DotMatch

DotMatch assigns short FASTQ read windows to a known list of DNA sequences. It is useful when you already know the guides, barcodes, feature tags, primers, or other targets that may be present and want every read reported as a unique match, an ambiguous match, unmatched, or invalid.

CI PyPI Documentation Bioconda License DOI

Documentation · Getting started · Command reference · Examples · Citation

FASTQ reads and a target table are compared at a fixed read window. DotMatch writes counts, split FASTQs, QC tables, and reports.

Install

PyPI is the quickest route on Linux and macOS:

python3 -m pip install dotmatch
dotmatch --version

Conda users can install the current Bioconda build:

conda create -n dotmatch -c conda-forge -c bioconda dotmatch
conda activate dotmatch

The Bioconda recipe supports Linux, Intel macOS, and Apple Silicon (osx-arm64). If a newly tagged version has not reached Bioconda yet, use the PyPI package or install from source.

A small example

Prepare a tab-separated target file:

target_id	sequence
guide_001	ACGTACGTACGTACGTACGT
guide_002	ACGTACGTACGTACGTAGGT

Then assign a fixed 20-base window from each read:

dotmatch count \
  --targets guides.tsv \
  --reads sample_R1.fastq.gz \
  --sample-label sample_1 \
  --target-start 23 \
  --target-length 20 \
  --k 1 \
  --metric hamming \
  --out counts.tsv \
  --sample-qc sample_qc.tsv \
  --summary summary.json

DotMatch only counts a read when exactly one target is compatible under the selected matching rule. Reads that fit several targets remain visible as ambiguous instead of being assigned arbitrarily.

What it is for

  • counting CRISPR guides and writing MAGeCK-compatible count tables;
  • demultiplexing fixed-position inline barcodes;
  • assigning feature-barcode and guide-capture reads;
  • checking primer, adapter, amplicon-panel, or whitelist sequences;
  • auditing target lists before enabling mismatch correction;
  • designing and checking barcode panels;
  • writing TSV, JSON, FASTQ, and HTML results for pipelines and lab review.

DotMatch is not a genome aligner, basecaller, UMI pipeline, variant caller, or screen-level statistics package. It compares short read windows with a finite target list.

Read outcomes

Outcome Meaning
unique Exactly one target is compatible.
ambiguous More than one target is compatible.
none No target is within the selected distance.
invalid The requested read window could not be extracted.

These states appear in the assignment and QC outputs. They are not folded into the unique counts.

Common workflows

Count CRISPR guides

For a new screen, DotMatch can prepare a small assay project and infer a likely guide window for review:

dotmatch crispr quickstart \
  --library guides.csv \
  --fastq 'fastqs/*.fastq.gz' \
  --out crispr_screen/

Review crispr_screen/inference_report.json and assay.toml, then run:

dotmatch assay start crispr_screen/assay.toml

For an explicit one-command run, use dotmatch crispr-count. The CRISPR tutorial covers both routes.

Demultiplex inline barcodes

dotmatch demux \
  --barcodes barcodes.tsv \
  --reads pooled.fastq.gz \
  --barcode-start 0 \
  --barcode-length 8 \
  --k 1 \
  --metric hamming \
  --out-dir demuxed/ \
  --summary demux.summary.json

If a run has an unexpectedly high unmatched or ambiguous rate, inspect it with:

dotmatch barcode autopsy \
  --barcodes barcodes.tsv \
  --reads pooled.fastq.gz \
  --scan-starts 0:12 \
  --k-values 0,1 \
  --out-dir autopsy/

Open autopsy/report.html first. The tables beside it record offset scans, near-neighbour barcodes, correction safety, and frequent unmatched windows.

Check a target library

Before allowing mismatch correction, check whether neighbouring targets can produce ambiguous assignments:

dotmatch audit \
  --targets guides.tsv \
  --k 1 \
  --audit-mode auto \
  --out-dir audit/

The barcode panel guide also covers panel design, optimisation, simulation, layout, and export.

Python API

import dotmatch

distance = dotmatch.distance("ACGT", "AGGT")
assert distance == 1

result = dotmatch.assign_posterior("ACGT", ["ACGT", "AGGT"], "IIII")
print(result.status)

The posterior helper is experimental and is not used by the high-throughput CLI path. The Python API documentation describes the supported streaming interfaces.

Outputs and workflow integration

Depending on the command, DotMatch writes count tables, split FASTQs, sample_qc.tsv, per-read assignments, unmatched-read tables, summary.json, and self-contained HTML reports. The formats are documented in the output schema reference.

Examples for Nextflow, nf-core, Snakemake, Galaxy, and MultiQC live under examples/workflows. The desktop Workbench is maintained separately in dotmatch-community.

