scholarly journals Computational inference of H3K4me3 and H3K27ac domain length

2016 ◽  
Author(s):  
Julian Zubek ◽  
Michael L Stitzel ◽  
Duygu Ucar ◽  
Dariusz M Plewczynski
Keyword(s):  
Random Forest ◽  
Dna Sequences ◽  
Regression Models ◽  
Cell Types ◽  
Regulatory Elements ◽  
Epigenetic Mark ◽  
Cell Type ◽  
Epigenetic Marks ◽  
Genomic Signatures ◽  
The Individual

Background. Recent epigenomic studies have shown that the length of a DNA region covered by an epigenetic mark is not just a byproduct of the assaying technologies and has functional implications for that locus. For example, expanded regions of DNA sequences that are marked by enhancer-specific histone modifications, such as acetylation of histone H3 lysine 27 (H3K27ac) domains coincide with cell-specific enhancers, known as super or stretch enhancers. Similarly, promoters of genes critical for cell-specific functions are marked by expanded H3K4me3 domains in the cognate cell type, and these can span DNA regions from 4-5kb up to 40-50kb in length. These expanded H3K4me3 domains are known as buffer domains or super promoters. Methods. To ask what correlates with—and potentially regulates—the length of loci marked with these two important histone marks, H3K4me3 and H3K27ac, we built Random Forest regression models. With these models we computationally identified genomic and epigenomic patterns that are predictive for the length of these marks in seven ENCODE cell lines. Results. We found that certain epigenetic marks and transcription factors explain the variability of the length of H3K4me3 and H3K27ac marks across different cell types, which implies that the lengths of these two epigenetic marks are tightly regulated in a given cell type. Our source code for the regression models and data can be found at our GitHub page: https://github.com/zubekj/broad_peaks Discussion. Our Random Forest based regression models enabled us to estimate the individual contribution of different epigenetic marks and protein binding patterns to the length of H3K4me3 and H3K27ac deposition patterns; therefore potentially revealing genomic signatures at cell specific regulatory elements.

scholarly journals Computational inference of H3K4me3 and H3K27ac domain length

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1750 ◽  
Author(s):  
Julian Zubek ◽  
Michael L. Stitzel ◽  
Duygu Ucar ◽  
Dariusz M. Plewczynski
Keyword(s):  
Random Forest ◽  
Dna Sequences ◽  
Regression Models ◽  
Cell Types ◽  
Regulatory Elements ◽  
Epigenetic Mark ◽  
Cell Type ◽  
Epigenetic Marks ◽  
Genomic Signatures ◽  
The Individual

Background.Recent epigenomic studies have shown that the length of a DNA region covered by an epigenetic mark is not just a byproduct of the assaying technologies and has functional implications for that locus. For example, expanded regions of DNA sequences that are marked by enhancer-specific histone modifications, such as acetylation of histone H3 lysine 27 (H3K27ac) domains coincide with cell-specific enhancers, known as super or stretch enhancers. Similarly, promoters of genes critical for cell-specific functions are marked by expanded H3K4me3 domains in the cognate cell type, and these can span DNA regions from 4–5kb up to 40–50kb in length. These expanded H3K4me3 domains are known as buffer domains or super promoters.Methods.To ask what correlates with—and potentially regulates—the length of loci marked with these two important histone marks, H3K4me3 and H3K27ac, we built Random Forest regression models. With these models, we computationally identified genomic and epigenomic patterns that are predictive for the length of these marks in seven ENCODE cell lines.Results.We found that certain epigenetic marks and transcription factors explain the variability of the length of H3K4me3 and H3K27ac marks across different cell types, which implies that the lengths of these two epigenetic marks are tightly regulated in a given cell type. Our source code for the regression models and data can be found at our GitHub page:https://github.com/zubekj/broad_peaks.Discussion.Our Random Forest based regression models enabled us to estimate the individual contribution of different epigenetic marks and protein binding patterns to the length of H3K4me3 and H3K27ac deposition patterns, therefore potentially revealing genomic signatures at cell specific regulatory elements.

scholarly journals Computational inference of H3K4me3 and H3K27ac domain length

2016 ◽  
Author(s):  
Julian Zubek ◽  
Michael L Stitzel ◽  
Duygu Ucar ◽  
Dariusz M Plewczynski
Keyword(s):  
Random Forest ◽  
Dna Sequences ◽  
Regression Models ◽  
Cell Types ◽  
Regulatory Elements ◽  
Epigenetic Mark ◽  
Cell Type ◽  
Epigenetic Marks ◽  
Genomic Signatures ◽  
The Individual

