The advent of high throughput next generation sequencing (NGS) technologies have revolutionized the fields of genetics and genomics by allowing rapid and inexpensive sequencing of billions of bases. Among the NGS applications, ChIP-seq (chromatin immunoprecipitation followed by NGS) is perhaps the most successful to date. ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Both of these play crucial roles in programming of gene expression in a cell specific manner;therefore their genome-wide mapping can significantly advance our ability to understand and diagnose human diseases. Although basic analysis tools for ChIP-seq data are rapidly increasing, all of the available methods share one or more of the following shortcomings. First, they focus on analyzing one ChIP- seq sample at a time. As ChIP-seq is becoming commonly utilized in epigenome mapping to understand phenotypic variation, the demand for methods that can handle multiple samples efficiently is rapidly rising. Second, they only utilize sequence reads that align to unique locations on the reference genome. This hinders the study of highly repetitive regions of genomes by ChIP-seq. Third, commonly used designs for ChIP-seq experiments employ one matching control sample per each ChIP-seq sample. This limits the genome coverage of control experiments and impacts the detection of enrichment in ChIP samples. It also significantly contributes to increase in sequencing costs for large-scale ChIP-seq studies. The objective of this project is to address these challenges of ChIP-seq analysis in three specific aims: (1) Statistical methods for inference from multiple samples;(2) Probabilistic models for utilizing reads that map to multiple locations (multi-reads) in the genome;(3) Development and evaluation of in silico pooling designs for control experiments. The projects will be accomplished through a combination of methodological development, simulation, computational analysis, and experimental validation. Methods will be developed and evaluated using datasets from the ENCODE, modENCODE, and the RoadMap Epigenomics consortiums as well as novel datasets from collaborators. Statistical resources generated from the project, which will be disseminated in publicly available software, will provide essential tools for the efficient design and analysis of ChIP-seq experiments.

Public Health Relevance

The proposed research is relevant to public health because capturing genome- wide binding of transcription factors and epigenomic information by ChIP-seq technology is invaluable for comprehensively understanding development, differentiation, and disease. ChIP-seq experiments present unprecedented challenges in statistical analysis. We will develop statistical methods and tools for challenging aspects of ChIP-seq analysis and disseminate results and software to the research community.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG003747-06
Application #
8605900
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Pazin, Michael J
Project Start
2005-07-01
Project End
2015-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
6
Fiscal Year
2014
Total Cost
$275,047
Indirect Cost
$90,308
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Li, Sisi; Papale, Ligia A; Zhang, Qi et al. (2016) Genome-wide alterations in hippocampal 5-hydroxymethylcytosine links plasticity genes to acute stress. Neurobiol Dis 86:99-108
Papale, Ligia A; Li, Sisi; Madrid, Andy et al. (2016) Sex-specific hippocampal 5-hydroxymethylcytosine is disrupted in response to acute stress. Neurobiol Dis 96:54-66
Yao, Chen; Chen, Brian H; Joehanes, Roby et al. (2015) Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes. Circulation 131:536-49
Zuo, Chandler; Shin, Sunyoung; Keleş, Sündüz (2015) atSNP: transcription factor binding affinity testing for regulatory SNP detection. Bioinformatics 31:3353-5
Papale, Ligia A; Zhang, Qi; Li, Sisi et al. (2015) Genome-wide disruption of 5-hydroxymethylcytosine in a mouse model of autism. Hum Mol Genet 24:7121-31
Lopez-Anido, Camila; Sun, Guannan; Koenning, Matthias et al. (2015) Differential Sox10 genomic occupancy in myelinating glia. Glia :
Hewitt, Kyle J; Kim, Duk Hyoung; Devadas, Prithvia et al. (2015) Hematopoietic Signaling Mechanism Revealed from a Stem/Progenitor Cell Cistrome. Mol Cell 59:62-74
Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan et al. (2015) Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data. Cancer Inform 13:123-31
Zeng, Xin; Li, Bo; Welch, Rene et al. (2015) Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping. PLoS Comput Biol 11:e1004491
Zhang, Qi; Keleş, Sündüz (2014) CNV-guided multi-read allocation for ChIP-seq. Bioinformatics 30:2860-7

Showing the most recent 10 out of 40 publications