News from the Broad

The Broad Institute is committed to open sharing not only of its scientific data and tools, but also information and news about our progress towards achieving our mission. Below are just a few highlights from the Broad scientific community.
  • Compendium of immune signatures to improve Gene Set Enrichment Analysis

    January 26th, 2016

    Researchers Nick Haining and Jernej Godec of Dana-Farber Cancer Institute, Harvard Medical School, and Broad Institute collaborated with Broad and UC San Francisco computational biologists to build a collection of gene signatures from different immune cell types and perturbations. The collection, called ImmuneSigDB, includes almost 5,000 new gene sets and has been added to MSigDB. The additions are expected to greatly improve the use of Gene Set Enrichment Analysis for immunological studies; in a paper published by the journal Immunity, the team uses the collection to identify species-specific and shared aspects of the biology of the immune response to sepsis between mice and humans.

  • Broad Institute Genomic Services partners with Takeda Pharmaceuticals to offer genomic insight into the recently approved therapeutic NINLARO® (ixazomib)

    January 22nd, 2016
    Broad Institute Genomic Services has partnered with Takeda Pharmaceutical Company Limited to perform genomic analysis on patient samples from a Phase 3 clinical trial of NINLARO® (ixazomib), an oral proteasome inhibitor that was recently approved by the U.S. Food and Drug Administration (FDA), indicated in combination with lenalidomide and dexamethasone, for the treatment of patients with multiple myeloma who have received at least one...
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  • Stuart Schreiber receives 2016 Wolf Prize in Chemistry

    January 14th, 2016
    Broad Institute core member receives 2016 Wolf Prize for his fundamental contributions to the field of chemical biology
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  • Broad Institute awarded grants to propel studies of common, rare diseases

    January 14th, 2016
    NGHRI grants will fund two centers at the Broad that harness genome sequencing to tackle human disease
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  • New method applies artificial intelligence to images to identify cell types

    January 13th, 2016

    Conventional flow cytometry is a powerful technique for measuring cell phenotype and function, but it relies on fluorescent stains, or labels, to identify particular cell subpopulations. At times, those labels can be incompatible with live cells, or unavailable to researchers. Now, researchers from the Broad Institute’s Imaging Platform, Swansea University’s College of Engineering, and fellow international collaborators have found a new way to detect these cellular subpopulations, by applying machine learning to the hidden information in images of unlabeled cells generated from image flow cytometry. Their method is described in Nature Communications and a Swansea University press release, and their open-source workflow is available online.