Broad Institute

About: Broad Institute is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 6584 authors who have published 11618 publications receiving 1522743 citations. The organization is also known as: Eli and Edythe L. Broad Institute of MIT and Harvard.

Systematic mapping of functional enhancer-promoter connections with CRISPR interference

Abstract: Gene expression in mammals is regulated by noncoding elements that can affect physiology and disease, yet the functions and target genes of most noncoding elements remain unknown. We present a high-throughput approach that uses clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) to discover regulatory elements and identify their target genes. We assess >1 megabase of sequence in the vicinity of two essential transcription factors, MYC and GATA1, and identify nine distal enhancers that control gene expression and cellular proliferation. Quantitative features of chromatin state and chromosome conformation distinguish the seven enhancers that regulate MYC from other elements that do not, suggesting a strategy for predicting enhancer-promoter connectivity. This CRISPRi-based approach can be applied to dissect transcriptional networks and interpret the contributions of noncoding genetic variation to human disease.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with biobakery 3

Abstract: Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations

Abstract: Meningiomas are the most common primary nervous system tumor. The tumor suppressor NF2 is disrupted in approximately half of all meningiomas, but the complete spectrum of genetic changes remains undefined. We performed whole-genome or whole-exome sequencing on 17 meningiomas and focused sequencing on an additional 48 tumors to identify and validate somatic genetic alterations. Most meningiomas had simple genomes, with fewer mutations, rearrangements and copy-number alterations than reported in other tumors in adults. However, several meningiomas harbored more complex patterns of copy-number changes and rearrangements, including one tumor with chromothripsis. We confirmed focal NF2 inactivation in 43% of tumors and found alterations in epigenetic modifiers in an additional 8% of tumors. A subset of meningiomas lacking NF2 alterations harbored recurrent oncogenic mutations in AKT1 (p.Glu17Lys) and SMO (p.Trp535Leu) and exhibited immunohistochemical evidence of activation of these pathways. These mutations were present in therapeutically challenging tumors of the skull base and higher grade. These results begin to define the spectrum of genetic alterations in meningiomas and identify potential therapeutic targets.

EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

Abstract: Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.