Abstract
BackgroundCo-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs.ResultsWe present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories.We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation.The code of the framework is available at https://github.com/henriettemiko/TimelessFlex.ConclusionsTimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.
Highlights
Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity
Multivariate, quantitative time series histone modification data is used as features for time series clustering, where available Hi-C data allows for the clustering of interacting pairs instead of individual regions
To utilize Hi-C data despite its frequently coarse resolution, we follow a two-step strategy, in which clusters are first determined on unambiguous assignments and in a second round extended by ambiguous interactions, which are resolved via expectationmaximization (EM)
Summary
Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Genomic regulatory regions like promoters and enhancers are important players in gene expression. Their activity has been shown to correlate with specific co-localized combinations of post-translational histone modifications (or marks) called ”chromatin states”. Chromatin states have initially been annotated in a spatial manner genome-wide, by segmenting the genome into distinct states based on histone modification ChIPseq data from, for instance, one cell line, which represents an unsupervised learning problem. In Segway, a Dynamic Bayesian Network modelling the read counts as independent Gaussian random variables is used to segment and label the genome at base-pair resolution into joint histone mark patterns [3]. Other methods for segmentation of a genome include jMOSAiCS [5], EpiCSeg [6] and Spectacle [7]
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