Please access the step-by-step tutorials for performing analyses similar to those described in our paper in Nature Comm. (Feb, 2015).Epigenome Tutorial
The Epigenomic Data Slice tool provides read information for a given sample and set of regions. Users can use the tab-delimited output file, which contains average signals of an epigenomic mark over a given set of regions of interest, for further off-line analysis. For example, users can use this information for generating a read density plot at the transcription start sites.
The Heatmap tool computes the similarity between datasets based on epigenomic signal. Several distance metric options are available to calculate the similarity between datasets and results are presented visually as a heatmap.
Spark is a visualization tool that employs clustering (k-means) of epigenomic data, such as chromatin marks from Chip-Seq experiments. The visualization allows one to examine epigenomic patterns across multiple datasets in a genome-wide perspective, while also enabling one to drill down to specific individual loci.
ChromHMM is a tool for learning and characterizing chromatin states (e.g., active transcription start sites (TSS), active enhancers, etc.). ChromHMM LearnModel uses binarized data files and learns a chromatin state model. It generates both the learned chromatin-state model parameters and the chromatin-state assignments for each genomic position.
HOMER (Hypergeometric Optimization of Motif Enrichment) is a motif discovery algorithm designed to find enriched motifs for a set of genomic regions.
GREAT (Genomic Regions Enrichment of Annotations Tool) is another enrichment tool that assigns biological meaning to a set of non-coding genomic regions by performing gene ontological (GO) enrichment analysis of nearby genes.
LIMMA (Linear Model for Microarray Analysis) is a tool that performs signal comparison of next-gen sequencing data and outputs the differences in signal intensity between samples of interest. This allows one to determine differences between samples (i.e., control muscle versus treated muscle). Differences may include differential methylation (if MeDIP input) or gene expression level (if RNA-seq input).
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