MIND Informatics develops and applies integrative computational methods in biomedical and brain research, working with leading clinicians and researchers to understand and cure neurological disorders.

Stem Cell Commons

Stem Cell Commons Logo

The Stem Cell Commons was initiated by the Harvard Stem Cell Institute to develop a community for stem cell bioinformatics to promote discovery and reproducibility in stem cell research. This open source environment for sharing, processing and analyzing stem cell data brings together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Dr. Sudeshna Das of MIND Informatics leads development of the Stem Cell Commons Data Repository.

 

Key features of the Stem Cell Commons

  • Contains stem cell related experiment data and metadata

  • Includes microarray and NGS data from human, mouse, rat and zebrafish

  • Data from multiple cell types and disease models

  • Carefully curated experimental metadata using controlled vocabularies

  • Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide

  • A community oriented resource with public data sets and freely available code in public code repositories such as GitHub

 

Currently in development

  • Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine

  • ChIP-seq analysis pipeline (additional pipelines in development)

  • Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources

 

Team

The Stem Cell Commons project is led by Dr. Winston Hide, of the Department of Biostatistics at Harvard School of Public Health.  Dr. Sudeshna Das of MIND Informatics leads development of the data repository, which is based on our eXframe reusable framework for experimental data.