DNA methylation plays a critical role in regulating lineage specification and restriction of potency during mammalian development;aberrant patterns of DNA methylation are generally observed in cancers. Second-generation sequencing of bisulfite treated DNA is enabling DNA methylation to be examined in greater detail, and recently demonstrated array-based capture technique allow ultra-deep bisulfite sequencing in selected genomic regions. This project develops algorithmic and statistical methods required to formulate and test specific hypotheses about DNA methylation based on data from these novel experimental technologies. A family of statistical models will be designed to characterize features of DNA methylation in a cell or sample, and algorithms will be designed for associated computational tasks of model fitting, probability calculations and feature identification. The methods will be validated through application to novel, ultra-deep bisulfite sequencing data;specific hypotheses about the regulation of DNA methylation and how methylation regulates gene expression will be tested simultaneously. Specific methylation datasets related to development and cancer will be produced. Computational methods will be developed for identifying clonal features of methylation profiles in cells, for predicting developmental relationships between cells, and for resolving information about the complexity and histology of tumor samples. Efficient and robust implementations of the methods will be developed and released for public use. The proposed research will provide increased analytical capability to complement emerging experimental technology for investigating DNA methylation. This will enable researchers, particularly those studying human development and cancers, to ask and answer more precise questions about the functions of DNA methylation. Moreover, this technology will assist in identifying the most effective methylation-based markers for clinical outcomes associated with cancers.

Public Health Relevance

The proposed research will produce analytic technology to complement emerging experimental technology in analyzing DNA methylation patterns. DNA methylation is involved in regulating human development, and aberrant methylation patterns are a general characteristic of cancer genomes. By providing increased capacity for analyzing methylation data, this project will assist researchers elucidate the role and mechanisms of DNA methylation in cancer and development.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG005238-03
Application #
8292202
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Pazin, Michael J
Project Start
2010-07-01
Project End
2014-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
3
Fiscal Year
2012
Total Cost
$389,864
Indirect Cost
$110,180
Name
University of Southern California
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Daley, Timothy; Smith, Andrew D (2014) Modeling genome coverage in single-cell sequencing. Bioinformatics 30:3159-65
Dolzhenko, Egor; Smith, Andrew D (2014) Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinformatics 15:215
Daley, Timothy; Smith, Andrew D (2013) Predicting the molecular complexity of sequencing libraries. Nat Methods 10:325-7
Qu, Jianghan; Zhou, Meng; Song, Qiang et al. (2013) MLML: consistent simultaneous estimates of DNA methylation and hydroxymethylation. Bioinformatics 29:2645-6
Hong, Elizabeth E; Okitsu, Cindy Y; Smith, Andrew D et al. (2013) Regionally specific and genome-wide analyses conclusively demonstrate the absence of CpG methylation in human mitochondrial DNA. Mol Cell Biol 33:2683-90
Fang, Fang; Hodges, Emily; Molaro, Antoine et al. (2012) Genomic landscape of human allele-specific DNA methylation. Proc Natl Acad Sci U S A 109:7332-7
Molaro, Antoine; Hodges, Emily; Fang, Fang et al. (2011) Sperm methylation profiles reveal features of epigenetic inheritance and evolution in primates. Cell 146:1029-41