MIRA - Methylation-Based Inference of Regulatory Activity

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.

Last updated 21 days ago

immunooncologydnamethylationgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqmethylseqsequencingepigeneticscoverage

7.52 score 11 stars 1 packages 7 scripts 208 downloads

simpleCache - Simply Caching R Objects

Provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the 'batchtools' package) making it flexible enough for use in large-scale data analysis projects.

Last updated 4 years ago

7.50 score 34 stars 1 packages 69 scripts 218 downloads

GenomicDistributions - GenomicDistributions: fast analysis of genomic intervals with Bioconductor

If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.

Last updated 21 days ago

softwaregenomeannotationgenomeassemblydatarepresentationsequencingcoveragefunctionalgenomicsvisualization

7.42 score 25 stars 25 scripts 246 downloads

COCOA - Coordinate Covariation Analysis

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

Last updated 21 days ago

epigeneticsdnamethylationatacseqdnaseseqmethylseqmethylationarrayprincipalcomponentgenomicvariationgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqsequencingimmunooncologydna-methylationpca

6.00 score 10 stars 20 scripts 220 downloads