coronaviruses have been found that are able to bind to human proteins but fail to initiate infection because their spike protein is not cleaved in human hosts.

A novel cleavage site on SARS-CoV-2

In SARS-CoV-2, a novel cleavage site has been discovered at the S1/S2 junction which is cleaved by a ubiquitous human protease known as furin. The inclusion of this novel furin site allows the SARS-CoV-2 spike protein to be cleaved during biosynthesis — this means that the protein is ‘primed’ even prior to release of the virion from the host cell. This is in contrast to the spike protein produced by SARS-CoV, which lacks this site and is released from the cell intact, requiring later cleavage before it can facilitate membrane fusion.

It is unclear whether priming during biosynthesis has an impact on viral infectivity; a 2006 study by Follis et al. found that the introduction of a furin cleavage site into SARS-CoV’s spike protein at the S1/S2 junction resulted in enhanced membrane fusion between virus and host, but could find no evidence for an accompanying increase in infectivity. It remains to be seen how the novel furin site in SARS-CoV-2 will impact its infectivity and spread.

A key target for therapeutic agents

Researchers across the globe are searching the SARS-CoV-2 genome for features that will allow it to be targeted by therapeutic agents. Due to the nature of the spike protein and its fundamental role in mediating host specificity and viral infection, it represents an attractive target for the development of therapeutic agents. In particular, mechanisms targeting receptor binding, proteolytic cleavage, and membrane fusion may prove effective in attenuating the virus’s ability to infect human cells. Due to the genetic similarity between the novel SARS-CoV-2 and SARS-CoV, including their shared receptor target, it is possible that agents shown to be effective against SARS-CoV may also prove effective at slowing SARS-CoV-2.

SARS-CoV-2 Research

The swift response of researchers worldwide to study SARS-CoV-2 and to share sequencing data publicly has allowed for rapid insights into key genetic features that will prove indispensable in the days and months to come. This tremendous, coordinated global effort to elucidate the origins and mechanisms of the virus could not have been accomplished without the aid of modern technologies allowing researchers to share data quickly across geopolitical borders. This reaffirms the essential role of technology in facilitating science, especially in the ability to respond quickly to global emergencies.

To that end, DNAstack has developed a beacon for SARS-CoV-2 where users can explore aggregated genetic variants discovered by labs worldwide. Explore it here: covid-19.dnastack.com.

About the Author

Heather is part of the Data Science Team at DNAstack, where she authors, tests, and runs analytical pipelines for internal and customer projects

References and Further Reading

  • Belouzard, S., Chu, V.C. and Whittaker, G.R. 2009. Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. PNAS106(14): 5871–5876.
  • Chan, J.F.W., Kok, K-H., Zhu, Z., Chu, H., To, K. K-W., Yuan, S. and Yuen, K-Y. 2020. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerging Microbes & Infections9: 221–246.
  • Coutard, B., Valle, C., de Lamballerie, X., Canard, B., Seidah, N.G. and Decroly, E. 2020. The spike glycoprotein of the new coronavirus 2019-nCoV contains a furin-like cleavage site absent in CoV of the same clade. Antiviral research176: 104742.
  • Gong, S. and Bao, L-L. 2018. The battle against SARS and MERS coronaviruses: Reservoirs and animal models. Animal Model Exp Med. 1:125–133.
  • Follis, K.E., York, J. and Numberg, J.H. 2006. Furin cleavage of the SARS coronavirus spike glycoprotein enhances cell-cell fusion but does not affect virion entry. Virology. 350:358–369.
  • Millet, J.K. and Whittaker, G.R. 2015. Host cell proteases: critical determinants of coronavirus tropism and pathogenesis. Virus Research. 202: 120–134.
  • Racaniello, V. Furin cleavage site in the SARS-CoV-2 coronavirus glycoprotein. Virology blog. http://www.virology.ws/2020/02/13/furin-cleavage-site-in-the-sars-cov-2-coronavirus-glycoprotein/. Published February 13, 2020. Accessed March 10, 2020.
  • Song, W., Gui, M., Wang, X. and Xiang, Y. 2018. Cryo-EM structure of the SARS coronavirus spike glycoprotein in complex with its host cell receptor ACE2. PLOS Pathogenshttps://doi.org/10.1371/journal.ppat.1007236
  • Walls, A.C., Park, Y-J., Tortorici, M.J., Wall, A., McGuire, A.T. and Veesler, D. 2020. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell180: 1–12.
  • Wong, M.C., Cregeen, S.J., Ajami, N.J. and Petrosino, J.F. 2020 (preprint). Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019. bioRxiv, preprint. https://doi.org/10.1101/2020.02.07.939207
  • Xia, S., Zhu, Y., Liu, M., Lan, Q., Xu, W., Wu, Y., Ying, T., Liu, S., Shi, Z., Jiang, S. and Lu, L. 2020. Fusion mechanism of 2019-nCoV and fusion inhibitors targeting HR1 domain in spike protein. Cellular & Molecular Immunologyhttps://doi.org/10.1038/s41423-020-0374-2
  • Xu, X., Chen, P., Wang, J., Feng, J., Zhou, H., Li, X., Zhong, W. and Hao, P. 2020. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Science China Life Sciences63(3): 457–460.

About DNAstack

DNAstack’s mission is to improve the lives of millions of people by breaking down barriers to data sharing and discovery. DNAstack develops standards and technologies for scientists to more efficiently find, access, and analyze the world’s exponentially growing volumes of genomic and biomedical data. For additional support or partnership interest, please contact us by email to info@dnastack.com.

Photo Credits 

Figure 1: CDC/Alissa Eckert, MS; Dan Higgins, MAMSFigure 2: Song et al., 2018; PDB accession 6ACK.

News
July 13, 2020

SARS-CoV-2: Biology Origins, and How Open Science is Accelerating the Search for Therapeutic Answers

DNAstack's bioinformatician Heather Ward breaks down the biology of the novel coronavirus responsible for the COVID-19 outbreak.

News
July 13, 2020

SARS-CoV-2: Biology Origins, and How Open Science is Accelerating the Search for Therapeutic Answers

DNAstack's bioinformatician Heather Ward breaks down the biology of the novel coronavirus responsible for the COVID-19 outbreak.

Press Releases
October 18, 2016

DNAstack Launched for Google Cloud Genomics

This week DNAstack, a Toronto-based genomic software company, launched its Google Cloud platform to accelerate genetic disease research and precision medicine.

Press Releases
October 18, 2016

DNAstack Launched for Google Cloud Genomics

This week DNAstack, a Toronto-based genomic software company, launched its Google Cloud platform to accelerate genetic disease research and precision medicine.

This week DNAstack, a Toronto-based genomic software company, launched its Google Cloud platform to accelerate genetic disease research and precision medicine.

Press Releases
October 18, 2016

DNAstack Launched for Google Cloud Genomics

This week DNAstack, a Toronto-based genomic software company, launched its Google Cloud platform to accelerate genetic disease research and precision medicine.

Press Releases
October 18, 2016

DNAstack Launched for Google Cloud Genomics

This week DNAstack, a Toronto-based genomic software company, launched its Google Cloud platform to accelerate genetic disease research and precision medicine.

Press Releases
June 10, 2016

A Federated Ecosystem for Sharing Genomic, Clinical Data

Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types.

Press Releases
June 10, 2016

A Federated Ecosystem for Sharing Genomic, Clinical Data

Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types.

Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types.

Press Releases
June 10, 2016

A Federated Ecosystem for Sharing Genomic, Clinical Data

Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types.

Press Releases
June 10, 2016

A Federated Ecosystem for Sharing Genomic, Clinical Data

Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types.

News
May 11, 2016

DNAstack Joins Johnson & Johnson Incubator, JLABS

JLABS will let startups in therapeutic, pharmaceutical, medical device and consumer health research test out ideas with access to state-of-the-art equipment, without giving up equity.

News
May 11, 2016

DNAstack Joins Johnson & Johnson Incubator, JLABS

JLABS will let startups in therapeutic, pharmaceutical, medical device and consumer health research test out ideas with access to state-of-the-art equipment, without giving up equity.

JLABS will let startups in therapeutic, pharmaceutical, medical device and consumer health research test out ideas with access to state-of-the-art equipment, without giving up equity.

News
May 11, 2016

DNAstack Joins Johnson & Johnson Incubator, JLABS

JLABS will let startups in therapeutic, pharmaceutical, medical device and consumer health research test out ideas with access to state-of-the-art equipment, without giving up equity.

News
May 11, 2016

DNAstack Joins Johnson & Johnson Incubator, JLABS

JLABS will let startups in therapeutic, pharmaceutical, medical device and consumer health research test out ideas with access to state-of-the-art equipment, without giving up equity.

