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Institution

Broad Institute

NonprofitCambridge, Massachusetts, United States
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.


Papers
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Journal ArticleDOI
Emek Demir1, Emek Demir2, Michael P. Cary1, Suzanne M. Paley3, Ken Fukuda, Christian Lemer4, Imre Vastrik, Guanming Wu5, Peter D'Eustachio6, Carl F. Schaefer7, Joanne S. Luciano, Frank Schacherer, Irma Martínez-Flores8, Zhenjun Hu9, Verónica Jiménez-Jacinto8, Geeta Joshi-Tope10, Kumaran Kandasamy11, Alejandra López-Fuentes8, Huaiyu Mi3, Elgar Pichler, Igor Rodchenkov12, Andrea Splendiani13, Andrea Splendiani14, Sasha Tkachev15, Jeremy Zucker16, Gopal R. Gopinath17, Harsha Rajasimha7, Harsha Rajasimha18, Ranjani Ramakrishnan19, Imran Shah20, Mustafa H Syed21, Nadia Anwar1, Özgün Babur1, Özgün Babur2, Michael L. Blinov22, Erik Brauner23, Dan Corwin, Sylva L. Donaldson12, Frank Gibbons23, Robert N. Goldberg24, Peter Hornbeck15, Augustin Luna7, Peter Murray-Rust25, Eric K. Neumann, Oliver Reubenacker22, Matthias Samwald26, Matthias Samwald27, Martijn P. van Iersel28, Sarala M. Wimalaratne29, Keith Allen30, Burk Braun, Michelle Whirl-Carrillo31, Kei-Hoi Cheung32, Kam D. Dahlquist33, Andrew Finney, Marc Gillespie34, Elizabeth M. Glass21, Li Gong31, Robin Haw5, Michael Honig35, Olivier Hubaut4, David W. Kane36, Shiva Krupa37, Martina Kutmon38, Julie Leonard30, Debbie Marks23, David Merberg39, Victoria Petri40, Alexander R. Pico41, Dean Ravenscroft42, Liya Ren10, Nigam H. Shah31, Margot Sunshine7, Rebecca Tang30, Ryan Whaley30, Stan Letovksy43, Kenneth H. Buetow7, Andrey Rzhetsky44, Vincent Schächter45, Bruno S. Sobral18, Ugur Dogrusoz2, Shannon K. McWeeney19, Mirit I. Aladjem7, Ewan Birney, Julio Collado-Vides8, Susumu Goto46, Michael Hucka47, Nicolas Le Novère, Natalia Maltsev21, Akhilesh Pandey11, Paul Thomas3, Edgar Wingender, Peter D. Karp3, Chris Sander1, Gary D. Bader12 
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Abstract: Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

673 citations

Journal ArticleDOI
TL;DR: PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes, is reported, which measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely-related subspecies, and improves consistency between phylogenetic and taxonomic groupings.
Abstract: New microbial genomes are constantly being sequenced, and it is crucial to accurately determine their taxonomic identities and evolutionary relationships. Here we report PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes. This method measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely related subspecies and improves consistency between phylogenetic and taxonomic groupings. PhyloPhlAn improved taxonomic accuracy for existing and newly sequenced genomes, detecting 157 erroneous labels, correcting 46 and placing or refining 130 new genomes. We provide examples of accurate classifications from subspecies (Sulfolobus spp.) to phyla, and of preliminary rooting of deep-branching candidate divisions, including consistent statistical support for Caldiserica (formerly candidate division OP5). PhyloPhlAn will thus be useful for both phylogenetic assessment and taxonomic quality control of newly sequenced genomes. The final phylogenies, conserved protein sequences and open-source implementation are available online.

672 citations

Journal ArticleDOI
TL;DR: How a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project is described.
Abstract: The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.

669 citations

Journal ArticleDOI
TL;DR: Seven prostate cancer risk variants are identified, five of them previously undescribed, spanning 430 kb and each independently predicting risk for prostate cancer (P = 7.9 × 10−19 for the strongest association), and common genotypes that span a more than fivefold range of susceptibility to cancer in some populations are defined.
Abstract: After the recent discovery that common genetic variation in 8q24 influences inherited risk of prostate cancer, we genotyped 2,973 SNPs in up to 7,518 men with and without prostate cancer from five populations. We identified seven risk variants, five of them previously undescribed, spanning 430 kb and each independently predicting risk for prostate cancer (P = 7.9 x 10(-19) for the strongest association, and P < 1.5 x 10(-4) for five of the variants, after controlling for each of the others). The variants define common genotypes that span a more than fivefold range of susceptibility to cancer in some populations. None of the prostate cancer risk variants aligns to a known gene or alters the coding sequence of an encoded protein.

668 citations

Journal ArticleDOI
TL;DR: The first calibrated population genetic model is presented and it is shown that, while still arbitrary, it successfully generates simulated data that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation.
Abstract: Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.

667 citations


Authors

Showing all 7146 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Albert Hofman2672530321405
Frank B. Hu2501675253464
David J. Hunter2131836207050
Kari Stefansson206794174819
Mark J. Daly204763304452
Lewis C. Cantley196748169037
Matthew Meyerson194553243726
Gad Getz189520247560
Stacey Gabriel187383294284
Stuart H. Orkin186715112182
Ralph Weissleder1841160142508
Chris Sander178713233287
Michael I. Jordan1761016216204
Richard A. Young173520126642
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202337
2022627
20211,727
20201,534
20191,364
20181,107