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| SING | |
|---|---|
| Name | SING |
| Type | Research initiative |
| Founded | 20XX |
| Founder | European Union Horizon 2020 consortium |
| Headquarters | Berlin |
| Fields | Bioinformatics; Genomics; Proteomics |
| Products | Data platform; Analytical pipelines |
SING
SING is an international research initiative and computational platform established to integrate large-scale genomics and proteomics datasets for cross-disciplinary analysis. It brings together consortia from institutions such as Max Planck Society, Wellcome Trust Sanger Institute, European Molecular Biology Laboratory, and Broad Institute to harmonize datasets, develop standardized pipelines, and provide shared infrastructure. The project emphasizes FAIR data principles championed by organizations like GO FAIR and standards bodies including Global Alliance for Genomics and Health.
The name originates as an acronym coined by a steering committee comprising representatives from Horizon 2020, National Institutes of Health, and the European Bioinformatics Institute. Early documents referenced partners such as University of Cambridge, Harvard Medical School, and Karolinska Institutet when formalizing the acronym, which was subsequently adopted in grant proposals submitted to European Research Council and philanthropic funders like the Bill & Melinda Gates Foundation. Minutes from workshops held at venues including Cold Spring Harbor Laboratory and EMBL-EBI record deliberations on branding and acronym clarity.
SING emerged from pilot collaborations among groups at ETH Zurich, University of Oxford, Massachusetts Institute of Technology, and Stanford University seeking to reconcile disparate datasets produced for projects like the 1000 Genomes Project, the Human Proteome Project, and the ENCODE Project. Initial phases mirrored methods used in consortia such as Human Cell Atlas and drew on data models from DBpedia and GenBank. Funding rounds involved calls from European Commission programmes and joint awards by agencies including National Science Foundation and Wellcome Trust. Technical milestones were achieved through hackathons hosted by GitHub sponsors and workshops at EMBO conferences. The development track incorporated lessons from platforms like Galaxy Project, CWL (Common Workflow Language), and Docker containerization.
SING aims to provide interoperable infrastructure for multi-omics integration to support researchers at institutions such as Johns Hopkins University, Yale University, University of Tokyo, and Peking University. Its scope covers standardized metadata schemas influenced by MIAME and ontologies used by Gene Ontology and Human Phenotype Ontology, enabling comparative studies spanning datasets from The Cancer Genome Atlas, GTEx, and population cohorts like UK Biobank. Strategic objectives align with priorities of advisory bodies such as World Health Organization and research roadmaps proposed by National Institutes of Health centers. The initiative targets translational pipelines relevant to consortia including ClinGen and eMERGE Network.
Organizationally, SING is governed by a board with seats allocated to representatives from European Research Council, National Institutes of Health, Wellcome Trust, and participating universities such as Imperial College London and University of California, Berkeley. Core components include a federated data repository interoperable with FAIR Data Point implementations, containerized analysis workflows compatible with Nextflow and Snakemake, and identity/access frameworks interoperable with ORCID and ELIXIR. Technical modules reuse libraries and standards from Bioconductor, UCSC Genome Browser, and Ensembl while cloud deployments rely on services like Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Partnerships with infrastructure projects such as European Open Science Cloud and organizations like DataCite provide DOI minting and provenance tracking.
Implementations of SING have been piloted in collaborations involving University of Toronto, McGill University, Seoul National University, and clinical centers such as Mayo Clinic and Sana Kliniken. Application areas include integrative oncology studies leveraging cohorts from International Cancer Genome Consortium, pathogen surveillance initiatives linked to Global Influenza Surveillance and Response System, and developmental biology efforts echoing frameworks from Allen Institute for Brain Science. Toolkits enable reproducible analyses for biomarker discovery, drug target prioritization aligned with initiatives like Open Targets, and population genomics crosswalks with datasets from HapMap and 100,000 Genomes Project. Training materials have been deployed through partnerships with Coursera, edX, and workshops at institutions such as Carnegie Mellon University.
SING has been praised by stakeholders including funders like Wellcome Trust and research networks such as ELIXIR for advancing interoperability and accelerating multi-omics research. Commentaries in outlets associated with institutions like Nature Research and PLOS have highlighted its potential to reduce duplicative efforts seen in earlier initiatives like Human Genome Project-era repositories. Criticisms target governance complexity involving bodies such as European Commission and National Institutes of Health, concerns about data sovereignty raised by national partners including Australia Research Council-funded groups, and challenges in integrating legacy datasets from repositories like ArrayExpress and Sequence Read Archive. Ethical and privacy debates reference frameworks from Council of Europe and International Committee of Medical Journal Editors regarding consent models and secondary use. Ongoing responses include policy workshops at UNESCO and technical audits by groups affiliated with IEEE and ISCB.