Press Releases
July 20, 2017

Data Sharing as a National Quality Improvement Program: Reporting on BRCA1 and BRCA2 Variant-Interpretation Comparisons Through the Canadian Open Genetics Repository (COGR)

The purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.

Press Releases
July 20, 2017

Data Sharing as a National Quality Improvement Program: Reporting on BRCA1 and BRCA2 Variant-Interpretation Comparisons Through the Canadian Open Genetics Repository (COGR)

The purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.

The purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.

Press Releases
July 20, 2017

Data Sharing as a National Quality Improvement Program: Reporting on BRCA1 and BRCA2 Variant-Interpretation Comparisons Through the Canadian Open Genetics Repository (COGR)

The purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.

Press Releases
July 20, 2017

Data Sharing as a National Quality Improvement Program: Reporting on BRCA1 and BRCA2 Variant-Interpretation Comparisons Through the Canadian Open Genetics Repository (COGR)

The purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.

News
December 1, 2020

Consortium Secures $5.1M to Expand Genomics Platform for COVID Research

A national consortium led by DNAstack will expand development of a software platform for genomics and health data and apply it to COVID-19.

The $5.1M project, called COVID Cloud, is co-funded by Canada’s Digital Technology Supercluster and aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health. The platform will be used by data scientists and domain experts to help understand, predict, and treat COVID-19 with molecular precision. With a global death count of over 1.4 million people and record numbers of cases nationally, solutions that can help Canada respond to ongoing challenges of the pandemic are urgently needed.

News
December 1, 2020

Consortium Secures $5.1M to Expand Genomics Platform for COVID Research

A national consortium led by DNAstack will expand development of a software platform for genomics and health data and apply it to COVID-19.

The $5.1M project, called COVID Cloud, is co-funded by Canada’s Digital Technology Supercluster and aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health. The platform will be used by data scientists and domain experts to help understand, predict, and treat COVID-19 with molecular precision. With a global death count of over 1.4 million people and record numbers of cases nationally, solutions that can help Canada respond to ongoing challenges of the pandemic are urgently needed.

A national consortium led by DNAstack will expand development of a software platform for genomics and health data and apply it to COVID-19.

The $5.1M project, called COVID Cloud, is co-funded by Canada’s Digital Technology Supercluster and aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health. The platform will be used by data scientists and domain experts to help understand, predict, and treat COVID-19 with molecular precision. With a global death count of over 1.4 million people and record numbers of cases nationally, solutions that can help Canada respond to ongoing challenges of the pandemic are urgently needed.

“We are proud to continue to support this consortium’s groundbreaking work through our COVID-19 program,” said Sue Paish, CEO of the Digital Technology Supercluster. “This project shows how Canadian partnerships across multiple organizations and sectors can drive innovation, help us address global health issues, showcase Canadian expertise, and position us well to rebuild and grow our economy.”

The project — a collaboration between BioSymetrics, Centre of Genomics and Policy at McGill University, DNAstack, FACIT, Genome BC, Mannin Research, McMaster University, Microsoft Canada, Ontario Genomics, Ontario Institute for Cancer Research, Roche Canada, Sunnybrook Research Institute, and Vector Institute — brings together Canadian leaders in software engineering, artificial intelligence, cloud computing, genomics, infectious disease, pharmaceuticals, commercialization, and policy. It leverages past work of partners to address needs of infectious disease research with guidance from domain experts.

“Tools that allow us to interrogate SARS-CoV-2 at a molecular level are essential to addressing this global health crisis, both now and in the future,” said Dr. Samira Mubareka, a microbiologist and infectious diseases physician at Sunnybrook, whose team was one of the first in Canada to isolate the novel coronavirus. “The insights we will learn by analysing integrated datasets using technology platforms like COVID Cloud can increase our preparedness for future waves and outbreaks.” Dr. Mubareka will co-chair the project’s translational science efforts along with Dr. Gabriel Musso, Chief Scientific Officer for BioSymetrics. “The infrastructure developed by this initiative will propel collaborative Canadian drug discovery efforts for COVID-19,” said Musso, whose team will lead bioinformatics and computational drug discovery for the project.

A major goal of the project is to make it easy for producers of genomic and health data to share data responsibly over industry standards, and for researchers to harness the collective power of information shared through them. The project deliverables include a suite of software products powered by enterprise-grade implementations of standards developed by Global Alliance for Genomics & Health (GA4GH), protocols that are being designed to facilitate the responsible sharing of genomic and health data, which will help advance precision medicine initiatives around the world.

