3C


FIND: difFerential chromatin INteractions Detection using a spatial Poisson process 1

Our next meeting will be at 2pm on Mar 26th, in room 4160 of the Discovery building. Our Selected paper is FIND: difFerential chromatin INteractions Detection using a spatial Poisson process.
The abstract is as follows.

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

Our next meeting will be at 11:00 on September 12th, in room 4160 of the Discovery building. Our Selected paper is GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs.
The abstract is as follows.

The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of the data and the resulting idiosyncratic properties of experimental noise. We introduce a multi-scale concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome capture experiments. We denoise the contact maps using random walks on the contact map graph, and integrate concordance at multiple scales of smoothing. We use simulated datasets to benchmark GenomeDISCO’s sensitivity to different types of noise typically affecting chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. Software implementing GenomeDISCO is available at http://github.com/kundajelab/genomedisco.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

Our next meeting will be at 3:00 on January 27th, in room 4160 of the Discovery building. Our Selected paper is Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters.
The abstract is as follows.

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases..

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.