pydada2 0.1.0

Pure-Python DADA2 — exact amplicon sequence variant inference from amplicon sequencing data.

dada2-py

A pure-Python re-implementation of DADA2 (Callahan et al., Nature Methods 2016) for exact amplicon sequence variant (ASV) inference from amplicon sequencing data.

• AnnData / pandas-friendly — drop-in for downstream microbiome analysis
• No rpy2, no R install, no Bioconductor dependency
• Same API as the R dada2 workflow (filter_and_trim → learn_errors → dada → merge_pairs → make_sequence_table → remove_bimera_denovo → assign_taxonomy)
• ASV-level identity matches R DADA2 on the canonical MiSeq SOP test data

This is a standalone mirror of the canonical implementation that lives in omicverse. All algorithmic work is developed upstream in omicverse and synced here for users who want DADA2 without the full omicverse stack.

Install

pip install pydada2

Quick-start

from pydada2 import (
    filter_and_trim, learn_errors, dada,
    merge_pairs, make_sequence_table,
    remove_bimera_denovo, assign_taxonomy,
)

# 1) Quality filter + trim
filter_and_trim(
    fwd="raw/F.fastq.gz", filt="filt/F.fastq.gz",
    rev="raw/R.fastq.gz", filt_rev="filt/R.fastq.gz",
    trunc_len=(240, 160), max_ee=(2, 2), trunc_q=2,
)

# 2) Learn the error model
errF = learn_errors("filt/F.fastq.gz")
errR = learn_errors("filt/R.fastq.gz")

# 3) Run the divisive amplicon denoising algorithm
ddF = dada("filt/F.fastq.gz", err=errF)
ddR = dada("filt/R.fastq.gz", err=errR)

# 4) Merge paired-end reads
mergers = merge_pairs(ddF, "filt/F.fastq.gz", ddR, "filt/R.fastq.gz")

# 5) Sample × sequence table
seqtab = make_sequence_table(mergers)

# 6) Chimera removal
seqtab_nochim = remove_bimera_denovo(seqtab, method="consensus")

# 7) Taxonomy assignment
taxa = assign_taxonomy(seqtab_nochim, ref_fasta="silva_nr99.fa.gz")

What's included

Module	Function	Purpose
pydada2.filter	filter_and_trim, fastq_filter	Quality filtering + trimming
pydada2.io	derep_fastq, get_uniques, get_sequences	FASTQ I/O + dereplication
pydada2.errors	learn_errors, loess_errfun, inflate_err	Error-rate model
pydada2.align	nwalign, nwhamming	Needleman-Wunsch ends-free alignment
pydada2.kmers	kmer_dist, kord_dist	k-mer distance pre-screen
pydada2.dada	dada	The divisive amplicon denoising algorithm
pydada2.paired	merge_pairs	Paired-end merging
pydada2.seqtab	make_sequence_table, merge_sequence_tables, collapse_no_mismatch	ASV table assembly
pydada2.chimeras	is_bimera_denovo, remove_bimera_denovo	Chimera detection
pydada2.taxonomy	assign_taxonomy, assign_species, add_species	RDP naive Bayes + exact-match species

Relationship to R DADA2

This is a Python port of the canonical R/C++ DADA2 package (benjjneb/dada2). All algorithmic behaviour is checked against the R reference on the MiSeq SOP test data (see tests/test_r_parity.py). The R reference is invoked from /scratch/users/steorra/env/CMAP during testing.

Citation

If you use this package, please cite the original DADA2 paper:

Callahan, B.J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13, 581–583 (2016).

and acknowledge omicverse / this repo for the Python port.

License

LGPL-2 — matches upstream R DADA2.