Data loading, peak analysis, and quality metrics for the three core epigenomic assay types: ATAC-seq, ChIP-seq, and DNA methylation.
| File |
Purpose |
atacseq.py |
ATAC-seq peak loading (narrowPeak), TSS enrichment, chromatin accessibility |
chipseq.py |
ChIP-seq peak loading, filtering, enrichment, motif discovery in peaks |
methylation.py |
Methylation BedGraph/cov parsing, DMR detection, CpG island identification |
| Function |
Description |
load_atac_peaks() |
Parse ATAC-seq narrowPeak file into ATACPeak list |
calculate_atac_statistics() |
Peak width distribution, signal stats, FRiP |
identify_tss_enrichment() |
Score ATAC signal enrichment around TSS sites |
find_tf_binding_sites() |
Identify transcription factor motifs within peaks |
load_chip_peaks() |
Parse ChIP-seq narrowPeak file into ChIPPeak list |
filter_peaks_by_score() |
Threshold-based peak filtering with optional top-N |
calculate_peak_enrichment() |
Fold enrichment of peaks in genomic regions |
find_motifs_in_peaks() |
Motif scanning within ChIP-seq peak sequences |
load_methylation_bedgraph() |
Parse methylation BedGraph with coverage filtering |
find_differentially_methylated_regions() |
Detect DMRs between conditions |
identify_cpg_islands() |
Locate CpG islands by GC content and CpG ratio |
calculate_methylation_entropy() |
Shannon entropy of methylation levels per region |
from metainformant.epigenome.assays.chipseq import load_chip_peaks, calculate_peak_statistics
from metainformant.epigenome.assays.methylation import load_methylation_bedgraph
peaks = load_chip_peaks("H3K4me3.narrowPeak")
stats = calculate_peak_statistics(peaks)
meth = load_methylation_bedgraph("bismark.bedGraph", min_coverage=5)