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]

scholarly journals The role of maternal pioneer factors in predefining first zygotic responses to inductive signals

2018 ◽  
Author(s):  
George E. Gentsch ◽  
Thomas Spruce ◽  
Nick D. L. Owens ◽  
James C. Smith
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Regulatory Elements ◽  
Regulatory Dna Sequences ◽  
Cell Type Specific ◽  
Pioneer Factors ◽  
Developmental Principles ◽  
Regulatory Dna ◽  
Different Cell Types ◽  
Inductive Signals

ABSTRACTEmbryonic development yields many different cell types in response to just a few families of inductive signals. The property of a signal-receiving cell that determines how it responds to such signals, including the activation of cell type-specific genes, is known as its competence. Here, we show how maternal factors modify chromatin to specify initial competence in the frog Xenopus tropicalis. We identified the earliest engaged regulatory DNA sequences, and inferred from them critical activators of the zygotic genome. Of these, we showed that the pioneering activity of the maternal pluripotency factors Pou5f3 and Sox3 predefines competence for germ layer formation by extensively remodeling compacted chromatin before the onset of signaling. The remodeling includes the opening and marking of thousands of regulatory elements, extensive chromatin looping, and the co-recruitment of signal-mediating transcription factors. Our work identifies significant developmental principles that inform our understanding of how pluripotent stem cells interpret inductive signals.

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 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.

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 Analysis of putative cis-regulatory elements regulating blood pressure variation

2019 ◽  
Author(s):  
Priyanka Nandakumar ◽  
Dongwon Lee ◽  
Thomas J. Hoffmann ◽  
Georg B. Ehret ◽  
Dan Arking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Pressure ◽  
Cell Types ◽  
Regulatory Elements ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Functional Scores ◽  
Cell Type Specific ◽  
Different Tissues

AbstractHundreds of loci have been associated with blood pressure 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 (GERA) study participants. In the present study, we subsequently focused on determining putative regulatory regions for these and other tissues of relevance to blood pressure, to both fine-map these loci by pinpointing genes and variants of functional interest within them, and to identify any novel genes.We constructed maps of putative cis-regulatory elements using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Sequence 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. In order to identify genes of interest, we aggregate these variants in these putative cis-regulatory elements within 50Kb of the start or end of genes considered as “expressed” in these tissues or cell types using publicly available gene expression data, and use the deltaSVM scores as weights in the well-known group-wise sequence kernel association test (SKAT). We test for association with both blood pressure traits as well as expression within these tissues or cell types of interest, and identify several genes, including MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B, and PPCDC. Although our study centers on blood pressure traits, we additionally examined two known genes, SCN5A and NOS1AP involved in the cardiac trait QT interval, in the Atherosclerosis Risk in Communities Study (ARIC), as a positive control, and observed an expected heart-specific effect. Thus, our method may be used to identify variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.Author SummarySequence change in genes (“variants”) are linked to the presence and severity of different traits or diseases. However, as genes may be expressed in different tissues and at different times and degrees, using this information is expected to more accurately identify genes of interest. Variants within the genes are essential, but also in the sequences (“regulatory elements”) that control the genes’ expression in different tissues or cell types. In this study, we aim to use this information about expression and variants potentially involved in gene expression regulation to better pinpoint genes and variants in regulatory elements of interest for blood pressure regulation. We do so by taking advantage of such data that are publicly available, and use methods to combine information about variants in aggregate within a gene’s putative regulatory elements in tissues thought to be relevant for blood pressure, and identify several genes, meant to enable experimental follow-up.

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

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

Abstract ObjectiveComputational identification of cell type-specific regulatory elements on a genome-wide scale is very challenging.ResultsWe 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.

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