The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

EpiVar Browser: advanced exploration of epigenomics data under controlled access

MOTIVATION

Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets.

RESULTS

Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed an approach and a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because individual genotypes and epigenetic signal tracks are not directly accessible, and rather aggregated in the portal output, no identifiable data is released, yet the interface allows for dynamic genotype-epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalizable strategy to facilitate responsible access to sensitive epigenomics data.

AVAILABILITY AND IMPLEMENTATION

Online portal: https://computationalgenomics.ca/tools/epivar; EpiVar Browser source code: https://github.com/c3g/epivar-browser; bw-merge-window tool source code: https://github.com/c3g/bw-merge-window.

Epigenetic variation impacts individual differences in the transcriptional response to influenza infection

Humans display remarkable interindividual variation in their immune response to identical challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we performed in-depth genetic, epigenetic and transcriptional profiling on primary macrophages derived from individuals of European and African ancestry before and after infection with influenza A virus. We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to influenza A virus across individuals. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation, with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape can be genetically controlled, even more than gene expression. Lastly, among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, while the remaining genetic variants impact epigenetic marks, stressing the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.

EpiVar Browser: advanced exploration of epigenomics data under controlled access

Motivation

Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets.

Results

Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because the information about individual genotypes is not accessible and aggregated in the output that is made available, no identifiable data is released, yet the interface allows for dynamic genotype - epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalisable strategy to facilitate responsible access to sensitive epigenomics data.

Availability and implementation

Online portal instance: https://computationalgenomics.ca/tools/epivarSource code: https://github.com/c3g/epivar-browser.

Data and Tools Integration in the Canadian Open Neuroscience Platform

We present the Canadian Open Neuroscience Platform (CONP) portal to answer the research community's need for flexible data sharing resources and provide advanced tools for search and processing infrastructure capacity. This portal differs from previous data sharing projects as it integrates datasets originating from a number of already existing platforms or databases through DataLad, a file level data integrity and access layer. The portal is also an entry point for searching and accessing a large number of standardized and containerized software and links to a computing infrastructure. It leverages community standards to help document and facilitate reuse of both datasets and tools, and already shows a growing community adoption giving access to more than 60 neuroscience datasets and over 70 tools. The CONP portal demonstrates the feasibility and offers a model of a distributed data and tool management system across 17 institutions throughout Canada.

RNAget: an API to securely retrieve RNA quantifications

SUMMARY

Large-scale sharing of genomic quantification data requires standardized access interfaces. In this Global Alliance for Genomics and Health project, we developed RNAget, an API for secure access to genomic quantification data in matrix form. RNAget provides for slicing matrices to extract desired subsets of data and is applicable to all expression matrix-format data, including RNA sequencing and microarrays. Further, it generalizes to quantification matrices of other sequence-based genomics such as ATAC-seq and ChIP-seq.

AVAILABILITY AND IMPLEMENTATION

https://ga4gh-rnaseq.github.io/schema/docs/index.html.

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

A small number of early introductions seeded widespread transmission of SARS-CoV-2 in Québec, Canada

Background

Québec was the Canadian province most impacted by COVID-19, with 401,462 cases as of September 24th, 2021, and 11,347 deaths due mostly to a very severe first pandemic wave. In April 2020, we assembled the Coronavirus Sequencing in Québec (CoVSeQ) consortium to sequence SARS-CoV-2 genomes in Québec to track viral introduction events and transmission within the province.

Methods

Using genomic epidemiology, we investigated the arrival of SARS-CoV-2 to Québec. We report 2921 high-quality SARS-CoV-2 genomes in the context of > 12,000 publicly available genomes sampled globally over the first pandemic wave (up to June 1st, 2020). By combining phylogenetic and phylodynamic analyses with epidemiological data, we quantify the number of introduction events into Québec, identify their origins, and characterize the spatiotemporal spread of the virus.

