Orchestrating and sharing large multimodal data for transparent and reproducible research

Reproducibility is essential to open science, yet the growing complexity and scale of biomedical data make it increasingly difficult to process, analyze, and share in accordance with FAIR (findable, accessible, interoperable, reusable) principles. To address these challenges, the authors developed ORCESTRA (orcestra.ca), a cloud-based platform that provides a flexible framework for the reproducible processing of multimodal biomedical data. ORCESTRA automates and customizes processing pipelines for clinical, genomic, and perturbation profiles of cancer samples; generates integrated, fully documented data objects with persistent identifiers (DOIs); and manages multiple dataset versions for transparent sharing and reuse.

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, Benjamin Haibe-Kains