Loom Workflow Engine
Making your data analysis portable, traceable, and reproducible.
Design goals for Loom:
- Simple: Loom runs out-of-the-box locally or in the cloud. Simple to write, read and edit workflows.
- Portable: Exactly the same workflow can be run on your laptop or on a public cloud service.
- Repeatable: Loom makes sure you can repeat your analysis months and years down the road after you've lost your notebook, your data analyst has found a new job, and your server has had a major OS version upgrade.
- Shareable: You completed your analysis. Now share with collaborators, NIH and the community.
- Traceable: Keep track of the actions in a separate database.
- Auditable: Make the traecability easy to interpret for purpose of audit. This feature is built for CLIA/CAP compatibility.
Projects using Loom
Stanford Clinical Genomics Service
Sequencing the human genome was one of the major scientific achievements of the last 50 years. Now, this technology has transformed the way we approach and diagnose genetic disease. The Clinical Genomics Service at Stanford Medicine brings the power of genomic sequencing into the hands of our physicians and with it, the knowledge into the hands of our patients. The Clinical Genomics Service (CGS) is a joint effort between our adult health care systems (Stanford Health Care), pediatric health care system (Stanford Children's Health), and the Stanford School of Medicine. To support CGS, the informatics team is in the process of developing an end-to-end Genomics Information Management Systems (GIMS), which meets the reproducibility, auditability, and traceability requirements of clinical testing, capabilities provided by Loom.
The video is from a seminar presented by Sowmi Utiramerur, Director of Genome Informatics at Stanford Health Care (SHC) and Nathan Hammond, Sr. Scientist at SHC who present on "GIMS – A true end-to-end analytical platform solution for Precision Medicine".
Isaac Liao, PhD
Isaac is a software engineer at the Stanford Center for Genomics and Personalized Medicine. He is part of a team that is currently designing and implementing a cloud-agnostic framework for processing medically relevant data. Previously, he was a postdoc at UC Davis studying data visualization, during which he programmed an interactive science exhibit about plankton at the Exploratorium in San Francisco. His graduate work applied machine learning techniques to gene expression data in neurodevelopmental disorders.
Nathan Hammond, PhD
Nathan is a Senior Scientist at Stanford Health Care on the Clinical Genomics Service team. Previously he worked as a software developer with the Stanford Center for Genomics and Personalized Medicine, and in software testing for the Mathworks bioinformatics team. His free time is dedicated to endless home repairs, hanging out with his wife, negotiating dishwashing schedules with his college-age roommates, and looking after a dog that crawled through a hole in his fence and never went home.