ABSTRACT: Average methylation levels based on nanopolish calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.
Project description:Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.
Project description:Average methylation levels based on nanopolish calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.
Project description:Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.
Project description:Average methylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types using nanopolish. Data from 8 individuals.
Project description:Average hydroxymethylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types. Data from 8 individuals.
Project description:Average hypermethylation on transcription factor binding sites based on nanopolish calls; only positions showing higher methylation than sample’s average methylation at enhancers were included when defining the average methylation level. Data from 6 individuals at different time points.
Project description:Data tracks from bisulfite sequencing (BS-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics DNA methylation datasets using an in-house algorithm to create an average data track.
Project description:Data tracks from chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics H3K4me3 ChIP-seq datasets using an in-house algorithm to create an average data track.
Project description:The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease. This data set was submitted by University of Washington as part of GSE18927 ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18927 ) / SRP001371 at NCBI Gene Expression Omnibus and Sequence Read Archive respectively. The ArrayExpress record here contains only RNA-seq meta-data for the 53 human fetal samples covering 19 different tissues/organs from 23 fetuses. You can find out more about WashU's contribution to this project here: http://egg2.wustl.edu/roadmap/web_portal/index.html. The full set of NIH Epigenome Project data (not limited to RNA-seq) can be found here: http://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics
