scholarly journals DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1981 ◽  
Author(s):  
Christophe D. Chabbert ◽  
Lars M. Steinmetz ◽  
Bernd Klaus
Keyword(s):  
Input Data ◽  
Enrichment Analysis ◽  
Bioconductor Package ◽  
Analytic Framework ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Bioconductor Project ◽  
User Friendly ◽  
Genome Wide Study

The genome-wide study of epigenetic states requires the integrative analysis of histone modification ChIP-seq data. Here, we introduce an easy-to-use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user-friendly R/Bioconductor packageDChIPRep.DChIPRepuses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment.DChIPRepis available for download through the Bioconductor project athttp://bioconductor.org/packages/[email protected].

scholarly journals DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus
Keyword(s):  
Input Data ◽  
Enrichment Analysis ◽  
Bioconductor Package ◽  
Analytic Framework ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Bioconductor Project ◽  
User Friendly ◽  
Genome Wide Study

The genome–wide study of epigenetic states requires the integrative analysis of histone modification ChIP–seq data. Here, we introduce an easy–to–use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user–friendly R/Bioconductor package DChIPRep. DChIPRep uses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment. DChIPRep is available for download through the Bioconductor project at http://bioconductor.org/packages/DChIPRep. Contact [email protected]

scholarly journals DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus
Keyword(s):  
Input Data ◽  
Enrichment Analysis ◽  
Bioconductor Package ◽  
Analytic Framework ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Bioconductor Project ◽  
User Friendly ◽  
Genome Wide Study

The genome–wide study of epigenetic states requires the integrative analysis of histone modification ChIP–seq data. Here, we introduce an easy–to–use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user–friendly R/Bioconductor package DChIPRep. DChIPRep uses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment. DChIPRep is available for download through the Bioconductor project at http://bioconductor.org/packages/DChIPRep. Contact [email protected]

scholarly journals DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus
Keyword(s):  
Input Data ◽  
Enrichment Analysis ◽  
Bioconductor Package ◽  
Analytic Framework ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Bioconductor Project ◽  
User Friendly ◽  
Genome Wide Study

The genome–wide study of epigenetic states requires the integrative analysis of histone modification ChIP–seq data. Here, we introduce an easy–to–use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user–friendly R/Bioconductor package DChIPRep. DChIPRep uses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment. DChIPRep is available for download through the Bioconductor project at http://bioconductor.org/packages/DChIPRep. Contact [email protected]

scholarly journals CAGEfightR: analysis of 5′-end data using R/Bioconductor

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Malte Thodberg ◽  
Axel Thieffry ◽  
Kristoffer Vitting-Seerup ◽  
Robin Andersson ◽  
Albin Sandelin
Keyword(s):  
Bioconductor Package ◽  
Transcription Start ◽  
Computational Tools ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Current Availability ◽  
Efficient Prediction ◽  
Downstream Analysis ◽  
Cap Analysis ◽  
Memory Efficient

Abstract Background 5′-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5′-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5′-end data is limited, preventing efficient analysis of new and existing 5′-end data. Results We present CAGEfightR, a framework for analysis of CAGE and other 5′-end data implemented as an R/Bioconductor-package. CAGEfightR can import data from BigWig files and allows for fast and memory efficient prediction and analysis of TSSs and enhancers. Downstream analyses include quantification, normalization, annotation with transcript and gene models, TSS shape statistics, linking TSSs to enhancers via co-expression, identification of enhancer clusters, and genome-browser style visualization. While built to analyze CAGE data, we demonstrate the utility of CAGEfightR in analyzing nascent RNA 5′-data (PRO-Cap). CAGEfightR is implemented using standard Bioconductor classes, making it easy to learn, use and combine with other Bioconductor packages, for example popular differential expression tools such as limma, DESeq2 and edgeR. Conclusions CAGEfightR provides a single, scalable and easy-to-use framework for comprehensive downstream analysis of 5′-end data. CAGEfightR is designed to be interoperable with other Bioconductor packages, thereby unlocking hundreds of mature transcriptomic analysis tools for 5′-end data. CAGEfightR is freely available via Bioconductor: bioconductor.org/packages/CAGEfightR .

scholarly journals Genome-Wide Identification of Transcription Start Sites, Promoters and Transcription Factor Binding Sites in E. coli

PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7526 ◽  
Author(s):  
Alfredo Mendoza-Vargas ◽  
Leticia Olvera ◽  
Maricela Olvera ◽  
Ricardo Grande ◽  
Leticia Vega-Alvarado ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
E Coli ◽  
Factor Binding ◽  
Genome Wide

scholarly journals Genome-Wide Identification of Estrogen Receptor α-Binding Sites in Mouse Liver

2008 ◽  
Vol 22 (1) ◽  
pp. 10-22 ◽  
Author(s):  
Hui Gao ◽  
Susann Fält ◽  
Albin Sandelin ◽  
Jan-Åke Gustafsson ◽  
Karin Dahlman-Wright
Keyword(s):  
Estrogen Receptor ◽  
Binding Sites ◽  
Mouse Liver ◽  
Estrogen Receptor Α ◽  
Estrogen Signaling ◽  
Transcription Start ◽  
Expression Levels ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Binding Regions

Abstract We report the genome-wide identification of estrogen receptor α (ERα)-binding regions in mouse liver using a combination of chromatin immunoprecipitation and tiled microarrays that cover all nonrepetitive sequences in the mouse genome. This analysis identified 5568 ERα-binding regions. In agreement with what has previously been reported for human cell lines, many ERα-binding regions are located far away from transcription start sites; approximately 40% of ERα-binding regions are located within 10 kb of annotated transcription start sites. Almost 50% of ERα-binding regions overlap genes. The majority of ERα-binding regions lie in regions that are evolutionarily conserved between human and mouse. Motif-finding algorithms identified the estrogen response element, and variants thereof, together with binding sites for activator protein 1, basic-helix-loop-helix proteins, ETS proteins, and Forkhead proteins as the most common motifs present in identified ERα-binding regions. To correlate ERα binding to the promoter of specific genes, with changes in expression levels of the corresponding mRNAs, expression levels of selected mRNAs were assayed in livers 2, 4, and 6 h after treatment with ERα-selective agonist propyl pyrazole triol. Five of these eight selected genes, Shp, Stat3, Pdgds, Pck1, and Pdk4, all responded to propyl pyrazole triol after 4 h treatment. These results extend our previous studies using gene expression profiling to characterize estrogen signaling in mouse liver, by characterizing the first step in this signaling cascade, the binding of ERα to DNA in intact chromatin.

scholarly journals Dynamics of transcription-dependent H3K36me3 marking by the SETD2:IWS1:SPT6 ternary complex

2019 ◽  
Author(s):  
Katerina Cermakova ◽  
Eric A. Smith ◽  
Vaclav Veverka ◽  
H. Courtney Hodges
Keyword(s):  
Ternary Complex ◽  
Spatial Distributions ◽  
Epigenetic Memory ◽  
Dynamic Features ◽  
Transcription Start ◽  
Accurate Representation ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Tight Correlation

AbstractSETD2 contributes to gene expression by marking gene bodies with H3K36me3, which is thought to assist in the concentration of transcription machinery at the small portion of the coding genome. Despite extensive genome-wide data revealing the precise localization of H3K36me3 over gene bodies, the physical basis for the accumulation, maintenance, and sharp borders of H3K36me3 over these sites remains rudimentary. Here we propose a model of H3K36me3 marking based on stochastic transcription-dependent placement and transcription-independent spreading. Our analysis of the spatial distributions and dynamic features of these marks indicates that transcription-dependent placement dominates the establishment of H3K36me3 domains compared to transcription-independent spreading processes, and that turnover of H3K36me3 limits its capacity for epigenetic memory. By adding additional terms for asymmetric histone turnover occurring at transcription start sites, our model provides a remarkably accurate representation of H3K36me3 levels and dynamics over gene bodies. Furthermore, we validate our findings by revealing that loss of SPT6 impairs the transcription-coupled activity of the SETD2:IWS1:SPT6 ternary complex, thereby reducing the tight correlation between transcription and H3K36me3 levels at gene bodies.

