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1. Accurate prediction of cis -regulatory modules reveals a prevalent regulatory genome of humans

   https://doi.org/10.1093/nargab/lqab052  

   Ni, Pengyu ; Su, Zhengchang ( April 2021 , NAR Genomics and Bioinformatics)

   null (Ed.)

   Abstract cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought. 

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2. A framework for group-wise summarization and comparison of chromatin state annotations

   https://doi.org/10.1093/bioinformatics/btac722  

   Vu, Ha ; Koch, Zane ; Fiziev, Petko ; Ernst, Jason ; Martelli, ed., Pier Luigi ( November 2022 , Bioinformatics)

   Genome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution.

   We developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference in CSREP’s probability assignments for the two groups can be used to identify genomic locations with differential chromatin state assignments. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution.

   The CSREP source code and generated data are available at http://github.com/ernstlab/csrep.

   Supplementary data are available at Bioinformatics online.

    

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3. A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

   https://doi.org/10.1186/s12864-022-08933-7  

   Ni, Pengyu ; Wilson, David ; Su, Zhengchang ( October 2022 , BMC Genomics)

   Mouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, the characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome.

   Here, by integrating binding peaks in ~ 9,000 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into acis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC set. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strong evolutionary constraints, indicating that they are likelycis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikelycis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively.

   Validation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans.

    

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4. Universal annotation of the human genome through integration of over a thousand epigenomic datasets

   https://doi.org/10.1186/s13059-021-02572-z  

   Vu, Ha ; Ernst, Jason ( January 2022 , Genome Biology)

   Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative “stacked modeling” approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.

   Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations.

   The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

    

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5. Methylation studies in Peromyscus: aging, altitude adaptation, and monogamy

   https://doi.org/10.1007/s11357-021-00472-5  

   Horvath, Steve ; Haghani, Amin ; Zoller, Joseph A. ; Naderi, Asieh ; Soltanmohammadi, Elham ; Farmaki, Elena ; Kaza, Vimala ; Chatzistamou, Ioulia ; Kiaris, Hippokratis ( October 2021 , GeroScience)

   DNA methylation-based biomarkers of aging have been developed for humans and many other mammals and could be used to assess how stress factors impact aging. Deer mice (Peromyscus) are long-living rodents that have emerged as an informative model to study aging, adaptation to extreme environments, and monogamous behavior. In the present study, we have undertaken an exhaustive, genome-wide analysis of DNA methylation inPeromyscus, spanning different species, stocks, sexes, tissues, and age cohorts. We describe DNA methylation-based estimators of age for different species of deer mice based on novel DNA methylation data generated on highly conserved mammalian CpGs measured with a custom array. The multi-tissue epigenetic clock for deer mice was trained on 3 tissues (tail, liver, and brain). Two human-Peromyscusclocks accurately measure age and relative age, respectively. We present CpGs and enriched pathways that relate to different conditions such as chronological age, high altitude, and monogamous behavior. Overall, this study provides a first step towards studying the epigenetic correlates of monogamous behavior and adaptation to high altitude inPeromyscus. The human-Peromyscusepigenetic clocks are expected to provide a significant boost to the attractiveness ofPeromyscusas a biological model.

    

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