Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

Abstract Background Co-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. Results We 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. Conclusions TimelessFlex 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.

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Antagonising Chromatin Remodelling Activities in the Regulation of Mammalian Ribosomal Transcription

Genes ◽

10.3390/genes12070961 ◽

2021 ◽

Vol 12 (7) ◽

pp. 961

Author(s):

Kanwal Tariq ◽

Ann-Kristin Östlund Farrants

Keyword(s):

Stress Responses ◽

Ribosomal Gene ◽

Chromatin Remodelling ◽

Chromatin State ◽

Rrna Gene ◽

Open Chromatin ◽

Histone Chaperones ◽

Embryonic Cells ◽

Cell Type ◽

Chromatin States

Ribosomal transcription constitutes the major energy consuming process in cells and is regulated in response to proliferation, differentiation and metabolic conditions by several signalling pathways. These act on the transcription machinery but also on chromatin factors and ncRNA. The many ribosomal gene repeats are organised in a number of different chromatin states; active, poised, pseudosilent and repressed gene repeats. Some of these chromatin states are unique to the 47rRNA gene repeat and do not occur at other locations in the genome, such as the active state organised with the HMG protein UBF whereas other chromatin state are nucleosomal, harbouring both active and inactive histone marks. The number of repeats in a certain state varies on developmental stage and cell type; embryonic cells have more rRNA gene repeats organised in an open chromatin state, which is replaced by heterochromatin during differentiation, establishing different states depending on cell type. The 47S rRNA gene transcription is regulated in different ways depending on stimulus and chromatin state of individual gene repeats. This review will discuss the present knowledge about factors involved, such as chromatin remodelling factors NuRD, NoRC, CSB, B-WICH, histone modifying enzymes and histone chaperones, in altering gene expression and switching chromatin states in proliferation, differentiation, metabolic changes and stress responses.

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Interdependence between histone marks and steps in Pol II transcription

10.21203/rs.3.rs-149042/v1 ◽

2021 ◽

Author(s):

Charles Danko ◽

Zhong Wang ◽

Alexandra Chivu ◽

Lauren Choate ◽

Edward Rice ◽

...

Keyword(s):

Machine Learning ◽

Histone Modifications ◽

Dnase I ◽

Open Chromatin ◽

Learning Tools ◽

Pol Ii ◽

Histone Marks ◽

Chromatin States ◽

Hypersensitive Sites

Abstract The role of histone modifications in transcription remains incompletely understood. Here we used experimental perturbations combined with sensitive machine learning tools that infer the distribution of histone marks using maps of nascent transcription. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3 consistent with a role for RNA in recruiting PRC2. A subset of DNase-I hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than a regulatory, role in transcription.

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Systematic mapping of chromatin state landscapes during mouse development

10.1101/166652 ◽

2017 ◽

Cited By ~ 15

Author(s):

David U. Gorkin ◽

Iros Barozzi ◽

Yanxiao Zhang ◽

Ah Young Lee ◽

Bin Li ◽

...

Keyword(s):

Histone Modifications ◽

Target Genes ◽

Developmental Stages ◽

Developmental Time ◽

Chromatin State ◽

Mouse Development ◽

Disease Etiology ◽

Chromatin States ◽

Mouse Tissues ◽

Core Components

SUMMARYEmbryogenesis requires epigenetic information that allows each cell to respond appropriately to developmental cues. Histone modifications are core components of a cell’s epigenome, giving rise to chromatin states that modulate genome function. Here, we systematically profile histone modifications in a diverse panel of mouse tissues at 8 developmental stages from 10.5 days post conception until birth, performing a total of 1,128 ChIP-seq assays across 72 distinct tissue-stages. We combine these histone modification profiles into a unified set of chromatin state annotations, and track their activity across developmental time and space. Through integrative analysis we identify dynamic enhancers, reveal key transcriptional regulators, and characterize the role of chromatin-based repression in developmental gene regulation. We also leverage these data to link enhancers to putative target genes, revealing connections between coding and non-coding sequence variation in disease etiology. Our study provides a compendium of resources for biomedical researchers, and achieves the most comprehensive view of embryonic chromatin states to date.

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Random order autoregressive time series model with structural break

Model Assisted Statistics and Applications ◽

10.3233/mas-200490 ◽

2020 ◽

Vol 15 (3) ◽

pp. 225-237

Author(s):

Saurabh Kumar ◽

Jitendra Kumar ◽

Vikas Kumar Sharma ◽

Varun Agiwal

Keyword(s):

Time Series ◽

Structural Breaks ◽

Time Series Data ◽

Structural Break ◽

Random Order ◽

Real Data ◽

Series Data ◽

Model Parameters ◽

Multiple Time ◽

Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

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Developmental Normalization of Phenomics Data Generated by High Throughput Plant Phenotyping Systems

10.1101/2020.05.17.100917 ◽

2020 ◽

Author(s):

Diego Lozano-Claros ◽

Xiangxiang Meng ◽

Eddie Custovic ◽

Guang Deng ◽

Oliver Berkowitz ◽

...

