dada2-rs

📖 Documentation: dada2-rs.readthedocs.io — installation, Illumina/PacBio walkthroughs, and the performance/benchmarking reference.

An experimental implementation of DADA2 in Rust, using Claude Code (specically Sonnet 4.6 and Opus 4.6/4.7/4.8) for the bulk of the work.

Implementations

Rust ports of:

Step	DADA2 (R)	dada2-rs
Filter/trimming FASTQ	filterAndTrim	filter-and-trim
Filter/trimming FASTQ (PacBio)	removePrimers,filterAndTrim	remove-primers (one step)
Dereplication	derepFastq	derep
Error models	learnErrors	learn-errors
Denoising	dada	dada
Merging	mergePairs	merge-pairs
Chimera removal	removeBimeraDenovo	remove-bimera-denovo
RDP taxonomic classifier	assignTaxonomy + assignSpecies	assign-taxonomy + assign-species
Merging sequence tables	mergeSequenceTables	make-sequence-table (accepts multiple inputs)
Making sequence tables from multiple inputs	makeSequenceTable	make-sequence-table

• Current error models:
  • loessErrfun
  • PacBioErrfun
  • noqualErrfun
  • makeBinnedErrfun
  • Custom error models in R and/or Python - Experimental, tested
• Other functionality:
  • Helper functions to convert sequence tables to TSV or FASTA (seq-table-to-fasta, seq-table-to-tsv)
  • Helper function to convert taxonomic output to TSV (tax-to-tsv)
  • Basic scripts and examples for comparing results between runs to trace differences
  • Experimental sub-sampling function for input FASTQ (sample) and related error model function (errors-from-sample) that mirrors learn-errors (can be used for bootstrapping)
  • Intermediate outputs (in JSON) - can be evaluated for debugging purposes or for plotting in R, Python, etc.
  • Dedicated Docker builds available
• In progress
  • Summary FASTQ metrics and plots

Building

Requires a recent stable Rust toolchain (rustup.rs).

cargo build --release
# binary at target/release/dada2-rs

For the native (-C target-cpu=native) build and Docker, see Installation.

Subcommands

Subcommand	Description
filter-and-trim	Filter and trim FASTQ reads (mirrors filterAndTrim)
derep	Dereplicate a FASTQ file
sample	Dereplicate and subsample FASTQ files, one JSON per sample
errors-from-sample	Learn error model from derep JSON files
learn-errors	Learn error model directly from FASTQ files
dada	Denoise a sample using a learned error model
merge-pairs	Merge denoised forward and reverse reads
make-sequence-table	Build a sample × sequence count table
remove-bimera-denovo	Remove chimeric sequences
summary	Per-position quality metrics from a FASTQ

Run dada2-rs <subcommand> --help for full parameter documentation.

Usage & walkthroughs

End-to-end examples live in the documentation:

• Illumina MiSeq walkthrough (paired-end)
• PacBio HiFi walkthrough (single-end, primer removal + PacBio-tuned params)

Run dada2-rs <subcommand> --help for the full parameter set of any step.

Benchmarks & comparison with R DADA2

Performance is benchmarked head-to-head against R DADA2 with the harness in comparison/benchmark/. See the docs for the tooling, metrics, and results:

• Performance — tooling & metrics (the harness, cores/cpu_s/peak-RSS, scaling sweeps, built-in logs, concordance checks)
• Benchmark results (head-to-head scorecards by platform and pooling mode)

AI Assistance Disclosure

See the project overview for the project's origins and goals, which are directly relevant here.

This tool was written with the assistance of AI coding agents, specifically Claude Code, using Sonnet and Opus LLMs. All commits using AI assistance are openly noted.

Correctness is validated by comparing output against DADA2 v1.36 on a suite of real sequencing datasets - not by manual code review alone. AI generated the implementation; humans defined the validation criteria, made some key coding updates, and verified results.

Citation

dada2-rs is a reimplementation; if you use it, please cite the original work that describes the algorithm (see CITATION.cff):

• Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods. 2016;13:581-583. doi:10.1038/nmeth.3869
• Rosen MJ, Callahan BJ, Fisher DS, Holmes SP. Denoising PCR-amplified metagenome data. BMC Bioinformatics. 2012;13:283. doi:10.1186/1471-2105-13-283