NOTT2019_get_epigenomic_peaks: Download cell type-specific epigenomic peaks

In RajLabMSSM/echoannot: echoverse module: Annotate fine-mapping results

Download cell type-specific epigenomic peaks

Description

API access to brain cell type-specific epigenomic peaks (bed format) from Nott et al. (2019).

Usage

NOTT2019_get_epigenomic_peaks(
  assays = c("ATAC", "H3K27ac", "H3K4me3"),
  cell_types = c("neurons", "microglia", "oligo", "astrocytes"),
  convert_to_granges = TRUE,
  save_dir = tools::R_user_dir(package = "echoannot", which = "cache"),
  force_new = FALSE,
  nThread = 1,
  verbose = TRUE
)

Arguments

assays	Which epigenomic assays to import data from.
cell_types	Which cell-types to import data from.
convert_to_granges	Whether to convert the peaks to a GRanges object.
save_dir	Where to save the processed data.
force_new	If the saved data already exists, re-downloaded anyway.
nThread	Number of threads to parallelise downloads across.
verbose	Print messages.

Source

Nott et al. (2019)

See Also

Other NOTT2019: NOTT2019_bigwig_metadata, NOTT2019_epigenomic_histograms(), NOTT2019_get_interactions(), NOTT2019_get_interactome(), NOTT2019_get_promoter_celltypes(), NOTT2019_get_promoter_interactome_data(), NOTT2019_get_regulatory_regions(), NOTT2019_plac_seq_plot(), NOTT2019_superenhancers(), get_NOTT2019_interactome(), get_NOTT2019_superenhancer_interactome()

Examples

PEAKS <- echoannot::NOTT2019_get_epigenomic_peaks() 

RajLabMSSM/echoannot documentation built on Oct. 26, 2023, 2:41 p.m.