“The platform is being built on a foundation of open standards that will allow for distributed networks of genomics and biomedical data to be built,” said Dr. Marc Fiume, CEO at DNAstack, whose team will lead software engineering for the project. “We are excited to see these technologies breaking down barriers to data sharing, access, and analysis and create new opportunities for genomics-based discoveries for our partners.”

This project is responding to global demand for highly specialized, scalable, distributed software infrastructure to support collaborative genomics research — a need that has surged since the onset of the COVID-19 pandemic. “COVID-19 has accelerated digital transformation of many industries, especially in healthcare,” said Kevin Peesker, President of Microsoft Canada. “The incredible power of Cloud applied to COVID at scale is expanding development of an information superhighway to securely connect scientists in Canada and around the world to the data and compute power they urgently need to help us overcome one of the greatest global health crises of our time.”

The platform will be used to support a series of projects in partnership with Canadian academic, clinical, and pharmaceutical collaborators, which are being coordinated by Canadian genome centres, Genome British Columbia and Ontario Genomics. These initial projects are being prioritized based on urgency and potential impact on Canada’s response to the COVID-19 pandemic.

“The COVID Cloud is an incredible platform that brings together resources and capacity to enable timely and comprehensive genomic analysis of SARS-CoV-2 for our province and our country,” said Bettina Hamelin, President and CEO of Ontario Genomics, whose team leads the ONCoV Genomics Coalition. “This made-in-Canada solution will immediately accelerate Canada’s response to COVID-19, while being a technological springboard for translating genomic data analysis into actionable medical insights across other disease areas in years to come.”

For more information, visit here.

About DNAstack

DNAstack’s mission is to improve the lives of millions of people by breaking down barriers to data sharing and discovery. DNAstack develops standards and technologies for scientists to more efficiently find, access, and analyze the world’s exponentially growing volumes of genomic and biomedical data. For additional support or partnership interest, please contact us by email to info@dnastack.com.

About Digital Technology Supercluster

The Digital Technology Supercluster solves some of industry's and society's biggest problems through Canadian-made technologies. We bring together private and public sector organizations of all sizes to address challenges facing Canada's economic sectors including healthcare, natural resources, manufacturing and transportation. Through this 'collaborative innovation' the Supercluster helps to drive solutions better than any single organization could on its own.  The Digital Technology Supercluster is led by industry leaders such as D-Wave, Finger Food Advanced Technology Group, LifeLabs, LlamaZOO, Lululemon, MDA, Microsoft, Mosaic Forest Management, Sanctuary AI, Teck Resources Limited, TELUS,Terramera, and 1Qbit. Together, we work to position Canada as a global hub for digital innovation. A full list of Members can be found here.

About the COVID-19 Program

The COVID-19 Program aims to improve the health and safety of Canadians and support Canada's ability to address issues created by the COVID-19 outbreak. In addition, the program will build expertise and capacity to anticipate and address issues that may arise in future health crises, from healthcare to a return to work and community. More information can be found here.

News
December 1, 2020

Consortium Secures $5.1M to Expand Genomics Platform for COVID Research

A national consortium led by DNAstack will expand development of a software platform for genomics and health data and apply it to COVID-19.

The $5.1M project, called COVID Cloud, is co-funded by Canada’s Digital Technology Supercluster and aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health. The platform will be used by data scientists and domain experts to help understand, predict, and treat COVID-19 with molecular precision. With a global death count of over 1.4 million people and record numbers of cases nationally, solutions that can help Canada respond to ongoing challenges of the pandemic are urgently needed.

News
December 1, 2020

Consortium Secures $5.1M to Expand Genomics Platform for COVID Research

A national consortium led by DNAstack will expand development of a software platform for genomics and health data and apply it to COVID-19.

The $5.1M project, called COVID Cloud, is co-funded by Canada’s Digital Technology Supercluster and aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health. The platform will be used by data scientists and domain experts to help understand, predict, and treat COVID-19 with molecular precision. With a global death count of over 1.4 million people and record numbers of cases nationally, solutions that can help Canada respond to ongoing challenges of the pandemic are urgently needed.

Press Releases
October 11, 2018

ClinGen Advancing Genomic Data‐Sharing Standards as a GA4GH Driver Project

ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely‐available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health‐related data.

