Abstract
This study aims to understand through statistical learning the basic biophysical mechanisms behind three-dimensional folding of epigenomes. The 3DEpiLoop algorithm predicts three-dimensional chromatin looping interactions within topologically associating domains (TADs) from one-dimensional epigenomics and transcription factor profiles using the statistical learning. The predictions obtained by 3DEpiLoop are highly consistent with the reported experimental interactions. The complex signatures of epigenomic and transcription factors within the physically interacting chromatin regions (anchors) are similar across all genomic scales: genomic domains, chromosomal territories, cell types, and different individuals. We report the most important epigenetic and transcription factor features used for interaction identification either shared, or unique for each of sixteen (16) cell lines. The analysis shows that CTCF interaction anchors are enriched by transcription factors yet deficient in histone modifications, while the opposite is true in the case of RNAP II mediated interactions. The code is available at the repository https://bitbucket.org/4dnucleome/3depiloop.
Highlights
Between interacting genomic segments, or following interactions established Topologically Associating Domains (TADs)
Cell line Interactions K562: CTCF ChIA-PET K562: RNA polymerase II (RNAP II) ChIA-PET IMR90: In situ Hi-C loops algorithm which uses tensor decomposition to find the genomic loci with similar epigenomic patterns across cell types, later assessing the strength of the interactions using ChIP-seq peak height at both analysed anchors
Fewer false positives were observed when dealing with CTCF ChIA-PET, and in situ Hi-C loops, though the false positive rate was higher with RNAP II ChIA-PET interactions. (Supplementary Methods sections 2.4, 3.3, and 4.5)
Summary
Between interacting genomic segments (anchors), or following interactions established Topologically Associating Domains (TADs) This is still insufficient to provide accurate statistical predictions due to the large number of possible formed pairs. Each type of chromatin state (controlled epigenetically) is linked with the unique repertoire of its own biochemical interactions, and it is changed during cell differentiation In this contribution, we develop 3DEpiLoop: a tool for the prediction of 3D genome-wide chromatin interactions using 1D genomic structure (mainly epigenomics and transcription factor assays). Our supervised learning predictor targets each cell type separately to reduce the bias towards the most common interactions across different cell types It uses the peaks of the mediating protein to capture all possible interacting segments whether they contain regulatory elements or not. We separately optimise and validate this tool on a range of different interactions types such as CTCF ChIA-PET, RNAP II ChIA-PET, in situ Hi-C loops, and in situ Hi-C heatmaps, and identify common predictive features
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