Genome sequence, basic to biomedical research, is efficaciously produced by whole-genome shotgun (WGS) sequencing. Although WGS sequencing is a major NIH activity, we lack answers to fundamental questions about sequencing strategy and assembly of WGS data. Our work and the community's have focused on assembly of particular data sets and development of assembly algorithms. This grant focuses on mathematical underpinnings and rigorous analysis of genome sequencing and assembly, to improve our assembly tools and approaches. We will develop general methodology for optimally choosing specific sequencing strategies for new and varied organisms, fully exploiting data from emerging technologies. So that assembly is also optimal, we will develop algorithms that exploit the data's exact information content, retaining intrinsic ambiguity, and allowing assembly of genomes beyond current capabilities. We will develop strict internal consistency tests, guaranteeing accuracy and completeness of assembly units. A new assembly quality markup tool will label assembly regions from finished to inconsistent, by their inferred accuracy. This will guide finishing work (improving efficiency) and clearly describe reliability of particular assembly regions to end-users. In short, the work will produce better quality genome sequence at lower cost, marked to show reliability, thereby increasing utility for downstream analysis and laboratory experimentation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG003474-03
Application #
7270377
Study Section
Special Emphasis Panel (ZRG1-BDMA (01))
Program Officer
Felsenfeld, Adam
Project Start
2005-09-26
Project End
2009-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
3
Fiscal Year
2007
Total Cost
$751,656
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
Weisenfeld, Neil I; Yin, Shuangye; Sharpe, Ted et al. (2014) Comprehensive variation discovery in single human genomes. Nat Genet 46:1350-5
Ross, Michael G; Russ, Carsten; Costello, Maura et al. (2013) Characterizing and measuring bias in sequence data. Genome Biol 14:R51
Goldberg, Jonathan M; Griggs, Allison D; Smith, Janet L et al. (2013) Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily. Bioinformatics 29:2387-94
Amemiya, Chris T; Alföldi, Jessica; Lee, Alison P et al. (2013) The African coelacanth genome provides insights into tetrapod evolution. Nature 496:311-6
Ribeiro, Filipe J; Przybylski, Dariusz; Yin, Shuangye et al. (2012) Finished bacterial genomes from shotgun sequence data. Genome Res 22:2270-7
Williams, Louise J S; Tabbaa, Diana G; Li, Na et al. (2012) Paired-end sequencing of Fosmid libraries by Illumina. Genome Res 22:2241-9
Calvo, Sarah E; Compton, Alison G; Hershman, Steven G et al. (2012) Molecular diagnosis of infantile mitochondrial disease with targeted next-generation sequencing. Sci Transl Med 4:118ra10
Jones, Felicity C; Grabherr, Manfred G; Chan, Yingguang Frank et al. (2012) The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484:55-61
Aird, Daniel; Ross, Michael G; Chen, Wei-Sheng et al. (2011) Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol 12:R18
Lindblad-Toh, Kerstin; Garber, Manuel; Zuk, Or et al. (2011) A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478:476-82

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