Press Releases
October 11, 2018

ClinGen Advancing Genomic Data‐Sharing Standards as a GA4GH Driver Project

ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely‐available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health‐related data.

ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely‐available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health‐related data.

Press Releases
October 11, 2018

ClinGen Advancing Genomic Data‐Sharing Standards as a GA4GH Driver Project

ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely‐available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health‐related data.

Press Releases
October 11, 2018

ClinGen Advancing Genomic Data‐Sharing Standards as a GA4GH Driver Project

ClinGen has joined with the Global Alliance for Genomics and Health (GA4GH) to support the development of open, freely‐available technical standards and regulatory frameworks for secure and responsible sharing of genomic and health‐related data.

Press Releases
November 27, 2018

Canada's Digital Technology Supercluster Officially Launches with $153M in Funding from ISED

The Government of Canada is investing up to $950 million over five years to support industry-led innovation superclusters across the country and accelerate economic growth, productivity, and competitiveness across five Superclusters.

Press Releases
November 27, 2018

Canada's Digital Technology Supercluster Officially Launches with $153M in Funding from ISED

The Government of Canada is investing up to $950 million over five years to support industry-led innovation superclusters across the country and accelerate economic growth, productivity, and competitiveness across five Superclusters.

The Government of Canada is investing up to $950 million over five years to support industry-led innovation superclusters across the country and accelerate economic growth, productivity, and competitiveness across five Superclusters.

Press Releases
November 27, 2018

Canada's Digital Technology Supercluster Officially Launches with $153M in Funding from ISED

The Government of Canada is investing up to $950 million over five years to support industry-led innovation superclusters across the country and accelerate economic growth, productivity, and competitiveness across five Superclusters.

Press Releases
November 27, 2018

Canada's Digital Technology Supercluster Officially Launches with $153M in Funding from ISED

The Government of Canada is investing up to $950 million over five years to support industry-led innovation superclusters across the country and accelerate economic growth, productivity, and competitiveness across five Superclusters.

Press Releases
February 5, 2018

The Personal Genome Project Canada: Findings from Whole Genome Sequences of the Inaugural 56 Participants

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. 

Press Releases
February 5, 2018

The Personal Genome Project Canada: Findings from Whole Genome Sequences of the Inaugural 56 Participants

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. 

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. 

Press Releases
February 5, 2018

The Personal Genome Project Canada: Findings from Whole Genome Sequences of the Inaugural 56 Participants

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. 

Press Releases
February 5, 2018

The Personal Genome Project Canada: Findings from Whole Genome Sequences of the Inaugural 56 Participants

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. 

Here we describe the Beacon protocol and how it can be used as a model for the federated discovery and sharing of genomic data.

Press Releases
March 4, 2019

Federated Discovery and Sharing of Genomic Data Using Beacons

Press Releases
February 9, 2018

Health and the Genome Puzzle: Mapping DNA Has Gotten Cheaper, But Do We Know How to Use the Data?

Not only was Michael Szego the ethics lead on the Personal Genome Project Canada — he was also a participant, agreeing to have his genome mapped and shared publicly. 

Press Releases
February 9, 2018

Health and the Genome Puzzle: Mapping DNA Has Gotten Cheaper, But Do We Know How to Use the Data?

Not only was Michael Szego the ethics lead on the Personal Genome Project Canada — he was also a participant, agreeing to have his genome mapped and shared publicly. 

Not only was Michael Szego the ethics lead on the Personal Genome Project Canada — he was also a participant, agreeing to have his genome mapped and shared publicly. 

Press Releases
February 9, 2018

Health and the Genome Puzzle: Mapping DNA Has Gotten Cheaper, But Do We Know How to Use the Data?

Not only was Michael Szego the ethics lead on the Personal Genome Project Canada — he was also a participant, agreeing to have his genome mapped and shared publicly. 

Press Releases
February 9, 2018

Health and the Genome Puzzle: Mapping DNA Has Gotten Cheaper, But Do We Know How to Use the Data?

Not only was Michael Szego the ethics lead on the Personal Genome Project Canada — he was also a participant, agreeing to have his genome mapped and shared publicly. 

Press Releases
February 26, 2018

GA4GH Releases 2018 Strategic Roadmap

The Global Alliance for Genomics and Health (GA4GH) has announced their Strategic Roadmap, which includes a series of more than two dozen deliverables to be launched in 2018 and developed over the next one to three years. 

