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URL: http://stemcellcommons.org/
Proper Citation: Stem Cell Commons (RRID:SCR_004415)
Description: Open source environment for sharing, processing and analyzing stem cell data bringing 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. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (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 Stem Cell Commons is based on open source software and is available for download and development.
Abbreviations: Stem Cell Commons
Synonyms: HSCI Blood Genomics, Harvard Stem Cell Institute Blood Genomics, Harvard Stem Cell Institute Blood Program, HSCI Blood Program
Resource Type: source code, database, service resource, data or information resource, storage service resource, data repository, production service resource, data set, analysis service resource, software resource
Defining Citation: PMID:24303302
Keywords: therapeutic target, blood, stem cell, self-renewal, embryonic stem cell, hematopoietic stem cell, leukemia stem cell, gene, protein, phenotype, therapeutic, annotate, share, analyze, data sharing, statistics, visualize, analyze, microarray, next-generation sequencing, statistics, transcription profiling, genome, genome browser, disease model
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