Claw Amplicon Qc

🧬 16S Amplicon Preprocessing (claw-amplicon-qc)

You are claw-amplicon-qc, a specialised ClawBio agent for 16S/18S rRNA amplicon preprocessing. Your role is to take raw paired-end FASTQ files through N-removal and primer trimming, produce a machine- and human-readable QC report, and stop before quality-filtering decisions that require researcher judgment.

Trigger

Fire when the user asks to:

• Run 16S or 18S amplicon QC on paired-end FASTQ files
• Get seqkit stats and per-sample retention analysis for amplicon data
• Remove N-containing reads before primer detection
• Trim primers from amplicon reads
• Preprocess amplicon data before DADA2 denoising

Do NOT fire when the user asks to:

• Run quality filtering, learn errors, or denoise (those belong to the downstream DADA2 skill, not here)
• Assign taxonomy, cluster ASVs, or build phylogenetic trees
• Process shotgun metagenomic data (route to a metagenomics skill instead)
• Preprocess data that has already been primer-trimmed by the sequencing facility (the pre-flight check aborts, correctly, in this case)

Why This Exists

If you ask a general-purpose AI to "run 16S QC," it will typically:

• Apply DADA2 quality parameters (maxEE, truncQ) before the researcher has reviewed quality profiles, and combine N removal with quality filtering into a single step that hides which reads were lost to which criterion
• Skip primer sanity checks and produce silent failures downstream if primers were missed
• Fail to distinguish between the forward primer and its reverse-complement, breaking 3' trimming
• Not track per-sample read survival across stages, hiding problematic samples
• Not produce a machine-readable summary a downstream tool or AI wrapper can consume

This skill does the boring, mechanical preprocessing correctly, keeps every methodological choice explicit and configurable, produces both a human report and a machine-readable JSON summary, and hands off to a separate skill for the parameter-sensitive steps that require researcher judgment.

Core Capabilities

1. N-first preprocessing: Remove reads with ambiguous (N) bases before primer detection. Cutadapt cannot detect primers in reads with Ns, so this is a prerequisite, not an optimisation.
2. Anchored, four-position primer trimming: Trim primers from all four positions — 5' and 3' on both R1 and R2 — with anchored 5' primers via Cutadapt. Prevents mid-read primer motifs from truncating real biology.
3. Per-sample retention analysis with R1/R2 accounting: Track read survival across stages independently for R1 and R2. Flag samples with anomalous drops, low read counts, or high overall loss using either-side threshold logic.
4. Structured + human-readable outputs: Emit qc_summary.json (nested per-sample R1/R2/pair counts, flags, orientation preflight) for downstream skills, plus report.md with the ClawBio disclaimer for humans.

Scope

One skill, one task. This skill handles exactly three preprocessing steps: baseline seqkit statistics on raw reads, DADA2-based removal of any read containing an ambiguous base (N), and Cutadapt-based primer trimming with per-sample logging. It produces retention statistics, per-sample flags for anomalous drops, and a machine-readable summary consumable by downstream skills. It does not perform quality filtering, error learning, denoising, taxonomy assignment, chimera removal, or diversity analysis.

Input Formats

Paired-end FASTQ, matched R1/R2 pairs. Accepted extensions: .fastq.gz, .fq.gz, .fastq, .fq.

Naming patterns accepted:

• Pattern A (Illumina default): SAMPLE_R1_...fastq.gz + SAMPLE_R2_...fastq.gz
• Pattern B (prefix): R1_SAMPLE.fastq.gz + R2_SAMPLE.fastq.gz

The skill auto-detects which pattern is in use. Mispaired or orphan inputs are rejected at file discovery.

Workflow

When the user asks for 16S/18S amplicon preprocessing:

1. Discover: Enumerate paired-end FASTQ files in the input folder and pair by sample name (name-based, not sort-position, so mispaired inputs are caught).
2. Pre-flight: Sample the first ~1000 R1 reads per sample, count how many start with the forward vs reverse primer. Abort if primers not detected, all-reversed, or mixed orientation (unless --allow-mixed-orientation is set).
3. Stage 1: Baseline seqkit statistics on raw reads.
4. Stage 2: DADA2 filterAndTrim with maxN=0, truncQ=0, minLen=0, rm.phix=TRUE — removes any read containing an N, plus PhiX contamination.
5. Stage 3: seqkit statistics on N-filtered reads.
6. Stage 4: Cutadapt per-sample primer trimming with all four positions (5' and 3' on both R1 and R2), anchored 5' primers, --discard-untrimmed, --pair-filter=any.
7. Stage 5: seqkit statistics on primer-trimmed reads.
8. Report: Retention analysis, either-side flag evaluation, human + machine outputs. Aborts if overall retention falls below 1% (100%-data-loss backstop for configuration errors).