Keyword(s):

Time Series ◽

High Throughput ◽

Leaf Size ◽

Plant Phenotyping ◽

Early Developmental Stage ◽

Multiple Time ◽

Sowing Time ◽

Statistical Dispersion ◽

Multiple Time Points ◽

Development Trajectories

AbstractBackgroundSowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.ResultsThe DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.

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RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking

Bioinformatics ◽

10.1093/bioinformatics/btaa631 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5068-5075 ◽

Cited By ~ 1

Author(s):

Yue Wu ◽

Michael T Judge ◽

Jonathan Arnold ◽

Suchendra M Bhandarkar ◽

Arthur S Edison

Keyword(s):

Time Series ◽

Nmr Spectra ◽

Chemical Shifts ◽

Supplementary Information ◽

Multiple Time ◽

Local Curvature ◽

Peak Overlap ◽

3D Surface ◽

Multiple Regions ◽

Multiple Time Points

Abstract Motivation Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. Results We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. Availability and implementation Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. Supplementary information Supplementary data are available at Bioinformatics online.

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Chromatin dynamics associated with sexual differentiation in a UV sex determination system

10.1101/2020.10.29.359190 ◽

2020 ◽

Author(s):

Josselin Gueno ◽

Simon Bourdareau ◽

Guillaume Cossard ◽

Olivier Godfroy ◽

Agnieszka Lipinska ◽

...

Keyword(s):

Gene Expression ◽

Sex Determination ◽

Histone Modifications ◽

Sex Chromosomes ◽

Sexual Differentiation ◽

Chromatin State ◽

State Transitions ◽

Male And Female ◽

Chromatin States ◽

Males And Females

SummaryIn many eukaryotes, such as dioicous mosses and many algae, sex is determined by UV sex chromosomes and is expressed during the haploid phase of the life cycle. In these species, the male and female developmental programs are initiated by the presence of the U- or V-specific regions of the sex chromosomes but, as in XY and ZW systems, phenotypic differentiation is largely driven by autosomal sex-biased gene expression. The mechanisms underlying sex-biased transcription in XY, ZW or UV sexual systems currently remain elusive. Here, we set out to understand the extent and nature of epigenomic changes associated with sexual differentiation in the brown alga Ectocarpus, which has a well described UV system. Five histone modifications, H3K4me3, H3K27Ac, H3K9Ac, H3K36me3, H4K20me3, were quantified in near-isogenic male and female lines, leading to the identification of 13 different chromatin states across the Ectocarpus genome that showed different patterns of enrichment at transcribed, silent, housekeeping or narrowly-expressed genes. Chromatin states were strongly correlated with levels of gene expression indicating a relationship between the assayed marks and gene transcription. The relative proportion of each chromatin state across the genome remained stable in males and females, but a subset of genes exhibited different chromatin states in the two sexes. In particular, males and females displayed distinct patterns of histone modifications at sex-biased genes, indicating that chromatin state transitions occur preferentially at genes involved in sex-specific pathways. Finally, our results reveal a unique chromatin landscape of the U and V sex chromosomes compared to autosomes. Taken together, our observations reveal a role for histone modifications in sex determination and sexual differentiation in a UV sexual system, and suggest that the mechanisms of epigenetic regulation of genes on the UV sex chromosomes may differ from those operating on autosomal genes.

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Sequential Histone Modifications at Hoxd4 Regulatory Regions Distinguish Anterior from Posterior Embryonic Compartments

Molecular and Cellular Biology ◽

10.1128/mcb.24.18.8090-8103.2004 ◽

2004 ◽

Vol 24 (18) ◽

pp. 8090-8103 ◽

Cited By ~ 48

Author(s):

Mojgan Rastegar ◽

Laila Kobrossy ◽

Erzsebet Nagy Kovacs ◽

Isabel Rambaldi ◽

Mark Featherstone

Keyword(s):

