With the reference human genome sequence now completed, the next wave of large-scale sequencing will be aimed at genomes that can further inform the human sequence or otherwise provide significant value for biological discovery. These sequences must be of high quality, yet must be generated efficiently and at a substantially lower cost. In this proposal, we describe technical developments that will allow us to produce longer sequence read lengths, decrease sequencing costs, improve physical map construction, streamline genome assembly, and automate sequence finishing. To support these advances, we will develop enhanced informatics tools and infrastructure to effectively integrate and improve management of the entire range of our laboratory processes. On the basis of these technical developments, we will produce genome sequence data at a rate of 3.3M reads/month in Year 1, scaling moderately to 3.8M reads/month in Year 3. Over the same time period, we aim to increase average read length by at least 300 bp, and to cut our per-read cost from $1.35 to $0.75 or less. Refined methods and tools to more efficiently finish genome sequences to high quality and continuity standards, as well as methods and tools for detection and annotation of genes and other elements encoded within those genomes, will further enhance the output data from our Center. Coupled with advances in strategy, these improvements will substantially improve the efficiency and the economics of genome sequencing, making it much more feasible to consider the analysis of additional human and animal genomes. ? ?

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
3U54HG003079-03S1
Application #
7293357
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Felsenfeld, Adam
Project Start
2003-11-10
Project End
2006-11-30
Budget Start
2005-11-01
Budget End
2006-11-30
Support Year
3
Fiscal Year
2006
Total Cost
$3,367,319
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

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