Background. Recent epigenomic studies have shown that the length of a DNA region covered by an epigenetic mark is not just a byproduct of the assaying technologies and has functional implications for that locus. For example, expanded regions of DNA sequences that are marked by enhancer-specific histone modifications, such as acetylation of histone H3 lysine 27 (H3K27ac) domains coincide with cell-specific enhancers, known as super or stretch enhancers. Similarly, promoters of genes critical for cell-specific functions are marked by expanded H3K4me3 domains in the cognate cell type, and these can span DNA regions from 4-5kb up to 40-50kb in length. These expanded H3K4me3 domains are known as buffer domains or super promoters. Methods. To ask what correlates with—and potentially regulates—the length of loci marked with these two important histone marks, H3K4me3 and H3K27ac, we built Random Forest regression models. With these models we computationally identified genomic and epigenomic patterns that are predictive for the length of these marks in seven ENCODE cell lines. Results. We found that certain epigenetic marks and transcription factors explain the variability of the length of H3K4me3 and H3K27ac marks across different cell types, which implies that the lengths of these two epigenetic marks are tightly regulated in a given cell type. Our source code for the regression models and data can be found at our GitHub page: https://github.com/zubekj/broad_peaks Discussion. Our Random Forest based regression models enabled us to estimate the individual contribution of different epigenetic marks and protein binding patterns to the length of H3K4me3 and H3K27ac deposition patterns; therefore potentially revealing genomic signatures at cell specific regulatory elements.

scholarly journals Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements

2017 ◽  
Author(s):  
Luca Pinello ◽  
Rick Farouni ◽  
Guo-Cheng Yuan
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Regulatory Elements ◽  
Epigenetic Mark ◽  
Cell Type ◽  
Epigenetic Variability ◽  
Bioinformatics Pipeline ◽  
Systematic Analysis ◽  
Regulatory Dna Sequences ◽  
Cell Type Specific

AbstractMotivationWith the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is how to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences that play a role in gene regulation.ResultsHaystack is a bioinformatics pipeline to characterize hotspots of epigenetic variability across different cell-types as well as cell-type specific cis-regulatory elements along with their corresponding transcription factors. Our approach is generally applicable to any epigenetic mark and provides an important tool to investigate cell-type identity and the mechanisms underlying epigenetic switches during development. Additionally, we make available a set of precomputed tracks for a number of epigenetic marks across several cell types. These precomputed results may be used as an independent resource for functional annotation of the human genome.AvailabilityThe Haystack pipeline is implemented as an open-source, multiplatform, Python package called haystack_bio available at https://github.com/pinellolab/[email protected], [email protected]

Role of Transcriptional Read-Through in PRE Activity in Drosophila melanogaster

Acta Naturae ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 79-86 ◽  
Author(s):  
P. V. Elizar’ev ◽  
D. V. Lomaev ◽  
D. A. Chetverina ◽  
P. G. Georgiev ◽  
M. M. Erokhin
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Transcription Terminator ◽  
Response Elements ◽  
Polycomb Response Elements ◽  
The Individual ◽  
Multicellular Organisms ◽  
Different Cell Types ◽  
Main Factor

Maintenance of the individual patterns of gene expression in different cell types is required for the differentiation and development of multicellular organisms. Expression of many genes is controlled by Polycomb (PcG) and Trithorax (TrxG) group proteins that act through association with chromatin. PcG/TrxG are assembled on the DNA sequences termed PREs (Polycomb Response Elements), the activity of which can be modulated and switched from repression to activation. In this study, we analyzed the influence of transcriptional read-through on PRE activity switch mediated by the yeast activator GAL4. We show that a transcription terminator inserted between the promoter and PRE doesnt prevent switching of PRE activity from repression to activation. We demonstrate that, independently of PRE orientation, high levels of transcription fail to dislodge PcG/TrxG proteins from PRE in the absence of a terminator. Thus, transcription is not the main factor required for PRE activity switch.

scholarly journals A scalable platform for the development of cell-type-specific viral drivers

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sinisa Hrvatin ◽  
Christopher P Tzeng ◽  
M Aurel Nagy ◽  
Hume Stroud ◽  
Charalampia Koutsioumpa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heterologous Gene Expression ◽  
High Specificity ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cell Type Specific ◽  
The Many ◽  
Dna Regulatory Elements

Enhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.

scholarly journals SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

2020 ◽  
Author(s):  
Yupeng Wang ◽  
Rosario B. Jaime-Lara ◽  
Abhrarup Roy ◽  
Ying Sun ◽  
Xinyue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Sequences ◽  
Cell Types ◽  
Learning Models ◽  
Cell Type ◽  
Coding Sequences ◽  
Sequence Features ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

Expression of the K-fgf proto-oncogene is controlled by 3' regulatory elements which are specific for embryonal carcinoma cells

1990 ◽  
Vol 10 (6) ◽  
pp. 2475-2484
Author(s):  
A M Curatola ◽  
C Basilico
Keyword(s):  
Dna Sequences ◽  
Embryonal Carcinoma ◽  
Protein S ◽  
Cell Types ◽  
Regulatory Elements ◽  
Developmentally Regulated ◽  
Cis Acting ◽  
Dna Elements ◽  
Ec Cells ◽  
Cat Expression

