fetchNOMe

Overview

fetchNOMe is an R package for fast and efficient retrieval of NOMe-seq data from BAM files. NOMe-seq (Nucleosome Occupancy and Methylome Sequencing) simultaneously captures endogenous CpG methylation and M.CviPI-induced GpC methylation, providing a high-resolution map of chromatin accessibility and DNA methylation.

This package is specifically designed to process BAM files generated from pipelines aligning bisulfite-converted DNA, such as Bismark, QuasR and BISCUIT aligners.

Features

• Extract GpC (M.CviPI-induced) and CpG (endogenous) methylation directly from BAM files (get_data_matrix_from_bams, get_molecule_data_list_from_bams)
• Extract co-occurrence statistics from BAM files for footprint spectral analysis by the nomeR package (get_ctable_from_bams)
• Extract statistics of protected states (get_protect_stats_from_bams)

Requirements

• Indexed BAM files from NOMe-seq experiments
• Index (.bai) file in the same location as the BAM

Installation

You can install the development version of fetchNOMe from GitHub with:

# install.packages("devtools")
devtools::install_github("fmi-basel/gpeters-fetchNOMe")

Usage

library(fetchNOMe)

# Define BAM file path
bam <- system.file("extdata", "QuasR_test.bam", package = "fetchNOMe")

# Define genome FASTA file path
genome <- system.file("extdata", "random_genome_700bp.fa", package = "fetchNOMe")

# Extract GCH protection states for a region of interest (ROI)
GCH_tbl_mat <- get_data_matrix_from_bams(
  bamfiles          = bam,
  samplenames       = "quasr_pe",
  regions           = GenomicRanges::GRanges(
                        seqnames = "random_genome_700bp",
                        strand   = "+",
                        IRanges::IRanges(start = 200, end = 500)
                      ),
  genome            = genome,
  whichContext      = "GCH",
  remove_nonunique  = FALSE,
  clip_until_nbg    = 0L,
  max_bisC_meth     = 1
)

# Extract WCG endogenous methylation states for a region of interest (ROI)
WCG_tbl_mat <- get_data_matrix_from_bams(
  bamfiles          = bam,
  samplenames       = "quasr_pe",
  regions           = GenomicRanges::GRanges(
                        seqnames = "random_genome_700bp",
                        strand   = "+",
                        IRanges::IRanges(start = 200, end = 500)
                      ),
  genome            = genome,
  whichContext      = "WCG",
  remove_nonunique  = FALSE,
  clip_until_nbg    = 0L,
  max_bisC_meth     = 1
)

# Extract GCH protection for all molecules within ROI
GCH_tbl_methlist <- get_molecule_data_list_from_bams(
  bamfiles           = bam,
  samplenames        = "quasr_pe",
  regions            = GenomicRanges::GRanges(
                         seqnames = "random_genome_700bp",
                         strand   = "+",
                         IRanges::IRanges(start = 200, end = 500)
                       ),
  genome             = genome,
  whichContext       = "GCH",
  remove_nonunique   = FALSE,
  clip_until_nbg     = 0L,
  max_bisC_meth      = 1,
  min_frag_data_len  = 50L,
  min_frag_data_dens = 0.05
)

# Extract co-occurrence count tables for footprint spectral analysis
ctable <- get_ctable_from_bams(
  bamfiles           = bam,
  samplenames        = "quasr_pe",
  regions            = GenomicRanges::GRanges(
                         seqnames = "random_genome_700bp",
                         strand   = "+",
                         IRanges::IRanges(start = 200, end = 500)
                       ),
  genome             = genome,
  alignerUsed        = "QuasR",
  min_frag_data_len  = 0L,
  min_frag_data_dens = 0.0,
  max_spacing        = 300,
  remove_nonunique   = FALSE,
  clip_until_nbg     = 0L,
  max_bisC_meth      = 1
)

                                                                            

Citation

If you use fetchNOMe in your work, please cite:

Evgeniy A. Ozonov. fetchNOMe: fast retrieval of NOMe-seq data from BAM files (https://doi.org/10.5281/zenodo.8402785). Available at: https://github.com/fmi-basel/gpeters-fetchNOMe

License

This package is licensed under the MIT License. Copyright (c) 2023 Friedrich Miescher Institute for Biomedical Research