The goal of the Encyclopedia of DNA Elements (ENCODE) project is to catalog all functional elements in the human genome through the integration and analysis of high-throughput data. We propose to continue the ENCODE Data Analysis Center (EDAC, DAC) which will provide support and leadership in analyzing and integrating data from the ENCODE project as well as work closely with other ENCODE groups including the Data Coordination Center. Our proposed DAC team (Zhiping Weng, Mark Gerstein, Manolis Kellis, Roderic Guigo, Rafael Irizarry, X. Shirley Liu, Anshul Kundaje, and William Noble) has expertise across a wide range of fields including transcriptional regulation, epigenetics, evolution, genomics and proteomics, regulatory RNA, biophysics, and computational biology, where they are the leaders in machine learning, statistical genetics, networks, and gene annotation. These investigators also have a history of successfully working collaboratively in large consortia, particularly with other ENCODE groups. Their publication records demonstrate their synergistic approach to producing high-impact science and useful resources that benefit the broader biomedical communities. The proposed DAC will pursue the following four aims:
Aim 1. Analyze and integrate data and metadata from a broad range of functional genomics projects;
Aim 2. Serve as an informatics resource by supporting the activities of the ENCODE Analysis Working Group;
Aim 3. Create high-quality Encyclopedias of DNA elements in the human and mouse genomes;
Aim 4. Assess quality and utility of the ENCODE data and provide feedback to NHGRI and the Consortium.

Public Health Relevance

The goal of the Encyclopedia of DNA Elements (ENCODE) project is a highly collaborative effort aiming to develop a comprehensive list of functional elements in the human genome. This proposal creates a data analysis center to provide support and computational prowess for this effort in collaboration with other ENCODE groups. This comprehensive list will be of use to the wider research community and will aid in understanding human biology particularly in the context of disease, ultimately leading to improvements in human health.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24HG009446-02
Application #
9420662
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Gilchrist, Daniel A
Project Start
2017-02-01
Project End
2021-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655
Yan, Koon-Kiu; Lou, Shaoke; Gerstein, Mark (2017) MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions. PLoS Comput Biol 13:e1005647
Teng, Mingxiang; Irizarry, Rafael A (2017) Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data. Genome Res 27:1930-1938
Yan, Koon-Kiu; Yardimci, Galip G├╝rkan; Yan, Chengfei et al. (2017) HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps. Bioinformatics 33:2199-2201