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

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 .

2018 ◽  
Author(s):  
Malte Thodberg ◽  
Axel Thieffry ◽  
Kristoffer Vitting-Seerup ◽  
Robin Andersson ◽  
Albin Sandelin

AbstractWe developed the CAGEfightR R/Biconductor-package for analyzing CAGE data. CAGEfightR allows for fast and memory efficient identification of transcription start sites (TSSs) and predicted enhancers. Downstream analysis, including annotation, quantification, visualization and TSS shape statistics are implemented in easy-to-use functions. The package is freely available at https://bioconductor.org/packages/CAGEfightR


2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus

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]


2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus

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]


2018 ◽  
Author(s):  
Malte Thodberg ◽  
Axel Thieffry ◽  
Jette Bornholdt ◽  
Mette Boyd ◽  
Christian Holmberg ◽  
...  

AbstractFission yeast, Schizosaccharomyces pombe, is an attractive model organism for transcriptional and chromatin biology research. Such research is contingent on accurate annotation of transcription start sites (TSSs). However, comprehensive genome-wide maps of TSSs and their usage across commonly applied laboratory conditions and treatments for S. pombe are lacking. To this end, we profiled TSS activity genome-wide in S. pombe cultures exposed to heat shock, nitrogen starvation, hydrogen peroxide and two commonly applied media, YES and EMM2, using Cap Analysis of Gene Expression (CAGE). CAGE-based annotation of TSSs is substantially more accurate than existing PomBase annotation; on average, CAGE TSSs fall 50-75 bp downstream of PomBase TSSs and co-localize with nucleosome boundaries. In contrast to higher eukaryotes, S. pombe does not show sharp and dispersed TSS distributions. Our data recapitulate known S. pombe stress expression response patterns and identify stress- and mediaresponsive alternative TSSs. Notably, alteration of growth medium induces changes of similar magnitude as some stressors. We show a link between nucleosome occupancy and genetic variation, and that the proximal promoter region is genetically diverse between S. pombe strains. Our detailed TSS map constitute a central resource for S. pombe gene regulation research.


Author(s):  
Masaki Suimye Morioka ◽  
Hideya Kawaji ◽  
Hiromi Nishiyori-Sueki ◽  
Mitsuyoshi Murata ◽  
Miki Kojima-Ishiyama ◽  
...  

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1981 ◽  
Author(s):  
Christophe D. Chabbert ◽  
Lars M. Steinmetz ◽  
Bernd Klaus

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].


2016 ◽  
Author(s):  
Christophe D Chabbert ◽  
Lars M Steinmetz ◽  
Bernd Klaus

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]


PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7526 ◽  
Author(s):  
Alfredo Mendoza-Vargas ◽  
Leticia Olvera ◽  
Maricela Olvera ◽  
Ricardo Grande ◽  
Leticia Vega-Alvarado ◽  
...  

2008 ◽  
Vol 22 (1) ◽  
pp. 10-22 ◽  
Author(s):  
Hui Gao ◽  
Susann Fält ◽  
Albin Sandelin ◽  
Jan-Åke Gustafsson ◽  
Karin Dahlman-Wright

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.


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