Rna Polymerase Ii ◽

Histone Modifications ◽

Chromatin Immunoprecipitation ◽

Gene Activation ◽

Open Chromatin ◽

Rna Pol Ii ◽

Regulatory Regions ◽

Pol Ii ◽

Embryonic Tissues ◽

Intervening Sequences

ABSTRACT Hox genes are differentially expressed along the embryonic anteroposterior axis. We used chromatin immunoprecipitation to detect chromatin changes at the Hoxd4 locus during neurogenesis in P19 cells and embryonic day 8.0 (E8.0) and E10.5 mouse embryos. During Hoxd4 induction in both systems, we observed that histone modifications typical of transcriptionally active chromatin occurred first at the 3′ neural enhancer and then at the promoter. Moreover, the sequential distribution of histone modifications between E8.0 and E10.5 was consistent with a spreading of open chromatin, starting with the enhancer, followed by successively more 5′ intervening sequences, and culminating at the promoter. Neither RNA polymerase II (Pol II) nor CBP associated with the inactive gene. During Hoxd4 induction, CBP and RNA Pol II were recruited first to the enhancer and then to the promoter. Whereas the CBP association was transient, RNA Pol II remained associated with both regulatory regions. Histone modification and transcription factor recruitment occurred in posterior, Hox-expressing embryonic tissues, but never in anterior tissues, where such genes are inactive. Together, our observations demonstrate that the direction of histone modifications at Hoxd4 mirrors colinear gene activation across Hox clusters and that the establishment of anterior and posterior compartments is accompanied by the imposition of distinct chromatin states.

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NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

10.1101/2021.06.05.447209 ◽

2021 ◽

Author(s):

Min Shi ◽

Shamim Mollah

Keyword(s):

Time Series ◽

Missing Values ◽

Short Term Memory ◽

Distinct Advantage ◽

Multiple Time ◽

Omics Data ◽

Data Imputation ◽

Time Points ◽

Forecasting Methods ◽

Multiple Time Points

Abstract: High-throughput studies of biological systems are rapidly generating a wealth of 'omics'-scale data. Many of these studies are time-series collecting proteomics and genomics data capturing dynamic observations. While time-series omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values resulting in data shortage. Data missing and shortage are especially problematic for downstream applications such as omics data integration and computational analyses that need complete and sufficient data representations. Data imputation and forecasting methods have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. As a result, these techniques lack the ability to capture the time-ordered nature of data and cannot handle omics data containing missing values at multiple time points. Result: We propose a network-based method for time-series omics data imputation and forecasting (NeTOIF) that handle omics data containing missing values at multiple time points. NeTOIF takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform time-series imputation and forecasting using a long short-term memory network. Evaluating NeTOIF with a proteomic and a genomic dataset demonstrated a distinct advantage of NeTOIF over existing data imputation and forecasting methods. The average mean square error of NeTOIF improved 11.3% for imputation and 6.4% for forcasting compared to the baseline methods.

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Persistent chromatin states, pervasive transcription, and shared cis-regulatory sequences have shaped the C. elegans genome

10.1101/817130 ◽

2019 ◽

Author(s):

James M. Bellush ◽

Iestyn Whitehouse

Keyword(s):

Mrna Expression ◽

Histone Modifications ◽

Open Chromatin ◽

Regulatory Sequences ◽

Splice Leader ◽

Rna Transcription ◽

Chromatin States ◽

C Elegans ◽

Gene Positioning ◽

Nascent Rna

AbstractDespite highly conserved chromatin states and cis-regulatory elements, studies of metazoan genomes reveal that gene organization and the strategies to control mRNA expression can vary widely among animal species. C. elegans gene regulation is often assumed to be similar to that of other model organisms, yet evidence suggests the existence of distinct molecular mechanisms to pattern the developmental transcriptome, including extensive post-transcriptional RNA control pathways, widespread splice leader (SL) trans-splicing of pre-mRNAs, and the organization of genes into operons. Here, we performed ChIP-seq for histone modifications in highly synchronized embryos cohorts representing three major developmental stages, with the goal of better characterizing whether the dynamic changes in embryonic mRNA expression are accompanied by changes to the chromatin state. We were surprised to find that thousands of promoters are persistently marked by active histone modifications, despite a fundamental restructuring of the transcriptome. We employed global run-on sequencing using a long-read nanopore format to map nascent RNA transcription across embryogenesis, finding that the invariant open chromatin regions are persistently transcribed by Pol II at all stages of embryo development, even though the mature mRNA is not produced. By annotating our nascent RNA sequencing reads into directional transcription units, we find extensive evidence of polycistronic RNA transcription genome-wide, suggesting that nearby genes in C. elegans are linked by shared transcriptional regulatory mechanisms. We present data indicating that the sharing of cis-regulatory sequences has constrained C. elegans gene positioning and likely explains the remarkable retention of syntenic gene pairs over long evolutionary timescales.

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