FIND: difFerential chromatin INteractions Detection using a spatial Poisson process

Abstract

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.

4DNvestigator: Time Series Genomic Data Analysis Toolbox

“…HiCRep, HiC-spector), and Differential Chromatin Interactions (DCI) methods (e.g. SELFISH, HiCCompare, FIND, diffHic) [11,12,13,14,15,16]. Hi-C reproducibility methods are useful for assessing the equality of Hi-C matrices genome-wide, and detecting technical bias between samples.…”

FreeHi-C: high fidelity Hi-C data simulation for benchmarking and data augmentation

“…We considered the detection of differential chromatin interactions (DCIs) as an example and compared the state-of-the-art differential interaction identification methods, diffHic 9 and multiHiCcompare 16 , across a series of sequencing depths. We singled out these two approaches because alternatives such as FIND 13 and HiCcompare 14 have either established lack of false discovery rate control and excessively long run times 14,19 or constituted a specialized version of multiHiCcompare to exclusively handle one replicate per condition. We compared diffHic and multiHiCcompare with respect to false discovery rate (FDR) control and power through both FreeHi-C simulation and downsampling.…”

“…Recent maturation of chromosome conformation capture (3C) 1 and Hi-C sequencing technologies 2,3 led to high-throughput profiling of three-dimensional chromatin architecture and revealed transformative insights on long-range gene regulation [4][5][6] . Alongside the technological breakthroughs, a growing number of methodologies and algorithms [7][8][9][10][11][12][13][14][15][16] emerged for the analysis of Hi-C and other 3C-derived data types. These methods are developed and benchmarked on disparate biological and simulated or computationally-constructed datasets that are often customized for the methods under the study.…”

“…Benchmarking and evaluation of Hi-C methods have relied on either conducting distancestratified permutations of the contact matrices 15 to nullify the underlying biological structure or simulating contact matrices directly by capturing only the most general spatial structures for specific analysis purposes 9,11,13,14,18,19 . Additionally, pooling biological replicates and then randomly partitioning to generate pseudo-replicates and downsampling are also common approaches to study similarity metrics or evaluate the sequencing depth effect 12,15,18,19 .…”

FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

“…A central task in Hi-C matrix analysis is the comparison of multiple datasets. A number of tools have been developed to identify and quantify differences between Hi-C matrices (Heinz et al 2010;Stansfield et al 2018;Lun and Smyth 2015;Ardakany et al 2019;Djekidel et al 2018). FAN-C focusses on the representation and visualisation of differences, and can therefore function as a direct extension to existing approaches.…”