Expression of the K-fgf/hst proto-oncogene appears to be restricted to cells in the early stages of development, such as embryonal carcinoma (EC) cells. When EC cells are induced to differentiate, K-fgf expression is drastically repressed. To identify cis-acting DNA elements responsible for this type of regulation, we constructed a plasmid in which cat gene expression was driven by about 1 kilobase of upstream K-fgf human DNA sequences, including the putative promoter, and transfected it into undifferentiated F9 EC cells or HeLa cells as prototypes of cells which express or do not express, respectively, the K-fgf proto-oncogene. This plasmid was essentially inactive in both cell types, and the addition of more than 8 kilobases of DNA sequences upstream of the K-fgf promoter did not lead to any increase in chloramphenicol acetyltransferase (CAT) expression. On the other hand, when we inserted in this plasmid DNA sequences which are 3' of the human K-fgf coding sequences, we could detect a significant stimulation of CAT activity. Analysis of these sequences led to the identification of enhancerlike DNA elements which are part of the 3' noncoding region of K-fgf exon 3 and promote CAT expression only in undifferentiated mouse F9 or human NT2/D1 EC cells, but not in HeLa, 3T3, or differentiated F9 cells, therefore mimicking the physiological expression of the K-fgf proto-oncogene. Similar elements are also present in the 3' region of the murine K-fgf proto-oncogene, in a region showing high homology to the human K-fgf sequences. These regulatory elements can promote CAT expression from heterologous promoters in an EC-specific manner, suggesting that they interact with a specific cellular transacting protein(s) whose expression is developmentally regulated.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Analysis of putative cis-regulatory elements regulating blood pressure variation

2020 ◽  
Vol 29 (11) ◽  
pp. 1922-1932
Author(s):  
Priyanka Nandakumar ◽  
Dongwon Lee ◽  
Thomas J Hoffmann ◽  
Georg B Ehret ◽  
Dan Arking ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Association Studies ◽  
Specific Effect ◽  
Cell Types ◽  
Regulatory Elements ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Functional Scores ◽  
Cell Type Specific

Abstract Hundreds of loci have been associated with blood pressure (BP) traits from many genome-wide association studies. We identified an enrichment of these loci in aorta and tibial artery expression quantitative trait loci in our previous work in ~100 000 Genetic Epidemiology Research on Aging study participants. In the present study, we sought to fine-map known loci and identify novel genes by determining putative regulatory regions for these and other tissues relevant to BP. We constructed maps of putative cis-regulatory elements (CREs) using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Variants within these regions may be evaluated quantitatively for their tissue- or cell-type-specific regulatory impact using deltaSVM functional scores, as described in our previous work. We aggregate variants within these putative CREs within 50 Kb of the start or end of ‘expressed’ genes in these tissues or cell types using public expression data and use deltaSVM scores as weights in the group-wise sequence kernel association test to identify candidates. We test for association with both BP traits and expression within these tissues or cell types of interest and identify the candidates MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B and PPCDC. Additionally, we examined two known QT interval genes, SCN5A and NOS1AP, in the Atherosclerosis Risk in Communities Study, as a positive control, and observed the expected heart-specific effect. Thus, our method identifies variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.

scholarly journals Evolution of regulatory signatures in primate cortical neurons at cell-type resolution

2020 ◽  
Vol 117 (45) ◽  
pp. 28422-28432
Author(s):  
Alexey Kozlenkov ◽  
Marit W. Vermunt ◽  
Pasha Apontes ◽  
Junhao Li ◽  
Ke Hao ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Brain Evolution ◽  
Cell Types ◽  
Regulatory Elements ◽  
Autism Spectrum ◽  
Projection Neurons ◽  
Evolutionary Divergence ◽  
Cell Type ◽  
Tissue Samples ◽  
Functional Changes

The human cerebral cortex contains many cell types that likely underwent independent functional changes during evolution. However, cell-type–specific regulatory landscapes in the cortex remain largely unexplored. Here we report epigenomic and transcriptomic analyses of the two main cortical neuronal subtypes, glutamatergic projection neurons and GABAergic interneurons, in human, chimpanzee, and rhesus macaque. Using genome-wide profiling of the H3K27ac histone modification, we identify neuron-subtype–specific regulatory elements that previously went undetected in bulk brain tissue samples. Human-specific regulatory changes are uncovered in multiple genes, including those associated with language, autism spectrum disorder, and drug addiction. We observe preferential evolutionary divergence in neuron subtype-specific regulatory elements and show that a substantial fraction of pan-neuronal regulatory elements undergoes subtype-specific evolutionary changes. This study sheds light on the interplay between regulatory evolution and cell-type–dependent gene-expression programs, and provides a resource for further exploration of human brain evolution and function.

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