Project Details
Description
Project Summary
The spatial organization of the genome in the nucleus plays an important role in the transcriptional control of
genes. Currently, Hi-C is the most widely used high-throughput technique that probes the genome-wide spatial
organization of chromatin. However, Hi-C experiments involve multiple complex experimental steps,
introducing various sources of biases. Many data-analytical challenges still must be overcome to reach
reliable and reproducible biological interpretations of the data. The small sample size of each individual study
further limits the power and reliability of data analyses. When replicate samples are available, reproducibility
across replicate samples informs us about the fidelity of the identification, and potentially it can be used to
detect reproducible signals that are too modest to be detected reliably in individual samples. Even for samples
from different cells, information may be borrowed through joint analyses to improve the identification of both
topologically associated domains (TADs) and regions with different structures. This project proposes to
develop a suite of new statistical methods that use the reproducibility information provided by replicate
samples to select reliable identifications and to improve the accuracy of peak calling and TAD calling.
Furthermore, it proposes a joint analysis framework to identify condition-specific architectural differences
across different cells. Aim 1 will develop statistical methods to evaluate the reproducibility of identified
chromatin loops and to select reproducible identifications. The reproducibility-based selection criterion
complements the usual measure of significance on a single sample, but has the benefit of being comparable
across data sets, protocols and different measures of significance. Aim 2 will develop robust, joint multi-sample
peak calling and TAD calling methods. These methods will allow one to synergize information across samples
and properly take account of variations across replicates, ultimately improving the power of the analysis and
reducing false positives. Aim 3 will develop statistical methods for detecting TAD and other architectural
differences between different cell types, cellular conditions, or disease status. Included in each proposed Aim
are rigorous evaluations of the output of these methods utilizing orthogonal epigenomic data and experimental
tests of hypotheses derived from the results of the analytical methods. These methods will enable users to
generate reliable and robust scientific interpretation, and ultimately advance the understanding of nuclear
organization and its role in gene expression and cellular function.
Status | Finished |
---|---|
Effective start/end date | 9/1/13 → 3/31/24 |
Funding
- National Institute of General Medical Sciences: $306,631.00
- National Institute of General Medical Sciences: $265,756.00
- National Institute of General Medical Sciences: $314,489.00
- National Institute of General Medical Sciences: $307,088.00
- National Institute of General Medical Sciences: $271,736.00
- National Institute of General Medical Sciences: $252,301.00
- National Institute of General Medical Sciences: $314,328.00
- National Institute of General Medical Sciences: $259,029.00
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