Nature Communications (Oct 2021)
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,
- Massive Analysis Quality Control (MAQC) Society Board of Directors,
- Eva Lin,
- Yihong Yu,
- Scott Martin,
- Marc Hafner,
- Benjamin Haibe-Kains
Affiliations
- Anthony Mammoliti
- Princess Margaret Cancer Centre, University Health Network
- Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network
- Minoru Nakano
- Princess Margaret Cancer Centre, University Health Network
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network
- Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network
- Heewon Seo
- Princess Margaret Cancer Centre, University Health Network
- Sisira Kadambat Nair
- Princess Margaret Cancer Centre, University Health Network
- Arvind S. Mer
- Princess Margaret Cancer Centre, University Health Network
- Ian Smith
- Princess Margaret Cancer Centre, University Health Network
- Chantal Ho
- Princess Margaret Cancer Centre, University Health Network
- Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network
- Rebecca Kusko
- Immuneering Corporation
- Massive Analysis Quality Control (MAQC) Society Board of Directors
- Eva Lin
- Department of Discovery Oncology, Genentech Inc
- Yihong Yu
- Department of Discovery Oncology, Genentech Inc
- Scott Martin
- Department of Discovery Oncology, Genentech Inc
- Marc Hafner
- Department of Discovery Oncology, Genentech Inc
- Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network
- DOI
- https://doi.org/10.1038/s41467-021-25974-w
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 10
Abstract
It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.