scholarly journals RNA polymerase II-independent recruitment of SPT6L at transcription start sites in Arabidopsis

2019 ◽  
Vol 47 (13) ◽  
pp. 6714-6725 ◽  
Author(s):  
Chen Chen ◽  
Jie Shu ◽  
Chenlong Li ◽  
Raj K Thapa ◽  
Vi Nguyen ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Transcription Initiation ◽  
Elongation Factor ◽  
Proper Function ◽  
Gene Body ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Yeast Mutants

Abstract SPT6 is a conserved elongation factor that is associated with phosphorylated RNA polymerase II (RNAPII) during transcription. Recent transcriptome analysis in yeast mutants revealed its potential role in the control of transcription initiation at genic promoters. However, the mechanism by which this is achieved and how this is linked to elongation remains to be elucidated. Here, we present the genome-wide occupancy of Arabidopsis SPT6-like (SPT6L) and demonstrate its conserved role in facilitating RNAPII occupancy across transcribed genes. We also further demonstrate that SPT6L enrichment is unexpectedly shifted, from gene body to transcription start site (TSS), when its association with RNAPII is disrupted. Protein domains, required for proper function and enrichment of SPT6L on chromatin, are subsequently identified. Finally, our results suggest that recruitment of SPT6L at TSS is indispensable for its spreading along the gene body during transcription. These findings provide new insights into the mechanisms underlying SPT6L recruitment in transcription and shed light on the coordination between transcription initiation and elongation.

scholarly journals GENE-58. ACTIVE TRANSCRIPTION START SITES OF MENINGIOMA SAMPLES ASSOCIATED WITH RISK OF RECURRENCE

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi110-vi110
Author(s):  
Tathiane Malta ◽  
Thais Sarraf Sabedot ◽  
Carlos Carlotti jr ◽  
Houtan Noushmehr
Keyword(s):  
Dna Methylation ◽  
Recurrence Risk ◽  
Transcription Start ◽  
Risk Of Recurrence ◽  
Transcription Start Sites ◽  
Data Alignment ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Substantial Risk ◽  
Active Transcription

Abstract Meningiomas are mostly benign brain tumors but have a substantial risk of recurrence, sometimes to more aggressive subtypes. Recently, a DNA methylation signature in meningioma was described as able to stratify patients by recurrence risk (favorable and unfavorable). It is well recognized that epigenetic deregulation at distinct genomic elements can affect changes in gene expression and contribute to cancer initiation and progression. Our goal for this study is to define genes that are actively expressed or repressed by both DNA methylation and chromatin histone modification (defined by H3K4me3). For this pilot study, we selected two favorable (grades I and II) and two unfavorable (grades II and III) meningioma primary tumor samples (N=4) and mapped H3K4me3 genome-wide and whole-genome DNA methylation, in an attempt to identify active transcription start sites at known promoters. After data alignment, preprocessing and peak calling, we identified 29,514 consensus peaks for H3K4me3. The differential binding analysis resulted in 5,752 H3K4me3 regions that distinguish favorable from unfavorable meningioma, mostly gain of peaks in the unfavorable group. We identified 1,505 peaks overlapping with known promoters, 51% associated with gain of peaks in the unfavorable group. Promoter-associated chromatin changes coincided with hypomethylation in 23 unique genes in the unfavorable group. Genes such as MET, PTEN, and the long non-coding RNA RP11-60L3.1 were identified as potential regulators of meningioma recurrence. Our preliminary results describe the identification of distinct genome-wide changes in chromatin associated with meningioma patient with high risk for recurrence. Identification of candidate genes will provide knowledge of the role of epigenomics in the development of malignant meningioma and of opportunities for targeted therapy.

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