Press Releases
February 26, 2018

GA4GH Releases 2018 Strategic Roadmap

The Global Alliance for Genomics and Health (GA4GH) has announced their Strategic Roadmap, which includes a series of more than two dozen deliverables to be launched in 2018 and developed over the next one to three years. 

The Global Alliance for Genomics and Health (GA4GH) has announced their Strategic Roadmap, which includes a series of more than two dozen deliverables to be launched in 2018 and developed over the next one to three years. 

Press Releases
February 26, 2018

GA4GH Releases 2018 Strategic Roadmap

The Global Alliance for Genomics and Health (GA4GH) has announced their Strategic Roadmap, which includes a series of more than two dozen deliverables to be launched in 2018 and developed over the next one to three years. 

Press Releases
February 26, 2018

GA4GH Releases 2018 Strategic Roadmap

The Global Alliance for Genomics and Health (GA4GH) has announced their Strategic Roadmap, which includes a series of more than two dozen deliverables to be launched in 2018 and developed over the next one to three years. 

Press Releases
June 25, 2018

DNAstack and Autism Speaks® Announce Collaboration to Accelerate Scientific Discovery on One of the World's Largest Autism Genome Databases

The Autism Speaks MSSNG project will help researchers answer the many remaining questions about the genetic underpinnings of autism by sequencing the DNA of over 10,000 families affected by autism. 

Press Releases
June 25, 2018

DNAstack and Autism Speaks® Announce Collaboration to Accelerate Scientific Discovery on One of the World's Largest Autism Genome Databases

The Autism Speaks MSSNG project will help researchers answer the many remaining questions about the genetic underpinnings of autism by sequencing the DNA of over 10,000 families affected by autism. 

The Autism Speaks MSSNG project will help researchers answer the many remaining questions about the genetic underpinnings of autism by sequencing the DNA of over 10,000 families affected by autism. 

Press Releases
June 25, 2018

DNAstack and Autism Speaks® Announce Collaboration to Accelerate Scientific Discovery on One of the World's Largest Autism Genome Databases

The Autism Speaks MSSNG project will help researchers answer the many remaining questions about the genetic underpinnings of autism by sequencing the DNA of over 10,000 families affected by autism. 

Press Releases
June 25, 2018

DNAstack and Autism Speaks® Announce Collaboration to Accelerate Scientific Discovery on One of the World's Largest Autism Genome Databases

The Autism Speaks MSSNG project will help researchers answer the many remaining questions about the genetic underpinnings of autism by sequencing the DNA of over 10,000 families affected by autism. 

Press Releases
March 14, 2018

Simplifying Research Access to Genomics and Health Data with Library Cards

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use.

Press Releases
March 14, 2018

Simplifying Research Access to Genomics and Health Data with Library Cards

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use.

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use.

Press Releases
March 14, 2018

Simplifying Research Access to Genomics and Health Data with Library Cards

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use.

Press Releases
March 14, 2018

Simplifying Research Access to Genomics and Health Data with Library Cards

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use.

Press Releases
August 2, 2018

Registered Access: Authorizing Data Access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research.

Press Releases
August 2, 2018

Registered Access: Authorizing Data Access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research.

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research.

Press Releases
August 2, 2018

Registered Access: Authorizing Data Access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research.

Press Releases
August 2, 2018

Registered Access: Authorizing Data Access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research.

Project partners will expand on infrastructure developed by DNAstack for accessing genomic data and explore patient consent models that support nationwide sharing.

Press Releases
August 9, 2018

Canadian Precision Health Infrastructure Emphasizes Secure Data Sharing, Privacy, Consent

Press Releases
January 29, 2021

Ontario Takes Immediate Action to Address COVID-19 Variants

The Ontario government has unveiled a six-point plan to prevent and stop the spread of new COVID-19 variants. 

Press Releases
January 29, 2021

Ontario Takes Immediate Action to Address COVID-19 Variants

The Ontario government has unveiled a six-point plan to prevent and stop the spread of new COVID-19 variants. 

The Ontario government has unveiled a six-point plan to prevent and stop the spread of new COVID-19 variants. 

Press Releases
January 29, 2021

Ontario Takes Immediate Action to Address COVID-19 Variants

The Ontario government has unveiled a six-point plan to prevent and stop the spread of new COVID-19 variants. 

Press Releases
January 29, 2021

Ontario Takes Immediate Action to Address COVID-19 Variants

The Ontario government has unveiled a six-point plan to prevent and stop the spread of new COVID-19 variants.