Orchestrating and sharing large multimodal data for transparent and reproducible research

Anthony Mammoliti, Petr Smirnov, Minoru Nakano, Zhaleh Safikhani, Christopher Eeles, Heewon Seo, Sisira Kadambat Nair, Arvind S. Mer, Ian Smith, Chantal Ho, Gangesh Beri, Rebecca Kusko, Eva Lin, Yihong Yu, Scott Martin, Marc Hafner and Benjamin Haibe-Kains ()
Additional contact information
Anthony Mammoliti: University Health Network
Petr Smirnov: University Health Network
Minoru Nakano: University Health Network
Zhaleh Safikhani: University Health Network
Christopher Eeles: University Health Network
Heewon Seo: University Health Network
Sisira Kadambat Nair: University Health Network
Arvind S. Mer: University Health Network
Ian Smith: University Health Network
Chantal Ho: University Health Network
Gangesh Beri: University Health Network
Rebecca Kusko: Immuneering Corporation
Eva Lin: Genentech Inc
Yihong Yu: Genentech Inc
Scott Martin: Genentech Inc
Marc Hafner: Genentech Inc
Benjamin Haibe-Kains: University Health Network

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Date: 2021
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DOI: 10.1038/s41467-021-25974-w

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