HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures

BACKGROUND

Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

RESULTS

To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

CONCLUSIONS

This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Learning chromatin states with factorized information criteria

MOTIVATION

Recent studies have suggested that both the genome and the genome with epigenetic modifications, the so-called epigenome, play important roles in various biological functions, such as transcription and DNA replication, repair, and recombination. It is well known that specific combinations of histone modifications (e.g. methylations and acetylations) of nucleosomes induce chromatin states that correspond to specific functions of chromatin. Although the advent of next-generation sequencing (NGS) technologies enables measurement of epigenetic information for entire genomes at high-resolution, the variety of chromatin states has not been completely characterized.

RESULTS

In this study, we propose a method to estimate the chromatin states indicated by genome-wide chromatin marks identified by NGS technologies. The proposed method automatically estimates the number of chromatin states and characterize each state on the basis of a hidden Markov model (HMM) in combination with a recently proposed model selection technique, factorized information criteria. The method is expected to provide an unbiased model because it relies on only two adjustable parameters and avoids heuristic procedures as much as possible. Computational experiments with simulated datasets show that our method automatically learns an appropriate model, even in cases where methods that rely on Bayesian information criteria fail to learn the model structures. In addition, we comprehensively compare our method to ChromHMM on three real datasets and show that our method estimates more chromatin states than ChromHMM for those datasets.

Spectacle: fast chromatin state annotation using spectral learning

Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle.

Role of chromatin and transcriptional co-regulators in mediating p63-genome interactions in keratinocytes

BACKGROUND

The Transcription Factor (TF) p63 is a master regulator of epidermal development and differentiation as evident from the remarkable skin phenotype of p63 mouse knockouts. Furthermore, ectopic expression of p63 alone is sufficient to convert simple epithelium into stratified epithelial tissues in vivo and p63 is required for efficient transdifferentiation of fibroblasts into keratinocytes. However, little is known about the molecular mechanisms of p63 function, in particular how it selects its target sites in the genome. p63, which acts both as an activator and repressor of transcription, recognizes a canonical binding motif that occurs over 1 million times in the human genome. But, in human keratinocytes less than 12,000 of these sites are bound in vivo suggesting that underlying chromatin architecture and cooperating TFs mediate p63-genome interactions.

RESULTS

We find that the chromatin architecture at p63-bound targets possess distinctive features and can be used to categorize p63 targets into proximal promoters (1%), enhancers (59%) and repressed or inactive (40%) regulatory elements. Our analysis shows that the chromatin modifications H3K4me1, H3K27me3, along with overall chromatin accessibility status can accurately predict bonafide p63-bound sites without a priori DNA sequence information. Interestingly, however there exists a qualitative correlation between the p63 binding motif and accessibility and H3K4me1 levels. Furthermore, we use a comprehensive in silico approach that leverages ENCODE data to identify several known TFs such as AP1, AP2 and novel TFs (RFX5 for e.g.) that can potentially cooperate with p63 to modulate its myriad biological functions in keratinocytes.

CONCLUSIONS

Our analysis shows that p63 bound genomic locations in keratinocytes are accessible, marked by active histone modifications, and co-targeted by other developmentally important transcriptional regulators. Collectively, our results suggest that p63 might actively remodel and/or influence chromatin dynamics at its target sites and in the process dictate its own DNA binding and possibly that of adjacent TFs.