The bread wheat epigenomic map reveals distinct chromatin architectural and evolutionary features of functional genetic elements

BACKGROUND

Bread wheat is an allohexaploid species with a 16-Gb genome that has large intergenic regions, which presents a big challenge for pinpointing regulatory elements and further revealing the transcriptional regulatory mechanisms. Chromatin profiling to characterize the combinatorial patterns of chromatin signatures is a powerful means to detect functional elements and clarify regulatory activities in human studies.

RESULTS

In the present study, through comprehensive analyses of the open chromatin, DNA methylome, seven major chromatin marks, and transcriptomic data generated for seedlings of allohexaploid wheat, we detected distinct chromatin architectural features surrounding various functional elements, including genes, promoters, enhancer-like elements, and transposons. Thousands of new genic regions and cis-regulatory elements are identified based on the combinatorial pattern of chromatin features. Roughly 1.5% of the genome encodes a subset of active regulatory elements, including promoters and enhancer-like elements, which are characterized by a high degree of chromatin openness and histone acetylation, an abundance of CpG islands, and low DNA methylation levels. A comparison across sub-genomes reveals that evolutionary selection on gene regulation is targeted at the sequence and chromatin feature levels. The divergent enrichment of cis-elements between enhancer-like sequences and promoters implies these functional elements are targeted by different transcription factors.

CONCLUSIONS

We herein present a systematic epigenomic map for the annotation of cis-regulatory elements in the bread wheat genome, which provides new insights into the connections between chromatin modifications and cis-regulatory activities in allohexaploid wheat.

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

Background

A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging.

Results

We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC). The source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic.

Conclusion

The pipeline can be used by researchers to map monoallelic expression in a variety of cell types using existing models and to train new models with additional sets of chromatin marks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2679-7) contains supplementary material, which is available to authorized users.

ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule mining

Background

Various chromatin modifications, identified in large-scale epigenomic analyses, are associated with distinct phenotypes of different cells and disease phases. To improve our understanding of these variations, many computational methods have been developed to discover novel sites and cell-specific chromatin modifications. Despite the availability of existing methods, there is still room for further improvement when they are applied to resolve the histone code hypothesis. Hence, we aim to investigate the development of a computational method to provide new insights into de novo combinatorial pattern discovery of chromatin modifications to characterize epigenetic variations in distinct phenotypes of different cells.

Results

We report a new computational approach, ChARM (Combinatorial Chromatin Modification Patterns using Association Rule Mining), that can be employed for the discovery of de novo combinatorial patterns of differential chromatin modifications. We used ChARM to analyse chromatin modification data from the livers of normal (non-cancerous) mice and hepatitis B virus X (HBx)-transgenic mice with hepatocellular carcinoma, and discovered 2,409 association rules representing combinatorial chromatin modification patterns. Among these, the combination of three histone modifications, a loss of H3K4Me3 and gains of H3K27Me3 and H3K36Me3, was the most striking pattern associated with the cancer. This pattern was enriched in functional elements of the mouse genome such as promoters, coding exons and 5′UTR with high CpG content, and CpG islands. It also showed strong correlations with polymerase activity at promoters and DNA methylation levels at gene bodies. We found that 30 % of the genes associated with the pattern were differentially expressed in the HBx compared to the normal, and 78.9 % of these genes were down-regulated. The significant canonical pathways (Wnt/ß-catenin, cAMP, Ras, and Notch signalling) that were enriched in the pattern could account for the pathogenesis of HBx.

Conclusions

ChARM, an unsupervised method for discovering combinatorial chromatin modification patterns, can identify histone modifications that occur globally. ChARM provides a scalable framework that can easily be applied to find various levels of combination patterns, which should reflect a range of globally common to locally rare chromatin modifications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1307-z) contains supplementary material, which is available to authorized users.