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      The Dockstore: enhancing a community platform for sharing reproducible and accessible computational protocols

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          Abstract

          Dockstore ( https://dockstore.org/) is an open source platform for publishing, sharing, and finding bioinformatics tools and workflows. The platform has facilitated large-scale biomedical research collaborations by using cloud technologies to increase the Findability, Accessibility, Interoperability and Reusability (FAIR) of computational resources, thereby promoting the reproducibility of complex bioinformatics analyses. Dockstore supports a variety of source repositories, analysis frameworks, and language technologies to provide a seamless publishing platform for authors to create a centralized catalogue of scientific software. The ready-to-use packaging of hundreds of tools and workflows, combined with the implementation of interoperability standards, enables users to launch analyses across multiple environments. Dockstore is widely used, more than twenty-five high-profile organizations share analysis collections through the platform in a variety of workflow languages, including the Broad Institute's GATK best practice and COVID-19 workflows (WDL), nf-core workflows (Nextflow), the Intergalactic Workflow Commission tools (Galaxy), and workflows from Seven Bridges (CWL) to highlight just a few. Here we describe the improvements made over the last four years, including the expansion of system integrations supporting authors, the addition of collaboration features and analysis platform integrations supporting users, and other enhancements that improve the overall scientific reproducibility of Dockstore content.

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          Dockstore: enhancing an open platform for sharing Docker-based tools described with workflow languages for the sciences.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Pan-cancer analysis of whole genomes

            Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 ; and evaluates a range of more-specialized features of cancer genomes 8,10–18 .
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              Nextflow enables reproducible computational workflows

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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                12 May 2021
                12 May 2021
                : gkab346
                Affiliations
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                Data Sciences Platform, Broad Institute , Boston, MA 02142, USA
                UC Santa Cruz Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95060, USA
                Adaptive Oncology, Ontario Institute for Cancer Research , Toronto, Ontario M5V 3S1, Canada
                Author notes
                To whom correspondence should be addressed. Tel: +1 647 260 7991; Email: denis.yuen@ 123456oicr.on.ca
                Correspondence may also be addressed to Louise Cabansay. Email: lcabansa@ 123456ucsc.edu

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

                Author information
                https://orcid.org/0000-0002-6130-1021
                https://orcid.org/0000-0002-1475-5979
                Article
                gkab346
                10.1093/nar/gkab346
                8218198
                33978761
                756b7300-281c-420a-8248-393a46fc7362
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 April 2021
                : 01 April 2021
                : 02 March 2021
                Page count
                Pages: 9
                Funding
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: PA-14-156
                Award ID: 5R01HG009737-01
                Funded by: NHLBI, DOI 10.13039/100000050;
                Award ID: RFA-RM-17-026
                Award ID: 1 OT3 HL142481-01-01
                Funded by: NHLGRI;
                Award ID: RFA-HG-17-011
                Award ID: 1 U24 HG010262
                Funded by: Genome Canada, DOI 10.13039/100008762;
                Funded by: Ontario Genomics Institute, DOI 10.13039/501100000092;
                Award ID: OGI-168
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue
                Custom metadata
                PAP

                Genetics
                Genetics

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