Genomics stands at a remarkable moment of scientific opportunity and technological ferment. DMA sequence information is becoming an increasingly powerful for investigating biological question, and new technologies promise to decrease dramatically the cost of DMA sequencing and thereby further expand its reach. At the same time, the scientific and technological path forward has many uncertainties and obstacles. For these reasons, NHGRI seeks to support genome centers with the ability to provide both scientific and technological leadership through this exciting period of transition. This proposal has three specific aims:
Aim 1. Current technology. We propose to operate a state-of-the-art genome center using current sequencing technology, continuing our 15-year track record of high throughput, low cost and flexibility. Our annual throughput will exceed 61 billion bases, with costs decreasing to $0.29/kQ20 base by the end of Year 4. The capacity will have the potential to be allocated flexibly across shotgun sequencing, targeted sequencing and finishing/improvement.
Aim 2. New technology. In parallel, we aim to completely replace the current generation with new, disruptive technologies, with the goal of dramatically decreasing cost and increasing output. During the first year, we will operate two new sequencing platforms (454 and Solexa) at sufficient scale to understand their true performance, cost and utility and to produce valuable data. Based on the results, we aim to scale-up one or both platforms as soon as possible. This work requires more than simply implementing new instruments at production scale, although this is a substantial challenge. The greater challenge is that the two platforms are not currently suited to NHGRI's key genomic applications ? namely, genome assembly and directed re-sequencing. We will develop the full set of laboratory and computational tools required to adapt the technologies to these key applications. Our goal will be to achieve a cost reduction of >30-fold in these key applications by the end of the grant period.
Aim 3. Scientific leadership. We will continue to serve as an intellectual resource for the scientific community, by pioneering new approaches for using DMA sequence to solve important biomedical problems and by working with the community to rapidly disseminate ideas, methods and data.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54HG003067-05
Application #
7318358
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
$48,352,630
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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
Huang, Kuan-Lin; Mashl, R Jay; Wu, Yige et al. (2018) Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 173:355-370.e14
Gigante, Eduardo D; Long, Alyssa Bushey; Ben-Ami, Johanna et al. (2018) Hypomorphic Smo mutant with inefficient ciliary enrichment disrupts the highest level of vertebrate Hedgehog response. Dev Biol 437:152-162
Emdin, Connor A; Khera, Amit V; Klarin, Derek et al. (2018) Phenotypic Consequences of a Genetic Predisposition to Enhanced Nitric Oxide Signaling. Circulation 137:222-232
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
Natarajan, Pradeep; Peloso, Gina M; Zekavat, Seyedeh Maryam et al. (2018) Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat Commun 9:3391
Ganna, Andrea; Satterstrom, F Kyle; Zekavat, Seyedeh M et al. (2018) Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. Am J Hum Genet 102:1204-1211
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 349 publications