Login / Signup

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

Anthony MammolitiPetr SmirnovMinoru NakanoZhaleh SafikhaniChristopher EelesHeewon SeoSisira Kadambat NairArvind S MerIan SmithChantal HoGangesh BeriRebecca Kuskonull nullEva LinYihong YuScott MartinMarc HafnerBenjamin Haibe-Kains
Published in: Nature communications (2021)
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.
Keyphrases
  • electronic health record
  • big data
  • public health
  • squamous cell carcinoma
  • high throughput
  • papillary thyroid
  • gene expression
  • minimally invasive
  • machine learning
  • chronic pain
  • copy number
  • protein kinase