Whereas only three years ago we concerned ourselves mainly with production capacity and costs, the landscape of genome sequencing and analysis has changed to the point that we now find our attention focused on the application of our technology platform and our expertise to large-scale studies of the diseasecausing elements of the human genome. This evolution is the result of two major factors. The first is a highquality reference sequence of the human genome; in recent years, the quality and value of both the sequence and the attendant annotation have been greatly improved as a result of sequencing the genomes of other organisms. The second factor is the emergence of new technology that provides sufficient low-cost sequencing capacity to facilitate the interrogation of many individual human genomes in search of the sequence variants that underlie disease susceptibility and pathogenesis. In this proposal, we describe our extant genome technology platform, our extensive experience in sequencing and analyzing genomes, and we discuss how these resources may be brought to bear as a component of the NHGRI large-scale sequencing program. Additionally, we describe the new Tumor Sequencing Project and five """"""""center- initiated"""""""" projects that further illustrate how our technology platform will impact the fields of genome biology and genomic medicine over the next several years.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54HG003079-05
Application #
7318354
Study Section
Special Emphasis Panel (ZHG1-HGR-P (A1))
Program Officer
Felsenfeld, Adam
Project Start
2003-11-10
Project End
2010-10-31
Budget Start
2007-12-01
Budget End
2008-10-31
Support Year
5
Fiscal Year
2008
Total Cost
$34,501,448
Indirect Cost
Name
Washington University
Department
Genetics
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Liu, Yang; Sethi, Nilay S; Hinoue, Toshinori et al. (2018) Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. Cancer Cell 33:721-735.e8
Raghavan, Neha S; Brickman, Adam M; Andrews, Howard et al. (2018) Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease. Ann Clin Transl Neurol 5:832-842
Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3
Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Martin, Ivonne; Djuardi, Yenny; Sartono, Erliyani et al. (2018) Dynamic changes in human-gut microbiome in relation to a placebo-controlled anthelminthic trial in Indonesia. PLoS Negl Trop Dis 12:e0006620
Ellrott, Kyle; Bailey, Matthew H; Saksena, Gordon et al. (2018) Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst 6:271-281.e7
Campbell, Joshua D; Yau, Christina; Bowlby, Reanne et al. (2018) Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell Rep 23:194-212.e6
Gao, Qingsong; Liang, Wen-Wei; Foltz, Steven M et al. (2018) Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep 23:227-238.e3
Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14

Showing the most recent 10 out of 234 publications