Institution
University of Utah
Education•Salt 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.
Topics: Population, Poison control, Health care, Cancer, Transplantation
Papers published on a yearly basis
Papers
More filters
••
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
••
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
••
Stockholm University1, Lawrence Berkeley National Laboratory2, University of California, Berkeley3, Pierre-and-Marie-Curie University4, Colorado College5, University of Colorado Boulder6, University of Utah7, Space Telescope Science Institute8, University of Tokyo9, University of Victoria10, Carnegie Institution for Science11, INAF12, University of Oxford13, Las Cumbres Observatory Global Telescope Network14, University of California, Santa Barbara15, Drexel University16, University of Bonn17, Japan Society for the Promotion of Science18, University of Barcelona19, Texas A&M University20, University of Pittsburgh21
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
••
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
••
University of Lausanne1, Memorial Sloan Kettering Cancer Center2, Rutgers University3, University of North Carolina at Chapel Hill4, Harvard University5, University of Southern California6, Broad Institute7, Washington University in St. Louis8, Buck Institute for Research on Aging9, University of British Columbia10, Van Andel Institute11, The Chinese University of Hong Kong12, University of Utah13, Stanford University14, University of California, San Francisco15, United States Department of Veterans Affairs16, University of Pittsburgh17, University of Texas MD Anderson Cancer Center18, BC Cancer Agency19
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
Name | H-index | Papers | Citations |
---|---|---|---|
Bert Vogelstein | 247 | 757 | 332094 |
George M. Whitesides | 240 | 1739 | 269833 |
Hongjie Dai | 197 | 570 | 182579 |
Robert M. Califf | 196 | 1561 | 167961 |
Frank E. Speizer | 193 | 636 | 135891 |
Yusuke Nakamura | 179 | 2076 | 160313 |
David L. Kaplan | 177 | 1944 | 146082 |
Marc G. Caron | 173 | 674 | 99802 |
George M. Church | 172 | 900 | 120514 |
Steven P. Gygi | 172 | 704 | 129173 |
Lily Yeh Jan | 162 | 467 | 73655 |
Tobin J. Marks | 159 | 1621 | 111604 |
David W. Bates | 159 | 1239 | 116698 |
Alfred L. Goldberg | 156 | 474 | 88296 |
Charles M. Perou | 156 | 573 | 202951 |