bio-ecological-genomics-edna-metabarcoding

What this skill does

## Version Compatibility

Reference examples tested with: DADA2 1.30+, cutadapt 4.7+, OBITools3 (Python 3), decontam 1.20+, microDecon 1.0+, occumb 1.0+, vsearch 2.27+, swarm 3.1+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# eDNA Metabarcoding

**"Process eDNA samples to identify species present"** -> Trim primers, denoise to ASVs (or cluster to OTUs), detect chimeras, assign taxonomy, filter contamination with negative controls AND DNA concentration, decompose tag-jumping artifacts, and quantify detection uncertainty via site-occupancy modeling. For the foundational eDNA-for-wildlife review, see Bohmann et al. 2014 *Trends Ecol Evol* 29:358-367.
- CLI: `cutadapt` for primer removal (linked-adapter mode)
- R: `dada2::filterAndTrim()` -> `dada()` -> `assignTaxonomy()` for ASV pipeline
- CLI: `obi stats` / `obi clean` / `obi ecotag` for OBITools3 (NOTE: v3 plural commands)
- R: `decontam::isContaminant()` for contamination screening
- R: `occumb::occumb()` for detection-corrected occurrence

## The Single Most Important Modern Insight -- Read Counts Are NOT Abundance

Elbrecht & Leese 2015 *PLoS One* 10:e0130324 and Lamb et al. 2019 *Mol Ecol* 28:420-430 (meta-analysis) established that metabarcoding read counts have weak-to-moderate, taxon-specific, NONLINEAR correlation with biomass or DNA input. Primer-binding bias dominates; PCR replicates introduce stochasticity. **Reporting read counts as abundance without mock-community calibration is malpractice.** Modern practice: report PRESENCE/ABSENCE or relative abundance with explicit calibration; use multiple PCR replicates; apply site-occupancy models for detection correction.

A second cornerstone: the ASV-vs-OTU debate is taxon-specific, not universal. Callahan, McMurdie, Holmes 2017 *ISME J* 11:2639-2643 argued ASVs replace OTUs because modern denoising resolves single-nucleotide differences. Schloss 2021 *mSphere* 6:e00191-21 showed that for bacterial 16S with 1-15 intra-genomic rRNA copies, a single E. coli strain produces ~7 distinct ASVs, splitting bacterial genomes across artificial clusters. **For COI metazoan metabarcoding, ASVs (DADA2/UNOISE3) are recommended; for bacterial 16S, ASVs inflate alpha-diversity and OTUs may be appropriate.**

A third: **decontam (Davis 2018) is a SCREENING tool, not a deterministic classifier.** It flags candidates; biological plausibility check is required before deletion. The default `threshold=0.1` over-flags in low-biomass data.

## Algorithmic Taxonomy

| Method | Output | Strength | Fails when |
|--------|--------|----------|------------|
| DADA2 | Single-nucleotide ASVs | High resolution; learned error model; standard for COI/12S/18S/fungal-ITS | Small datasets (< 100 samples) for error learning; multi-copy bacterial rRNA |
| UNOISE3 (USEARCH/VSEARCH; Edgar 2016) | zOTUs (essentially ASVs) | Fast; algorithmic simplicity | Limited Linux/Mac binary distribution under license |
| Swarm v2 `-d 1 --fastidious` (Mahé 2015) | Abundance-weighted single-linkage OTUs | Modern OTU pipeline; better than legacy 97% UCLUST | OTUs by design (not single-nt resolution) |
| 97% UCLUST | Classical OTUs | Legacy familiarity | Biologically arbitrary threshold; supersedes by DADA2/swarm |
| VSEARCH global pairwise | Taxonomic assignment via best-hit | Fast, transparent, no training | Conservative; mis-assigns sister species when ref incomplete |
| Naive Bayes (q2-feature-classifier, RDP) | Probabilistic taxonomic assignment | Probabilistic confidence; standard for 16S | Confidence values are scikit-learn calibrated, not true probabilities (Bokulich 2018) |
| SINTAX (Edgar) | Bootstrap-supported taxonomy | Fast; no training | Less accurate than Naive Bayes for divergent sequences |
| LCA (BASTA, MEGAN-LCA) | Lowest common ancestor of multiple hits | Conservative; never over-confident | Can over-merge to high taxonomic ranks |
| Phylogenetic placement (EPA-ng + gappa) | Position on reference tree | Most rigorous; phylogenetically explicit | 10-100x slower; emerging not yet standard |
| decontam | Flagged contaminant candidates | Statistical screening of negative controls and DNA concentration patterns | Output is screening, not classification; needs biological-plausibility check |
| UCHIME3 (in DADA2/VSEARCH) | Chimera detection | Standard for de novo chimera removal | Some divergent chimeras escape |

