Quickstart

:material-download: Download quickstart.py

Installation

Container (Docker / Apptainer)

The fastest way to get started — no compiler or conda environment required.

# Docker
docker pull ghcr.io/rec3141/papa2:latest
docker run -v $(pwd):/data ghcr.io/rec3141/papa2 python3 my_script.py

# Apptainer (HPC)
apptainer pull papa2.sif docker://ghcr.io/rec3141/papa2:latest
apptainer exec papa2.sif python3 my_script.py

---

Prerequisites

papa2 requires a compiled libpapa2.so shared library. Build it from source:

git clone https://github.com/rec3141/papa2.git
cd papa2

Build the C/C++ core

# Using the conda environment (recommended)
conda env create -f environment.yml
conda activate papa2-dev
make libpapa2.so

Or build directly with:

make libpapa2.so

Install the Python package

pip install -e .

Verify

import papa2
print(papa2.__version__)
# 0.1.0

---

Minimal Example

papa2 ships with two small test FASTQ files in tests/data/. This example runs the core denoising pipeline on them — no external data needed.

import papa2

# The bundled test files (forward reads from two 16S amplicon samples)
fwd_files = ["tests/data/sam1F.fastq.gz", "tests/data/sam2F.fastq.gz"]

# --- 1. Dereplicate ---
dereps = [papa2.derep_fastq(f, verbose=True) for f in fwd_files]
# Read 1500 reads, 896 unique sequences
# Read 1500 reads, 906 unique sequences

# --- 2. Learn error rates ---
err = papa2.learn_errors(fwd_files, verbose=True)
# 750000 total bases in 3000 reads from 2 samples ...

# --- 3. Denoise ---
dadas = papa2.dada(dereps, err=err, verbose=True)

# --- 4. Inspect results ---
for i, dd in enumerate(dadas):
    print(f"Sample {i+1}: {len(dd['denoised'])} ASVs, "
          f"{sum(dd['denoised'].values())} reads")

# --- 5. Build sequence table ---
seqtab = papa2.make_sequence_table(dadas)
print(f"Sequence table: {seqtab['table'].shape[0]} samples x {seqtab['table'].shape[1]} ASVs")

# --- 6. Visualise read tracking (Sankey diagram) ---
track = papa2.track_reads(dereps=dereps, dadas=dadas, seqtab=seqtab)
papa2.plot_sankey(track, output="read_tracking.html")
print("Sankey diagram saved to read_tracking.html")

Test data

The bundled files are small subsets (1500 reads each) of the same samples used in the DADA2 tutorial. For real analyses you'll typically have 10K–200K reads per sample.

---

Paired-End Example

For paired-end data, filter, dereplicate, and denoise forward and reverse reads separately, then merge:

import os
import papa2

# Forward and reverse FASTQ files (replace with your own paths)
fwd_files = ["sample1_R1.fastq.gz", "sample2_R1.fastq.gz"]
rev_files = ["sample1_R2.fastq.gz", "sample2_R2.fastq.gz"]

# Filter and trim
filt_fwd = [os.path.join("filtered", os.path.basename(f)) for f in fwd_files]
filt_rev = [os.path.join("filtered", os.path.basename(f)) for f in rev_files]

papa2.filter_and_trim(
    fwd_files, filt_fwd,
    rev=rev_files, filt_rev=filt_rev,
    trunc_len=(240, 200), max_ee=(2, 2), trunc_q=2,
    rm_phix=True, verbose=True,
)

# Learn errors from filtered reads
errF = papa2.learn_errors(filt_fwd, verbose=True)
errR = papa2.learn_errors(filt_rev, verbose=True)

# Dereplicate
derepFs = [papa2.derep_fastq(f) for f in filt_fwd]
derepRs = [papa2.derep_fastq(f) for f in filt_rev]

# Denoise
dadaFs = papa2.dada(derepFs, err=errF)
dadaRs = papa2.dada(derepRs, err=errR)

# Merge paired reads
mergers = [
    papa2.merge_pairs(dF, drF, dR, drR)
    for dF, drF, dR, drR in zip(dadaFs, derepFs, dadaRs, derepRs)
]

# Build sequence table and remove chimeras
seqtab = papa2.make_sequence_table(mergers)
seqtab_nochim = papa2.remove_bimera_denovo(seqtab, verbose=True)

print(f"Final: {seqtab_nochim['table'].shape[1]} ASVs across "
      f"{seqtab_nochim['table'].shape[0]} samples")

# Visualise read tracking
track = papa2.track_reads(
    dereps=derepFs, dadas=dadaFs, mergers=mergers,
    seqtab=seqtab, seqtab_nochim=seqtab_nochim,
)
papa2.plot_sankey(track, output="read_tracking.html")

---

Parallelism

Set DADA2_WORKERS to control parallel workers for multi-sample denoising:

export DADA2_WORKERS=8

Set OMP_NUM_THREADS=1 before importing papa2 for best multi-sample performance (avoids contention between Python-level and OpenMP parallelism).