HiCmapTools: a tool to access HiC contact maps

Background

With the development of HiC technology, more and more HiC sequencing data have been produced. Although there are dozens of packages that can turn sequencing data into contact maps, there is no appropriate tool to query contact maps in order to extract biological information from HiC datasets.

Results

We present HiCmapTools, a tool for biologists to efficiently calculate and analyze HiC maps. The complete program provides multi-query modes and analysis tools. We have validated its utility on two real biological questions: TAD loop and TAD intra-density.

Conclusions

HiCmapTools supports seven access options so that biologists can quantify contact frequency of the interest sites. The tool has been implemented in C++ and R and is freely available at https://github.com/changlabtw/hicmaptools and documented at https://hicmaptools.readthedocs.io/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04589-y.

A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features

Technological advances have lead to the creation of large epigenetic datasets, including information about DNA binding proteins and DNA spatial structure. Hi-C experiments have revealed that chromosomes are subdivided into sets of self-interacting domains called Topologically Associating Domains (TADs). TADs are involved in the regulation of gene expression activity, but the mechanisms of their formation are not yet fully understood. Here, we focus on machine learning methods to characterize DNA folding patterns in Drosophila based on chromatin marks across three cell lines. We present linear regression models with four types of regularization, gradient boosting, and recurrent neural networks (RNN) as tools to study chromatin folding characteristics associated with TADs given epigenetic chromatin immunoprecipitation data. The bidirectional long short-term memory RNN architecture produced the best prediction scores and identified biologically relevant features. Distribution of protein Chriz (Chromator) and histone modification H3K4me3 were selected as the most informative features for the prediction of TADs characteristics. This approach may be adapted to any similar biological dataset of chromatin features across various cell lines and species. The code for the implemented pipeline, Hi-ChiP-ML, is publicly available: https://github.com/MichalRozenwald/Hi-ChIP-ML.