## Decision Tree by Scenario

| Scenario | Recommended approach | Why |
|----------|---------------------|-----|
| Metazoan COI metabarcoding (water, gut content) | mlCOIintF/jgHCO2198 (Leray 2013) primers; DADA2 ASVs | Standard primer set; ASVs preserve single-nt resolution |
| Fish eDNA from water | MiFish-U/E (Miya 2015) 12S primers; DADA2 ASVs | Dominant eDNA fish marker globally |
| Freshwater macroinvertebrate bioassessment | BF1/BR1 freshwater-optimized COI primers | Higher primer-binding inclusivity for aquatic insects |
| Bacterial community 16S | 515F/806R (V4) Parada modified; ASVs OR Swarm v2 | Schloss 2021 caveat applies; ASVs may oversplit multi-copy rRNA |
| Fungal community ITS | ITS2 primers; DADA2 or UNITE pipeline | UNITE is curated for fungal ITS |
| Plant community DNA | trnL P6 loop (Taberlet 2007) for degraded DNA | Robust to degradation |
| Deciding ASV vs OTU | ASVs for COI/12S/18S/fungi; OTU consideration for 16S with multi-copy concern | Taxon-specific |
| NovaSeq library (patterned flow cell) | Heavier tag-jumping correction; expect 10x higher rates than MiSeq | Patterned-cell index hopping |
| Low-biomass eDNA (deep ocean, ancient) | decontam frequency + prevalence methods; explicit reagent-contamination check | Reagent contamination dominates |
| Quantitative comparison across samples | Mock-community calibration BEFORE reporting read counts | Without mock, read counts are biased estimators of biomass |
| Detection probability with replication | Site-occupancy models (occumb, eDNAoccupancy; Ficetola 2015) | Read counts alone underestimate occurrence; replicates correct |
| Taxonomic assignment for marker > 80% covered | Naive Bayes (q2-feature-classifier) | Probabilistic; well-supported |
| Taxonomic assignment for sparse reference | Phylogenetic placement (EPA-ng) | Robust to incomplete references |
| OBITools3 pipeline | `obi stats` (NOTE: plural), DMS-based, `.tar.gz` taxonomy | v3 syntax differs from v1 |

## Primer Trimming with cutadapt

**Goal:** Remove primer sequences while discarding reads that lack primers, before quality filtering.

**Approach:** Use cutadapt linked-adapter mode with marker-specific 5' and 3' primer pairs. `--discard-untrimmed` removes reads lacking expected primers; `min_overlap` prevents false primer detection in random sequence regions.

```bash
# COI metazoan (Leray mlCOIintF / jgHCO2198 -> 313 bp)
cutadapt -g 'GGWACWGGWTGAACWGTWTAYCCYCC;min_overlap=20' \
         -G 'TAIACYTCIGGRTGICCRAARAAYCA;min_overlap=20' \
         --discard-untrimmed --pair-filter=any \
         -o trimmed_R1.fastq.gz -p trimmed_R2.fastq.gz \
         raw_R1.fastq.gz raw_R2.fastq.gz

# Fish 12S (MiFish-U -> 163-185 bp)
cutadapt -g 'GTCGGTAAAACTCGTGCCAGC;min_overlap=18' \
         -G 'CATAGTGGGGTATCTAATCCCAGTTTG;min_overlap=18' \
         --discard-untrimmed --pair-filter=any \
         -o trimmed_R1.fastq.gz -p trimmed_R2.fastq.gz \
         raw_R1.fastq.gz raw_R2.fastq.gz

# Fungal ITS2
cutadapt -g 'GTGAATCATCGAATCTTTGAAC;min_overlap=18' \
         -G 'TCCTCCGCTTATTGATATGC;min_overlap=18' \
         --discard-untrimmed --pair-filter=any \
         -o trimmed_R1.fastq.gz -p trimmed_R2.fastq.gz \
         raw_R1.fastq.gz raw_R2.fastq.