Peak tables, differential regions, methylation calls, quality metrics, and statistical significance for all epigenomics tools.

Each tool returns coordinate-anchored peaks, differential regions, or per-site methylation calls with statistics and quality metrics. These give you the regulatory context around a target — active enhancers and promoters, accessibility that shifts under treatment, and methylation-silenced regions — that informs target selection and where to design CRISPR guides at open chromatin.

Interpreting epigenomics outputs

ATAC-seq Peaks output

atacseq_peaks and chipseq_peaks return the same result shape.

Field	Type	Description
`num_peaks`	number	Total peaks called
`frip`	number	Fraction of reads in peaks (quality metric, 0-1)
`total_reads`	number	Total mapped reads
`reads_in_peaks`	number	Reads overlapping peaks
`method`	string	Peak caller used
`genome_build`	string	Reference genome build
`peak_width_stats`	object	`mean`, `median`, `std` peak width in base pairs
`chromosome_distribution`	object	Peak count per chromosome
`top_peaks`	array	Top peaks by signal (subset, not the full peak set)
`parameters`	object	Peak calling parameters used

Peak fields (`top_peaks`)

Field	Type	Description
`chrom`	string	Chromosome name
`start`	number	Peak start coordinate
`end`	number	Peak end coordinate
`signal_value`	number	Signal value at the peak
`pvalue`	number	Peak significance
`qvalue`	number	FDR-adjusted significance

The result does not include gene annotation (nearest gene, distance to TSS, feature type) — chain into a genomics tool for that.

Quality metrics

Metric	Good	Acceptable	Poor
FRiP (Fraction of Reads in Peaks)	> 0.3	0.1 - 0.3	< 0.1
Total peaks	30,000 - 100,000	10,000 - 30,000	< 10,000
Median peak width	200 - 500 bp	100 - 200 or 500 - 1000 bp	< 100 or > 1000 bp

ChIP-seq Peaks output

chipseq_peaks returns the same num_peaks / frip / top_peaks shape documented above for ATAC-seq — there is no separate chip_reads, input_reads, signal_to_noise, or peak_type field.

Expected peak counts by mark

Histone Mark	Typical Peak Count	Width Range
H3K4me3	15,000 - 40,000	200 - 2,000 bp
H3K27ac	20,000 - 80,000	200 - 3,000 bp
H3K4me1	30,000 - 100,000	500 - 5,000 bp
H3K27me3	5,000 - 30,000	5,000 - 100,000 bp
H3K36me3	10,000 - 40,000	5,000 - 50,000 bp
CTCF	20,000 - 60,000	100 - 500 bp

ATAC-seq Differential output

Field	Type	Description
`differential_peaks`	number	Number of differential peaks
`total_peaks_tested`	number	Total peaks tested
`gained`	number	Peaks with increased accessibility in treatment
`lost`	number	Peaks with decreased accessibility in treatment
`method`	string	Differential method used
`genome_build`	string	Reference genome build
`fold_change_threshold`	number	Log2 fold-change threshold used
`fdr_threshold`	number	FDR threshold used
`peaks`	array	Differential peak details

Differential peak fields

Field	Type	Description
`chrom`	string	Chromosome name
`start`	number	Region start coordinate
`end`	number	Region end coordinate
`log2_fold_change`	number	Log2 fold change (treatment / control)
`p_value`	number	Raw p-value
`adjusted_p_value`	number	FDR-adjusted p-value
`treatment_count`	number	Treatment signal
`control_count`	number	Control signal
`direction`	string	`"gained"` (more open) or `"lost"` (more closed)

The result does not include gene annotation (nearest gene, distance to TSS) — chain into a genomics tool for that.

Fold change interpretation

Log2 FC	Direction	Meaning
> 1.0	Positive	Substantially more accessible in treatment
0.5 to 1.0	Positive	Moderately more accessible
-0.5 to 0.5	--	No significant change
-1.0 to -0.5	Negative	Moderately less accessible
< -1.0	Negative	Substantially less accessible

ChIP-seq Differential output

chipseq_differential returns the same shape as atacseq_differential, except the peak-count fields are named differential_sites / total_sites_tested, and the direction fields are enriched / depleted instead of gained / lost. Per-region direction values are "enriched" (increased binding) and "depleted" (decreased binding).

Methylation Calling output

methylation_call returns genome-wide and per-chromosome summary statistics — it does not return a per-site table.

Field	Type	Description
`total_cpgs`	number	Total CpGs analyzed
`total_reads_processed`	number	Total reads processed
`mean_coverage`	number	Mean CpG coverage
`median_coverage`	number	Median CpG coverage
`global_methylation`	number	Genome-wide average methylation level (0-1)
`median_methylation`	number	Median methylation level (0-1)
`genome_build`	string	Reference genome build
`method`	string	Method used
`chromosome_methylation`	object	Mean methylation per chromosome
`methylation_distribution`	array	Methylation histogram bins

Methylation level interpretation

Methylation	Interpretation
0.8 - 1.0	Heavily methylated (typically silenced)
0.2 - 0.8	Partially methylated (heterogeneous)
0.0 - 0.2	Unmethylated (typically active)

DMR Detection output

Field	Type	Description
`num_dmrs`	number	Total DMRs detected
`hyper`	number	Hypermethylated DMRs (treatment > control)
`hypo`	number	Hypomethylated DMRs (treatment < control)
`method`	string	DMR detection method used
`genome_build`	string	Reference genome build
`total_cpgs_tested`	number	Total CpG sites tested
`total_windows_tested`	number	Total sliding windows tested
`window_size`	number	Sliding window size (bp)
`methylation_diff_threshold`	number	Methylation-difference threshold used
`fdr_threshold`	number	FDR threshold used
`dmrs`	array	DMR details

DMR fields

Field	Type	Description
`chrom`	string	Chromosome name
`start`	number	DMR start coordinate
`end`	number	DMR end coordinate
`n_cpgs`	number	Number of CpG sites in the DMR
`treatment_methylation`	number	Mean methylation in treatment
`control_methylation`	number	Mean methylation in control
`methylation_difference`	number	Methylation difference (treatment - control)
`p_value`	number	Raw p-value
`adjusted_p_value`	number	FDR-adjusted p-value
`direction`	string	`"hyper"` or `"hypo"`

The result does not include gene annotation — chain into a genomics tool for that.

DMR interpretation

Direction	Mean Diff	Biological Implication
Hyper	> 0.2	Gene silencing, repression gained
Hypo	< -0.2	Gene activation, repression lost

Promoter hypermethylation is commonly associated with tumor suppressor silencing in cancer. Gene body methylation often correlates with active transcription.

Interpreting epigenomics outputs