Introduction

DeamTools is an open-source Python 3.12+ command-line toolkit for the analysis of deaminase-based chromatin accessibility data. Double-stranded DNA cytosine deaminases (e.g. DddA, DddSs/SsdAtox) preferentially edit cytosines in accessible, protein-free chromatin. DeamTools turns the resulting single-base editing events into genome-wide accessibility and transcription-factor footprinting signal.

It is developed for assays such as ACCESS-ATAC and related deaminase footprinting methods, and provides a small set of composable commands that cover the full workflow from raw reads to quantitative tracks and quality reports.

Background

Chromatin accessibility marks the regulatory regions of the genome — promoters, enhancers, and the binding sites of transcription factors (TFs). The established assays, DNase-seq and ATAC-seq, read out accessibility from the ends of enzymatically cut or transposed fragments, which yields at most two informative positions per fragment and limits resolution to roughly the fragment length.

Deaminase-based assays instead use a double-stranded DNA cytosine deaminase to write many marks along each accessible molecule. Every editable cytosine in exposed DNA can be converted, so a single read carries dozens of accessibility measurements rather than two. This gives several advantages:

• Single-base resolution — editing is recorded per cytosine, not per fragment end.

• Footprints — DNA bound by a TF or nucleosome is shielded from editing, leaving a local depletion that pinpoints the bound element.

• Single-molecule / single-allele readout — because the marks are encoded as sequence changes, each read reports the accessibility state of one DNA molecule, and is compatible with standard short-read sequencing and PCR.

How it works

A deaminase converts cytosine (C) to uracil (U) on exposed single-stranded DNA; after PCR and sequencing this is read as a C→T substitution on the top strand, or a G→A substitution on reads from the bottom strand. The density of these edits reports chromatin accessibility, while proteins bound to DNA shield it from editing, leaving footprints.

DeamTools handles the two computational challenges this creates. First, heavily edited reads align poorly to an unmodified genome, so index/align use a bwa-meth-style three-letter alignment against a converted reference. Second, the edits must be distinguished from the reference and tallied; bam2bw, bam2fragment, and qc detect C→T / G→A mismatches at aligned positions and summarise them — as per-base tracks, per-fragment tables, or QC metrics — at single-base resolution.

Key features

• Deamination-aware alignment of heavily converted reads, with original sequences and chromosome names restored in a standard sorted BAM.

• Per-base editing tracks (BigWig) as raw counts or conversion ratios, with optional signal extension and region restriction.

• Per-fragment tables capturing single-molecule edit patterns, with cell-barcode support for single-cell data.

• Quality control in a self-contained HTML report: editing rate, per-fragment edit-rate distribution, enzyme context bias, fragment sizes, and ENCODE-style TSS enrichment.

• Enzyme-bias diagnostics, including a deaminase sequence-preference logo built directly from the BAM and embedded in the QC report.

• Multi-process and region-restricted processing for whole-genome or targeted runs.

Components

DeamTools is organised as a set of subcommands, run as deamtools <command>:

Command	Purpose
index	Build the deamination-aware reference index (FASTA .fai + converted BWA index).
align	Align deaminated reads (bwa-meth-style) to the indexed reference, producing a sorted BAM.
bam2bw	Convert a BAM to a per-base BigWig of editing counts or conversion ratios.
bam2fragment	Convert a BAM to a per-fragment editing-signal table (bulk or single-cell).
qc	Quality-control metrics — including the deaminase sequence-motif logo — in a self-contained HTML report.
match	Scan regions for TF motif matches (motifmatchpy) and write a BED of binding sites.
footprint	Score TF footprints at motif sites from a BigWig (flank-vs-centre depletion).
seq2edit	Train a CNN that maps DNA sequence to per-base editing (the deaminase sequence bias).

Workflow

FASTQ ─▶ index ─▶ align ─▶ BAM ─┬─▶ bam2bw ──────▶ BigWig track
                                ├─▶ bam2fragment ─▶ fragment table
                                └─▶ qc ──────────▶ JSON + HTML report (+ motif logo)

Quick start

A typical end-to-end run, from a reference and FASTQs to tracks and a QC report:

# 1. One-time: index the reference (writes <fasta>.fai + the converted BWA index)
deamtools index --fasta genome.fa

# 2. Align deaminated reads -> results/sample.bam (+ .bai)
deamtools align --fasta genome.fa \
    --read1 sample_R1.fq.gz --read2 sample_R2.fq.gz \
    --out_dir results --out_name sample

# 3. Per-base editing track -> results/sample.bw
deamtools bam2bw --bam results/sample.bam --fasta genome.fa \
    --out_dir results --out_name sample

# 4. Quality-control report (with the deaminase motif logo)
#    -> results/sample.json + results/sample.html
deamtools qc --bam results/sample.bam --fasta genome.fa \
    --out_dir results --out_name sample

See each command page for the full option list.

Supported file formats

Format	Used by	Role
FASTA (.fa + .fai)	all	Reference genome.
FASTQ (.fq[.gz])	align	Raw sequencing reads.
BAM (.bam + .bai)	most	Coordinate-sorted aligned reads.
BigWig (.bw)	bam2bw	Per-base editing signal.
BED	bam2bw, qc	Regions / TSS to restrict analysis to.

Getting started

1. Install DeamTools and samtools/bwa — see Installation.

2. Index your reference, then align reads — see index and align.

3. Generate signal and QC — see bam2bw and qc.

To understand how editing events are detected and counted, see the Algorithm page; for the Python API, see the API reference.

Related methods and further reading

Deaminase-based accessibility and footprinting is an active area; DeamTools targets ACCESS-ATAC but the file formats and metrics apply broadly. Key methods:

• ACCESS-ATAC — Yu, Li, et al. Deaminase-mediated chromatin accessibility profiling with single-allele resolution. bioRxiv (2024). doi:10.1101/2024.12.17.628768

• DAF-seq — Swanson et al. Mapping single-cell diploid chromatin fiber architectures. Nature Biotechnology (2025). doi:10.1038/s41587-025-02914-3

• TDAC-seq — Roh et al. Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase. Nature Methods (2025). doi:10.1038/s41592-025-02811-2

• cFOOT-seq — Wang et al. Genome-wide investigation of transcription factor footprints using cFOOT-seq. Protein & Cell (2025). doi:10.1093/procel/pwaf071

• FOODIE — He et al. Genome-wide single-cell and single-molecule footprinting of transcription factors with deaminase. PNAS (2024). doi:10.1073/pnas.2423270121

Source and issues

DeamTools is developed on GitHub at github.com/lzj1769/deamTools. Please report bugs and feature requests via the issue tracker. Released under the MIT License.