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.

Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants

Abstract: Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

Mass spectrometry-based proteomics turns quantitative.

Abstract: The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.

MetaPhlAn2 for enhanced metagenomic taxonomic profiling.

Abstract:  Profiling of all domains of life. Marker and quasi-marker genes are now identified not only for microbes (Bacteria and Archaea), but also for viruses and Eukaryotic microbes (Fungi, Protozoa) that are crucial components of microbial communities.  A 6-fold increase in the number of considered species. Markers are now identified from >16,000 reference genomes and >7,000 unique species, dramatically expanding the comprehensiveness of the method. The new pipeline for identifying marker genes is also scalable to the quickly increasing number of reference genomes. See Supplementary Tables 1-3.  Introduction of the concept of quasi-markers, allowing more comprehensive and accurate profiling. For species with less than 200 markers, MetaPhlAn2 adopts additional quasi-marker sequences (Supplementary Note 2) that are occasionally present in other genomes (because of vertical conservation or horizontal transfer). At profiling time, if no other markers of the potentially confounding species are detected, the corresponding quasi-local markers are used to improve the quality and accuracy of the profiling.  Addition of strain-specific barcoding for microbial strain tracking. MetaPhlAn2 includes a completely new feature that exploits marker combinations to perform species-specific and genus-specific “barcoding” for strains in metagenomic samples (Supplementary Note 7). This feature can be used for culture-free pathogen tracking in epidemiology studies and strain tracking across microbiome samples. See Supplementary Figs. 12-20.  Strain-level identification for organisms with sequenced genomes. For the case in which a microbiome includes strains that are very close to one of those already sequenced, MetaPhlAn2 is now able to identify such strains and readily reports their abundances. See Supplementary Note 7, Supplementary Table 13, and Supplementary Fig. 21.  Improvement of false positive and false negative rates. Improvements in the underlying pipeline for identifying marker genes (including the increment of the adopted genomes and the use of quasi-markers) and the profiling procedure resulted in much improved quantitative performances (higher correlation with true abundances, lower false positive and false negative rates). See the validation on synthetic metagenomes in Supplementary Note 4.  Estimation of the percentage of reads mapped against known reference genomes. MetaPhlAn2 is now able to estimate the number of reads that would map against genomes of each clade detected as present and for which an estimation of its relative abundance is provided by the default output. See Supplementary Note 3 for details.  Integration of MetaPhlAn with post-processing and visualization tools. The MetaPhlAn2 package now includes a set of post-processing and visualization tools (“utils” subfolder of the MetaPhlAn2 repository). Multiple MetaPhlAn profiles can in fact be merged in an abundance table (“merge_metaphlan_tables.py”), exported as BIOM files, visualized as heatmap (“metaphlan_hclust_heatmap.py” or the integrated “hclust2” package), GraPhlAn plots (“export2graphlan.py” and the GraPhlAn package1), Krona2 plots (“metaphlan2krona.py”), and single microbe barplot across samples and conditions (“plot_bug.py”).

High-quality draft assemblies of mammalian genomes from massively parallel sequence data

Abstract: Massively parallel DNA sequencing technologies are revolutionizing genomics by making it possible to generate billions of relatively short (~100-base) sequence reads at very low cost. Whereas such data can be readily used for a wide range of biomedical applications, it has proven difficult to use them to generate high-quality de novo genome assemblies of large, repeat-rich vertebrate genomes. To date, the genome assemblies generated from such data have fallen far short of those obtained with the older (but much more expensive) capillary-based sequencing approach. Here, we report the development of an algorithm for genome assembly, ALLPATHS-LG, and its application to massively parallel DNA sequence data from the human and mouse genomes, generated on the Illumina platform. The resulting draft genome assemblies have good accuracy, short-range contiguity, long-range connectivity, and coverage of the genome. In particular, the base accuracy is high (≥99.95%) and the scaffold sizes (N50 size = 11.5 Mb for human and 7.2 Mb for mouse) approach those obtained with capillary-based sequencing. The combination of improved sequencing technology and improved computational methods should now make it possible to increase dramatically the de novo sequencing of large genomes. The ALLPATHS-LG program is available at http://www.broadinstitute.org/science/programs/genome-biology/crd.

Integrative genomics identifies MCU as an essential component of the mitochondrial calcium uniporter

Abstract: Mitochondria from diverse organisms are capable of transporting large amounts of Ca(2+) via a ruthenium-red-sensitive, membrane-potential-dependent mechanism called the uniporter Although the uniporter's biophysical properties have been studied extensively, its molecular composition remains elusive We recently used comparative proteomics to identify MICU1 (also known as CBARA1), an EF-hand-containing protein that serves as a putative regulator of the uniporter Here, we use whole-genome phylogenetic profiling, genome-wide RNA co-expression analysis and organelle-wide protein coexpression analysis to predict proteins functionally related to MICU1 All three methods converge on a novel predicted transmembrane protein, CCDC109A, that we now call 'mitochondrial calcium uniporter' (MCU) MCU forms oligomers in the mitochondrial inner membrane, physically interacts with MICU1, and resides within a large molecular weight complex Silencing MCU in cultured cells or in vivo in mouse liver severely abrogates mitochondrial Ca(2+) uptake, whereas mitochondrial respiration and membrane potential remain fully intact MCU has two predicted transmembrane helices, which are separated by a highly conserved linker facing the intermembrane space Acidic residues in this linker are required for its full activity However, an S259A point mutation retains function but confers resistance to Ru360, the most potent inhibitor of the uniporter Our genomic, physiological, biochemical and pharmacological data firmly establish MCU as an essential component of the mitochondrial Ca(2+) uniporter