Orchestrating and sharing large multimodal data for transparent and reproducible research.

TitleOrchestrating and sharing large multimodal data for transparent and reproducible research.
Publication TypeJournal Article
Year of Publication2021
AuthorsMammoliti, A, Smirnov, P, Nakano, M, Safikhani, Z, Eeles, C, Seo, H, Nair, SKadambat, Mer, AS, Smith, I, Ho, C, Beri, G, Kusko, R, Lin, E, Yu, Y, Martin, S, Hafner, M, Haibe-Kains, B
Corporate AuthorsMassive Analysis Quality Control (MAQC) Society Board of Directors
JournalNat Commun
Date Published2021 10 04

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.

Alternate JournalNat Commun
PubMed ID34608132
PubMed Central IDPMC8490371
Grant List / / CIHR / Canada