Results

Conservatively, we estimated approximately 600 independent introduction events, the majority of which happened from spring break until 2 weeks after the Canadian border closed for non-essential travel. Subsequent mass repatriations did not generate large transmission lineages (> 50 sequenced cases), likely due to mandatory quarantine measures in place at the time. Consistent with common spring break and “snowbird” destinations, most of the introductions were inferred to have originated from Europe via the Americas. Once introduced into Québec, viral lineage sizes were overdispersed, with a few lineages giving rise to most infections. Consistent with founder effects, the earliest lineages to arrive tended to spread most successfully. Fewer than 100 viral introductions arrived during spring break, of which 7–12 led to the largest transmission lineages of the first wave (accounting for 52–75% of all sequenced infections). These successful transmission lineages dispersed widely across the province. Transmission lineage size was greatly reduced after March 11th, when a quarantine order for returning travellers was enacted. While this suggests the effectiveness of early public health measures, the biggest transmission lineages had already been ignited prior to this order.

Conclusions

Combined, our results reinforce how, in the absence of tight travel restrictions or quarantine measures, fewer than 100 viral introductions in a week can ensure the establishment of extended transmission chains.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00986-9.

The epiGenomic Efficient Correlator (epiGeEC) tool allows fast comparison of user datasets with thousands of public epigenomic datasets

Summary

In recent years, major initiatives such as the International Human Epigenome Consortium have generated thousands of high-quality genome-wide datasets for a large variety of assays and cell types. This data can be used as a reference to assess whether the signal from a user-provided dataset corresponds to its expected experiment, as well as to help reveal unexpected biological associations. We have developed the epiGenomic Efficient Correlator (epiGeEC) tool to enable genome-wide comparisons of very large numbers of datasets. A public Galaxy implementation of epiGeEC allows comparison of user datasets with thousands of public datasets in a few minutes.

Availability and implementation

The source code is available at https://bitbucket.org/labjacquespe/epigeec and the Galaxy implementation at http://epigeec.genap.ca.

Supplementary information

Supplementary data are available at Bioinformatics online.

GenPipes: an open-source framework for distributed and scalable genomic analyses

BACKGROUND

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing.

FINDINGS

Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for high-performance computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA sequencing, chromatin immunoprecipitation sequencing, DNA sequencing, methylation sequencing, Hi-C, capture Hi-C, metagenomics, and Pacific Biosciences long-read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has already been configured on several servers, and a Docker image is also available to facilitate additional installations.

CONCLUSIONS

GenPipes offers genomics researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14⁺ monocytes, CD16⁺ neutrophils, and naive CD4⁺ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

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Genome, transcriptome, and epigenome reference panel in three human immune cell types

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Identified 4,418 genes associated with epigenetic changes independent of genetics

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Described genome-epigenome coordination defining cell-type-specific regulatory events

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Functionally mapped disease mechanisms at 345 unique autoimmune disease loci

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

The incidence of type 1 diabetes is increasing, potentially implicating non-genetic factors. Here the authors conduct an epigenome-wide association study in disease-discordant twins and find increased DNA methylation variability at genes associated with immune cell metabolism and the cell cycle.

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

The International Human Epigenome Consortium Data Portal

The International Human Epigenome Consortium (IHEC) coordinates the production of reference epigenome maps through the characterization of the regulome, methylome, and transcriptome from a wide range of tissues and cell types. To define conventions ensuring the compatibility of datasets and establish an infrastructure enabling data integration, analysis, and sharing, we developed the IHEC Data Portal (http://epigenomesportal.ca/ihec). The portal provides access to >7,000 reference epigenomic datasets, generated from >600 tissues, which have been contributed by seven international consortia: ENCODE, NIH Roadmap, CEEHRC, Blueprint, DEEP, AMED-CREST, and KNIH. The portal enhances the utility of these reference maps by facilitating the discovery, visualization, analysis, download, and sharing of epigenomics data. The IHEC Data Portal is the official source to navigate through IHEC datasets and represents a strategy for unifying the distributed data produced by international research consortia.