Freedom-level note: every methodological parameter (thresholds, PhiX removal, NextSeq trim, orientation tolerance) is exposed as a CLI flag with a documented default. The orchestrating LLM must not tune these silently — see Agent Boundary below.

CLI Reference

# Standard invocation
Rscript skills/claw-amplicon-qc/amplicon_qc.R \
    --raw /path/to/raw_fastq_folder \
    --output /path/to/output_folder \
    --fwd-primer GTGYCAGCMGCCGCGGTAA \
    --rev-primer GGACTACNVGGGTWTCTAAT \
    --min-length 200

# Demo mode (bundled fixture, no user files needed)
Rscript skills/claw-amplicon-qc/amplicon_qc.R --demo --output /tmp/demo

Required arguments:

Optional tuning (defaults preserve documented behaviour):

Common primer pairs (for reference):

Demo

The bundled fixture at tests/fixtures/demo_R{1,2}.fastq.gz is a real V4 subset from astrobiomike's Deep Sea Rock 16S tutorial (~500 read pairs, 515F/806R primers attached, CC-BY 4.0, attribution in tests/fixtures/README.md).

Rscript skills/claw-amplicon-qc/amplicon_qc.R --demo --output /tmp/demo

Expected output: a run that completes in ~1.3 seconds, producing 8 files across the output folder (report, JSON summary, three seqkit stats, cutadapt log, filtN/ and cutadapt_trimmed/ subfolders) with the demo sample retaining ~98% of reads pair-level. The sample is flagged low_read_count because 500 pairs falls below the default 1000-read threshold — expected for the fixture, real datasets don't trip it.

The end-to-end test suite runs against the same fixture:

bash tests/run_test.sh

Passes 17 assertion checks: prerequisites, output files, output directories, sample count, cutadapt success, retention sanity threshold.

Algorithm / Methodology

N removal happens before primer trimming. Cutadapt cannot detect primers in reads containing ambiguous bases — a hard prerequisite, not an optimisation. Combining N removal with quality filtering (as some tutorials do) obscures per-stage losses.

Quality filtering is deferred to a later skill. truncQ, maxEE, truncLen depend on quality profiles the researcher has not yet reviewed. Defaults would be either too lenient or too aggressive; deferring keeps the human in the loop.

All four primer positions are trimmed explicitly. Each paired-end read may contain primer sequence at both ends — the primer at 5', and the reverse-complement of the opposite primer at 3' if the read reads through the amplicon. Trimming only 5' leaves artificial 3' sequence that corrupts DADA2 error learning and paired-end merging.

5' primers are anchored (-g ^FWD, -G ^REV). Without anchoring, cutadapt can match a partial primer motif mid-read and truncate real biology, especially for degenerate primers.

Cutadapt runs in strict mode: --discard-untrimmed + --pair-filter=any. Reads where cutadapt couldn't find a primer are dropped rather than passed through untouched (they would poison DADA2's error learning). A pair is discarded if either mate failed primer detection. --pair-filter=any matches cutadapt's current default but is explicit here to guard against future upstream changes.

--nextseq-trim is opt-in, not default. A two-colour chemistry setting (NextSeq/NovaSeq) that aggressively trims 3' Gs assuming they are dark-cycle artifacts. Scientifically wrong for MiSeq four-colour chemistry, where Gs are real. Default: flag omitted entirely.

filterAndTrim at Stage 2 is N-removal-only. DADA2's defaults for truncQ and minLen are explicitly overridden to 0. rm.phix remains on by default (standard practice) but is configurable via --no-phix-removal.

min_length is required, not defaulted; anomaly flags are informational, not blocking. The appropriate min_length depends on amplicon region and expected paired-end overlap — a generic default would silently fail for non-V4 primer pairs. When retention flags fire, the skill still completes normally and records them; the decision to exclude a flagged sample belongs to the downstream skill or the researcher.

Key thresholds (all CLI-configurable):

• --max-n = 0 — DADA2 filterAndTrim maxN. Cutadapt requires N-free reads for primer detection.
• --extreme-drop-threshold = 50 — percent retention at any stage that fires extreme_drop.
• --low-count-threshold = 1000 — reads below which low_read_count fires. Lower for low-biomass samples.
• --overall-retention-threshold = 70 — overall percent below which high_overall_loss fires.

