Abstract

We propose to develop statistical and computational methods for interpreting data on gene regulation in a variety of cell types from the ENCODE Project. Our work will focus on (i) developing methods for integrating diverse data types in many tissues to infer chromatin architecture; (ii) inferring the sequence determinants and regulatory consequences of differences in chromatin across cell types; and (iii) empirical tests of our predictions using both natural genetic variation and experimental approaches.

Public Health Relevance

The purpose of this project is to develop new statistical and computational tools for analyzing and interpreting data from the ENCODE project. These data relate to the controls of gene regulation in a wide variety of cell types.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01HG007036-04S1
Application #
9090937
Study Section
Special Emphasis Panel (ZHG1-HGR-M (M2))
Program Officer
Gilchrist, Daniel A
Project Start
2012-09-17
Project End
2016-06-30
Budget Start
2015-07-01
Budget End
2016-06-30
Support Year
4
Fiscal Year
2015
Total Cost
$278,423
Indirect Cost
$104,951

  Institution

Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304

  Publications

Banovich, Nicholas E; Li, Yang I; Raj, Anil et al. (2018) Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res 28:122-131
Li, Yang I; Knowles, David A; Humphrey, Jack et al. (2018) Annotation-free quantification of RNA splicing using LeafCutter. Nat Genet 50:151-158
Sharon, Eilon; Sibener, Leah V; Battle, Alexis et al. (2016) Genetic variation in MHC proteins is associated with T cell receptor expression biases. Nat Genet 48:995-1002
Raj, Anil; Wang, Sidney H; Shim, Heejung et al. (2016) Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. Elife 5:
Li, Yang I; van de Geijn, Bryce; Raj, Anil et al. (2016) RNA splicing is a primary link between genetic variation and disease. Science 352:600-4
Raj, Anil; Shim, Heejung; Gilad, Yoav et al. (2015) msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding. PLoS One 10:e0138030
Battle, Alexis; Khan, Zia; Wang, Sidney H et al. (2015) Genomic variation. Impact of regulatory variation from RNA to protein. Science 347:664-7
van de Geijn, Bryce; McVicker, Graham; Gilad, Yoav et al. (2015) WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat Methods 12:1061-3
Raj, Anil; Stephens, Matthew; Pritchard, Jonathan K (2014) fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197:573-89
Nalabothula, Narasimharao; McVicker, Graham; Maiorano, John et al. (2014) The chromatin architectural proteins HMGD1 and H1 bind reciprocally and have opposite effects on chromatin structure and gene regulation. BMC Genomics 15:92

Showing the most recent 10 out of 12 publications