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Institution

University of Utah

EducationSalt Lake City, Utah, United States
About: University of Utah is a education organization based out in Salt Lake City, Utah, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52894 authors who have published 124076 publications receiving 5265834 citations. The organization is also known as: The U & The University of Utah.


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Journal ArticleDOI
TL;DR: Ultra-long reads enabled assembly and phasing of the 4-Mb major histocompatibility complex (MHC) locus in its entirety, measurement of telomere repeat length, and closure of gaps in the reference human genome assembly GRCh38.
Abstract: We report the sequencing and assembly of a reference genome for the human GM12878 Utah/Ceph cell line using the MinION (Oxford Nanopore Technologies) nanopore sequencer. 91.2 Gb of sequence data, representing ∼30× theoretical coverage, were produced. Reference-based alignment enabled detection of large structural variants and epigenetic modifications. De novo assembly of nanopore reads alone yielded a contiguous assembly (NG50 ∼3 Mb). We developed a protocol to generate ultra-long reads (N50 > 100 kb, read lengths up to 882 kb). Incorporating an additional 5× coverage of these ultra-long reads more than doubled the assembly contiguity (NG50 ∼6.4 Mb). The final assembled genome was 2,867 million bases in size, covering 85.8% of the reference. Assembly accuracy, after incorporating complementary short-read sequencing data, exceeded 99.8%. Ultra-long reads enabled assembly and phasing of the 4-Mb major histocompatibility complex (MHC) locus in its entirety, measurement of telomere repeat length, and closure of gaps in the reference human genome assembly GRCh38.

1,425 citations

Journal ArticleDOI
TL;DR: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation.
Abstract: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.

1,425 citations

Journal ArticleDOI
TL;DR: Kowalski et al. as mentioned in this paper reported on work to increase the number of well-measured Type Ia supernovae (SNe Ia) at high redshifts.
Abstract: We report on work to increase the number of well-measured Type Ia supernovae (SNe Ia) at high redshifts. Light curves, including high signal-to-noise HST data, and spectra of six SNe Ia that were discovered during 2001 are presented. Additionally, for the two SNe with z > 1, we present groundbased J-band photometry from Gemini and the VLT. These are among the most distant SNe Ia for which ground based near-IR observations have been obtained. We add these six SNe Ia together with other data sets that have recently become available in the literature to the Union compilation (Kowalski et al. 2008). We have made a number of refinements to the Union analysis chain, the most important ones being the refitting of all light curves with the SALT2 fitter and an improved handling of systematic errors. We call this new compilation, consisting of 557 supernovae, the Union2

1,424 citations

Journal ArticleDOI
TL;DR: A triggering mechanism for automatically maintaining these invariants during update operations is proposed, and a simple mapping of aggregation/generalization hierarchies onto owner-coupled set structures is given.
Abstract: Two kinds of abstraction that are fundamentally important in database design and usage are defined Aggregation is an abstraction which turns a relationship between objects into an aggregate object Generalization is an abstraction which turns a class of objects into a generic object It is suggested that all objects (individual, aggregate, generic) should be given uniform treatment in models of the real world A new data type, called generic, is developed as a primitive for defining such models Models defined with this primitive are structured as a set of aggregation hierarchies intersecting with a set of generalization hierarchies Abstract objects occur at the points of intersection This high level structure provides a discipline for the organization of relational databases In particular this discipline allows: (i) an important class of views to be integrated and maintained; (ii) stability of data and programs under certain evolutionary changes; (iii) easier understanding of complex models and more natural query formulation; (iv) a more systematic approach to database design; (v) more optimization to be performed at lower implementation levels The generic type is formalized by a set of invariant properties These properties should be satisfied by all relations in a database if abstractions are to be preserved A triggering mechanism for automatically maintaining these invariants during update operations is proposed A simple mapping of aggregation/generalization hierarchies onto owner-coupled set structures is given