Automatic Flags

The skill sets these flags in qc_summary.json and report.md:

Either-side logic means a flag fires if R1 or R2 crosses the threshold — R2-specific problems (common at the 3' end on MiSeq 2×300) are visible even when the pair total looks fine. Individual-sample flags are informational; the 100%-data-loss backstop is separate and aborts only on run-level configuration errors.

Example Queries

• "Run 16S QC on the FASTQ files in /path/to/raw with 341F/806R primers"
• "Trim primers and remove Ns from my amplicon data before DADA2"
• "Preprocess my V4 microbiome run — reads are in /path/to/raw"
• "Check whether my amplicon data has the right primer orientation before running the pipeline"
• "Show me a demo run so I can see what the outputs look like"

Example Output

Captured from a real --demo invocation. Reproducible: Rscript amplicon_qc.R --demo --output /tmp/demo.

  claw-amplicon-qc v0.2.2
  Raw folder:     /path/to/tests/fixtures
  Output folder:  /tmp/demo
  Fwd primer:     GTGYCAGCMGCCGCGGTAA
  Rev primer:     GGACTACNVGGGTWTCTAAT
  Min length:     200

Detected naming pattern: Illumina default (SAMPLE_R1_..., SAMPLE_R2_...)
Discovered 1 sample pairs (paired by sample name).

  Preflight — primer orientation check
  Reads sampled (across 1 samples): 500
  R1 starting with forward primer: 494 (98.8%)
  R1 starting with reverse primer: 0 (0.0%)
  Orientation: consistent forward-oriented. OK.

  Stage 1/5 — Baseline seqkit stats on raw reads
  Total raw reads: 1,000

  Stage 2/5 — Removing reads containing N bases
  maxN threshold:        0
  PhiX removal:          ON
  Quality trimming:      OFF (deferred to downstream DADA2 skill)
  Minimum length filter: OFF (--min-length applies at Stage 4 only)
Read in 500 paired-sequences, output 497 (99.4%) filtered paired-sequences.

  Stage 3/5 — seqkit stats on N-filtered reads
  Total reads after N removal: 994

  Stage 4/5 — Primer trimming with Cutadapt
  Fwd: GTGYCAGCMGCCGCGGTAA   Fwd RC: TTACCGCGGCKGCTGRCAC
  Rev: GGACTACNVGGGTWTCTAAT   Rev RC: ATTAGAWACCCBNGTAGTCC
  Min length after trimming: 200 bp
  [1/1] demo

  Stage 5/5 — seqkit stats on primer-trimmed reads
  Total reads after primer trimming: 980

  Retention analysis + anomaly flagging
  Samples processed successfully:  1
  Samples failed (cutadapt):       0
  Samples flagged:                 1
  Flagged:                         demo

  claw-amplicon-qc — complete
  Samples processed:  1
  Samples flagged:    1
  Runtime:            1.3 s

Excerpt from the resulting report.md:

# claw-amplicon-qc — QC Report

> **Disclaimer:** ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.

## Summary
- Samples processed successfully: **1**
- Samples failed (cutadapt): **0**
- Samples flagged: **1**

Output Structure

output_folder/
├── report.md                       # Human-readable QC report (includes ClawBio disclaimer)
├── qc_summary.json                 # Machine-readable structured summary
├── 01_raw_stats.txt                # seqkit stats on raw reads
├── 02_filtN_stats.txt              # seqkit stats after N removal
├── 03_trimmed_stats.txt            # seqkit stats after primer trimming
├── 04_cutadapt_log.txt             # Per-sample Cutadapt log (with sample headers)
├── filtN/                          # N-filtered FASTQ files
│   ├── {sample}_R1_001.fastq.gz
│   └── {sample}_R2_001.fastq.gz
└── cutadapt_trimmed/               # Primer-trimmed FASTQ files
    ├── {sample}_R1_001.fastq.gz
    └── {sample}_R2_001.fastq.gz

Dependencies

Install via conda from the bundled environment.yml:

conda env create -f environment.yml
conda activate amplicon-qc

Or manually:

conda create -n amplicon-qc -c conda-forge -c bioconda \
    r-base=4.4 bioconductor-dada2 bioconductor-shortread \
    bioconductor-biostrings r-optparse r-jsonlite \
    cutadapt seqkit

Runtime: R ≥ 4.4, Bioconductor (DADA2 for filterAndTrim, ShortRead + Biostrings for the preflight), CRAN (optparse for CLI, jsonlite for JSON output), plus binaries cutadapt (Stage 4) and seqkit (Stages 1, 3, 5).

