QuantNado

Multi-modal genomic signal storage and analysis

QuantNado converts aligned genomic files (BAM, bedGraph, VCF) into efficient Zarr-backed stores and provides a unified Python API for signal extraction, methylation analysis, variant handling, and visualisation.

What QuantNado Does

Modality	Input	Stored data
Coverage	BAM files	per-base read depth, dense (sample × position)
Methylation	MethylDackel bedGraph	CpG methylation %, counts, sparse
Variants	VCF.gz files	genotype, allele depths, quality, sparse

All modalities live in a single directory (MultiomicsStore) and are accessed through one Python object.

Quick Example

import quantnado as qn

# Create a multi-modal dataset
qn.create_dataset(
    store_dir="dataset/",
    bam_files=["atac.bam", "chip.bam"],
    methyldackel_files=["meth-rep1.bedGraph", "meth-rep2.bedGraph"],
    vcf_files=["snp.vcf.gz"],
    methyldackel_sample_names=lambda p: p.stem.split("_hg38")[0],
)

# Open and explore
ds = qn.open("dataset/")
print(ds.modalities)   # ['coverage', 'methylation', 'variants']

# Coverage analysis
reduced = ds.reduce("promoters.bed", reduction="mean")
pca_obj, pca_result = ds.pca(reduced["mean"])
counts, features = ds.count_features("genes.gtf", feature_type="gene")

# Visualise
binned = ds.extract(feature_type="transcript", gtf_path="genes.gtf",
                    upstream=2000, downstream=2000, anchor="start", bin_size=50)
ds.metaplot(binned, modality="coverage", title="Metagene profile")

# Methylation
binned_meth = ds.extract(modality="methylation", feature_type="transcript",
                         gtf_path="genes.gtf", upstream=1000, downstream=1000,
                         anchor="start", bin_size=50)
ds.metaplot(binned_meth, modality="methylation", title="CpG methylation at TSS")

# Sub-store access
ds.coverage    # BamStore
ds.methylation # MethylStore
ds.variants    # VariantStore

Key Features

• Unified entry point — qn.open() / qn.create_dataset() for all modalities
• Lazy loading — Zarr + Dask; only reads what you ask for
• Multi-modal — coverage, methylation, and variant data in one store
• Analysis built-in — reduce, PCA, feature counts, metaplots, tornado plots
• Resumable — skip completed samples when re-running

Documentation

• Installation — Setup and dependencies
• Quick Start — First analysis in minutes
• Usage Guide — Coverage, methylation, and variant workflows
• CLI Reference — Command-line interface
• API Reference — Full Python API

Citation

If you use QuantNado, please cite:

QuantNado: Efficient genomic signal quantification and analysis
Milne Group, University of Oxford