Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data

Abstract

Over the past few decades we have witnessed great efforts to understand the cellular function at the cytoplasm level. Nowadays there is a growing interest in understanding the relationship between function and structure at the nuclear, chromosomal and sub-chromosomal levels. Data on chromosomal interactions that are now becoming available in unprecedented resolution and scale open the way to address this challenge. Consequently, there is a growing need for new methods and tools that will transform these data into knowledge and insights. Here, we have developed all the steps required for the analysis of chromosomal interaction data (Hi-C data). The result is a methodology which combines a wavelet change point with the Bayes factor for useful correction, segmentation and comparison of Hi-C data. We further developed chromoR, an R package that implements the methods presented here. The chromoR package provides researchers with a means to analyse chromosomal interaction data using statistical bioinformatics, offering a new and comprehensive solution to this task.

GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data

“…GOTHiC’s approach is conceptually and computationally simpler than existing methods, which require the identification and separate modeling of individual biases [8,9], an iterative correction of biases [10,11], or variance stabilisation [12]. It yields similar rankings to previous methods, with comparable or even slightly improved bias removal and reproducibility between replicates.…”

“…ChromoR , also only uses the information encaptured in observed read counts. For normalisation of Hi-C data it uses Haar-Fisz Transformation to decompose the Poisson distributed read counts into Gaussian coefficients that are subsequently de-noised by wavelet shrinkage methods [12]. …”

“…First, the assumptions behind the methods are untested against experimental control data and so their success in eliminating biases is unclear. Second, the software implementation of some of these methods have either several dependencies that make them technically demanding to install and operate [8,10], require extensive pre-processing [10,11], or cannot be applied at higher resolution [12]. …”

Genome-wide mapping and analysis of chromosome architecture

“…In practice, both explicit and implicit approaches have been used to account for biases in Hi-C data; therefore, it would be helpful to conduct a comprehensive comparison between the two approaches. To date, only a partial comparison has been made, which highlighted the differences in reproducibility of cis and trans interaction frequencies at low resolution 84 . A novel computational framework that combines the strengths of the two approaches may enable more accurate bias removal and higher computational efficiency.…”

GITAR: an Open Source Tool for Analysis and Visualization of Hi-C Data

“…Mostly during the past five years, several software applications have been developed to analyze and visualize genomic interaction data, with different characteristics and outputs. If we consider Hi-C, available analysis software applications are chromoR [6], HiCdat [7], HiCNorm [8], Hi-Corrector [9], Hi-C Pipeline [10], HiC-Pro [11], HiCUP [12], HiFive [13], HIPPIE [14], HiTC [15], HOMER [16], ICE [17]. However, besides correcting biases and generating contact maps, most of them do not provide the entire pipeline to pre-process the raw data (downloadable files) or to compute topological domain coordinates.…”