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.


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One thought on “FIND: difFerential chromatin INteractions Detection using a spatial Poisson process

  • Sushmita

    The goal of this paper is to develop a computational method to identify differential interactions from high-resolution Hi-C data while accounting for the spatial/distance-dependence nature of the data. The idea is to use some kind of a smoothed value of the mean value of a region pair, (i,j) (mean estimated from different replicates) from the k-nearest neighbors of the region pair. The k-nearest neighbors are themselves obtained based on the spatial distance (e.g. the radius of W around the pair i,j) and the counts for its neighbors. The method works for two conditions and requires multiple replicates for each condition.