1,414 citations

Journal ArticleDOI
Giovanni Ciriello1, Giovanni Ciriello2, Michael L. Gatza3, Michael L. Gatza4, Andrew H. Beck5, Matthew D. Wilkerson4, Suhn K. Rhie6, Alessandro Pastore2, Hailei Zhang7, Michael D. McLellan8, Christina Yau9, Cyriac Kandoth2, Reanne Bowlby10, Hui Shen11, Sikander Hayat2, Robert J. Fieldhouse2, Susan C. Lester5, Gary M. Tse12, Rachel E. Factor13, Laura C. Collins5, Kimberly H. Allison14, Yunn Yi Chen15, Kristin C. Jensen16, Kristin C. Jensen14, Nicole B. Johnson5, Steffi Oesterreich17, Gordon B. Mills18, Andrew D. Cherniack7, Gordon Robertson10, Christopher C. Benz9, Chris Sander2, Peter W. Laird11, Katherine A. Hoadley4, Tari A. King2, Rehan Akbani, J. Todd Auman4, Miruna Balasundaram, Saianand Balu, Thomas Barr, Stephen C. Benz, Mario Berrios, Rameen Beroukhim, Tom Bodenheimer, Lori Boice, Moiz S. Bootwalla, Jay Bowen, Denise Brooks, Lynda Chin, Juok Cho, Sudha Chudamani, Tanja M. Davidsen, John A. Demchok, Jennifer B. Dennison, Li Ding, Ina Felau, Martin L. Ferguson, Scott Frazer, Stacey Gabriel, Jianjiong Gao, Julie M. Gastier-Foster, Nils Gehlenborg, Mark Gerken, Gad Getz, William J. Gibson, D. Neil Hayes, David I. Heiman, Andrea Holbrook, Robert A. Holt, Alan P. Hoyle, Hai Hu, Mei Huang, Carolyn M. Hutter, E. Shelley Hwang, Stuart R. Jefferys, Steven J.M. Jones, Zhenlin Ju, Jaegil Kim, Phillip H. Lai, Michael S. Lawrence, Kristen M. Leraas, Tara M. Lichtenberg, Pei Lin, Shiyun Ling, Jia Liu, Wen-Bin Liu, Laxmi Lolla, Yiling Lu, Yussanne Ma, Dennis T. Maglinte, Elaine R. Mardis, Jeffrey R. Marks, Marco A. Marra, Cynthia McAllister, Shaowu Meng, Matthew Meyerson, Richard A. Moore, Lisle E. Mose, Andrew J. Mungall, Bradley A. Murray, Rashi Naresh, Michael S. Noble, Olufunmilayo I. Olopade, Joel S. Parker, Todd Pihl, Gordon Saksena, Steven E. Schumacher, Kenna R. Mills Shaw, Nilsa C. Ramirez, W. Kimryn Rathmell, Jeffrey Roach, A. Gordon Robertson19, Jacqueline E. Schein, Nikolaus Schultz, Margi Sheth, Yan Shi, Juliann Shih, Carl Simon Shelley, Craig D. Shriver, Janae V. Simons, Heidi J. Sofia, Matthew G. Soloway, Carrie Sougnez, Charlie Sun, Roy Tarnuzzer, Daniel Guimarães Tiezzi, David Van Den Berg, Doug Voet, Yunhu Wan, Zhining Wang, John N. Weinstein, Daniel J. Weisenberger, Rick K. Wilson, Lisa Wise, Maciej Wiznerowicz, Junyuan Wu, Ye Wu, Liming Yang, Travis I. Zack, Jean C. Zenklusen, Jiashan Zhang, Erik Zmuda, Charles M. Perou4 
08 Oct 2015-Cell
TL;DR: This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options, suggesting differential modulation of ER activity in I LC and IDC.

1,414 citations


Authors

Showing all 53431 results

NameH-indexPapersCitations
Bert Vogelstein247757332094
George M. Whitesides2401739269833
Hongjie Dai197570182579
Robert M. Califf1961561167961
Frank E. Speizer193636135891
Yusuke Nakamura1792076160313
David L. Kaplan1771944146082
Marc G. Caron17367499802
George M. Church172900120514
Steven P. Gygi172704129173
Lily Yeh Jan16246773655
Tobin J. Marks1591621111604
David W. Bates1591239116698
Alfred L. Goldberg15647488296
Charles M. Perou156573202951
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023203
2022769
20217,363
20207,015
20196,309
20185,651