Matching rules and performance

Hamming distance is the usual choice for fixed-length windows where only base substitutions should be considered. Levenshtein distance can also account for short insertions and deletions. The default radius policy requires a single compatible target; the optional best policy exists for compatibility with workflows that select the nearest target.

Indexed candidate generation and native distance kernels make fixed-window assignment practical for large FASTQ inputs. Benchmark results, hardware, commands, and known limitations are kept with the benchmark reports. Those reports cover the tested workloads; they are not a claim that DotMatch replaces general alignment or every demultiplexing workflow.

Documentation

Citation

Run dotmatch citation to print the citation for the installed version. The repository also includes CITATION.cff, and release archives are deposited with Zenodo.

Development

git clone https://github.com/dnncha/dotmatch.git
cd dotmatch
make
make test

See CONTRIBUTING.md for the development setup and pull-request checks.

License

Apache-2.0. See LICENSE.

Project links

Data verified by PyPI on Jul 23, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for dnncha from gravatar.com dnncha

Credits

Author: Donncha O'Toole

GitHub Statistics

Data verified by PyPI on Jul 23, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.9

Provides Extra

pandas anndata polars multiqc dev

Tags

bioinformatics computational biology CRISPR FASTQ known-target assignment feature barcodes sequencing QC barcode demultiplexing edit distance

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dotmatch-0.2.2.tar.gz (228.5 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

dotmatch-0.2.2-py3-none-musllinux_1_2_x86_64.whl (316.4 kB view details)

Uploaded Python 3musllinux: musl 1.2+ x86-64

dotmatch-0.2.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (312.1 kB view details)

Uploaded Python 3manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

dotmatch-0.2.2-py3-none-macosx_11_0_universal2.whl (422.6 kB view details)

Uploaded Python 3macOS 11.0+ universal2 (ARM64, x86-64)

File details

Details for the file dotmatch-0.2.2.tar.gz.

File metadata

  • Download URL: dotmatch-0.2.2.tar.gz
  • Upload date:
  • Size: 228.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for dotmatch-0.2.2.tar.gz
Algorithm Hash digest
SHA256 c441aaafb6b29db51560d3fc68c52a8ad01ed0f08158a89544c1d9366f12fce8
MD5 ae1bececb86f0bc98cee30172d103a38
BLAKE2b-256 242032b2daeeab01820c5980fa9f22b1f36e3f8d9fd02def955125cab993dd0b

See more details on using hashes here.

Provenance

The following attestation bundles were made for dotmatch-0.2.2.tar.gz:

Publisher: release.yml on dnncha/dotmatch

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file dotmatch-0.2.2-py3-none-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for dotmatch-0.2.2-py3-none-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 124648d8856bed8c4f6283f07a55baf8b4ea8d00ace18880b36a3a7638f1c3fc
MD5 f8372c635b3d1cd9c765cfc7e5208e97
BLAKE2b-256 b6ff2c3323542d643efdcf6df4d68d1768ce178de0b38fa13b5064c5e3345f42

See more details on using hashes here.

Provenance

The following attestation bundles were made for dotmatch-0.2.2-py3-none-musllinux_1_2_x86_64.whl:

Publisher: release.yml on dnncha/dotmatch

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file dotmatch-0.2.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for dotmatch-0.2.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6748a134c4abb8bab62d42de4018a0e7441ea5841f2f13f5c628ec7234650adf
MD5 eedd29fbd6f1373b684aac1b3c396b0f
BLAKE2b-256 029f04b3e7ecab66ef15c0e40d0e690741bbdef0cf6dbeed95f38460e83ac9ea

See more details on using hashes here.

Provenance

The following attestation bundles were made for dotmatch-0.2.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl:

Publisher: release.yml on dnncha/dotmatch

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file dotmatch-0.2.2-py3-none-macosx_11_0_universal2.whl.

File metadata

File hashes

Hashes for dotmatch-0.2.2-py3-none-macosx_11_0_universal2.whl
Algorithm Hash digest
SHA256 a4780d65c4ef738e0289be770cceda37bbd245057300d035c23a3329c2415494
MD5 e3973fb7cf4e94a9b69410087acdb0b0
BLAKE2b-256 7599f11d487007f6464c2ae7ac8ee164a653d8a6a14a914c42f94e7daabeaa8f

See more details on using hashes here.

Provenance

The following attestation bundles were made for dotmatch-0.2.2-py3-none-macosx_11_0_universal2.whl:

Publisher: release.yml on dnncha/dotmatch

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

0.2.2 This release

4 files

0.2.1

4 files

0.2.0

4 files

0.1.9

4 files

0.1.8

4 files

0.1.7

4 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page