bio-ecological-genomics-edna-metabarcoding
Processes environmental DNA metabarcoding data from raw amplicon reads to species occurrence tables using OBITools3, DADA2, and taxonomic assignment against BOLD, MIDORI2, or MitoFish databases. Handles COI, 12S, rbcL, and ITS barcode regions with primer removal, denoising, chimera detection, and contamination filtering via decontam. Includes occupancy modeling (occumb) for detection probability correction. Use when analyzing eDNA from water, soil, or bulk samples for biodiversity monitoring. Not for 16S human microbiome (see microbiome/amplicon-processing).
npx skills add BioTender-max/awesome-bio-agent-skills --skill edna-metabarcoding --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: DADA2 1.30+, cutadapt 4.4+ Before using code patterns, verify installed versions match. If versions differ: - 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 my eDNA samples to identify species"** → Transform raw amplicon reads (COI, 12S, rbcL, ITS) into species occurrence tables through primer removal, denoising (DADA2 ASVs), taxonomy assignment against reference databases (BOLD, MIDORI2), and contamination filtering with decontam. - CLI: `cutadapt` for primer removal, `obi` (OBITools3) for paired-end assembly - R: `dada2::filterAndTrim()` → `dada()` → `assignTaxonomy()` for ASV pipeline Processes environmental DNA amplicon reads into species occurrence tables with taxonomy assignment, contamination filtering, and occupancy modeling. ## Primer Removal with Cutadapt **Goal:** Remove amplicon primers from paired-end eDNA reads while discarding untrimmed
- Version Compatibility
- Primer Removal with Cutadapt
- OBITools3 Pipeline
- DADA2 Pipeline for eDNA
- Reference Databases
- Contamination Filtering
- decontam (frequency/prevalence method)
- microDecon for tag-jumping artifacts
- Occupancy Modeling with occumb
- Key Thresholds
- Related Skills
COI primers (mlCOIintF / jgHCO2198) cutadapt -g 'GGWACWGGWTGAACWGTWTAYCCYCC;min_overlap=20' \ raw_R1.fastq.gz raw_R2.fastq.gz cutadapt -g 'GTCGGTAAAACTCGTGCCAGC;min_overlap=18' \ ITS primers (ITS1F / ITS2) cutadapt -g 'CTTGGTCATTTAGAGGAAGTAA;min_overlap=18' \ Import paired FASTQ into OBITools3 DMS obi import --fastq-input raw_R1.fastq.gz EDNA/reads1 obi import --fastq-input raw_R2.fastq.gz EDNA/reads2 Paired-end alignment
What does the bio-ecological-genomics-edna-metabarcoding skill do?
Processes environmental DNA metabarcoding data from raw amplicon reads to species occurrence tables using OBITools3, DADA2, and taxonomic assignment against BOLD, MIDORI2, or MitoFish databases. Handles COI, 12S, rbcL, and ITS barcode regions with primer removal, denoising, chimera detection, and contamination filtering via decontam. Includes occupancy modeling (occumb) for detection probability correction. Use when analyzing eDNA from water, soil, or bulk samples for biodiversity monitoring. Not for 16S human microbiome (see microbiome/amplicon-processing).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill edna-metabarcoding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.