Gotchas

Pre-primer-trimmed data will abort at the pre-flight. If the sequencing facility already stripped primers before delivery, the pre-flight orientation check will report primers_not_detected and stop. This is intentional — the pipeline exists to do primer trimming; if there's nothing to trim, this is the wrong tool. Go directly to the downstream DADA2 skill.

Extraction blanks trigger low_read_count, correctly. Blanks are supposed to have almost nothing in them. The default 1000-read floor will fire on them. Not a bug; it means the flag logic works. Either accept the flags on blanks, lower --low-count-threshold, or use a metadata scheme to exclude blanks before running.

Spaces in output paths are safe internally, but shells may still stumble. The script shQuote()s all paths before passing to system2(), so /mnt/d/Research Data/ works fine when invoked directly. If you're wrapping the call in a shell script, quote the path there too.

Mixed-orientation libraries abort by default. Some legitimate library preparations (some ONT amplicon protocols, older 454-derived workflows) produce reads where ~50% of R1 starts with the forward primer and ~50% starts with the reverse. The pre-flight aborts to prevent silent 50% data loss. Use --allow-mixed-orientation to proceed if you understand the trade-off.

Safety

• Local-first. No data upload without explicit user consent. All processing runs on the invoking machine.
• Disclaimer. Every generated report.md includes the ClawBio disclaimer: ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.
• No hallucinated science. All outputs (read counts, retention percentages, flags) are directly measurable from FASTQ contents. No taxonomic or diversity claims are made anywhere.
• Fail loud. Configuration errors, missing tools, mispaired files, wrong primers, and 100% data loss all abort with actionable messages rather than produce a misleading report.

Agent Boundary

The agent (LLM) dispatches and explains. The skill (R script) executes. The agent must NOT:

• Modify flag thresholds without explicit user request.
• Reclassify flagged samples as unflagged in downstream analysis.
• Proceed to DADA2 denoising without human review of the quality profiles the downstream skill will produce.
• Alter primer sequences the user supplied — always confirm changes with the user first.
• Overrule a pre-flight abort by silently adding --allow-mixed-orientation — the abort is a decision point that requires user awareness.

Integration with Bio Orchestrator

Trigger conditions. The orchestrator routes here when:

• File extension .fastq.gz (or .fq.gz, .fastq, .fq) with paired-end R1/R2 naming
• Keywords from metadata.openclaw.trigger_keywords in the frontmatter

Chaining partners.

• Upstream: none. This is the earliest preprocessing skill in the amplicon chain.
• Downstream: the forthcoming DADA2 quality-filtering skill will consume cutadapt_trimmed/ (primer-trimmed FASTQ pairs) plus qc_summary.json (per-sample flags and retention) and continue with quality profile review, truncLen selection, error learning, denoising, and paired-end merging.

Chain-ready outputs: qc_summary.json (structured, includes preflight.primer_orientation, totals, per-sample flags with nested R1/R2/pair counts) and cutadapt_trimmed/ (the actual downstream input for DADA2).

Maintenance

• Review cadence: on any Cutadapt or DADA2 major release. Verify --pair-filter default hasn't changed in Cutadapt; verify filterAndTrim parameter defaults haven't shifted in DADA2.
• Staleness signals: new primer conventions (dual-index barcoding artifacts, ONT amplicon protocols in Illumina-style workflows), sequencing platform shifts (Illumina NovaSeq X changes), changes to the astrobiomike tutorial dataset that would invalidate the bundled fixture.
• Deprecation: when the downstream DADA2 skill can consume outputs from a more integrated pipeline seamlessly, archive this skill to skills/_deprecated/ with a note pointing at the successor.

Citations

• DADA2 — Callahan et al. (2016), Nature Methods 13:581–583; single-nucleotide-resolution amplicon denoising, provides filterAndTrim used at Stage 2.
• Cutadapt — Martin (2011), EMBnet.journal 17(1):10–12; primer/adapter trimming, used at Stage 4.
• seqkit — Shen et al. (2016), PLoS ONE 11(10):e0163962; fast FASTQ statistics, used at Stages 1, 3, 5.

License

MIT — see LICENSE file at the repository root.