git:20260628.9edbf07 to git:20260721.2758b4c

---

name: ocean-genomics

category: science

description: >

Comprehensive bioinformatics and ocean genomics skill for eDNA metabarcoding, metagenomics,

protein structure prediction, and marine biodiversity analysis. Use when: (1) Working with DNA/RNA

sequences (FASTQ, FASTA, SAM/BAM, VCF files), (2) Running BLAST, QIIME2, Kraken2, or Nextflow

environmental DNA (eDNA) from marine samples, (5) Querying marine databases (OBIS, BOLD, DOO,

MCP servers for bioinformatics (gget-mcp, bio-mcp), (8) Building agentic bioinformatics workflows,

(9) Ocean biodiversity research, deep-sea species discovery, extremophile enzyme characterization,

(10) Any task involving genomics, proteomics, transcriptomics, or computational biology.

---

# Ocean Genomics

Agentic bioinformatics skill for ocean genomics — from eDNA water sampling to species identification, protein structure prediction, and scientific insight publication.

## Quick Start

### 1. Set Up MCP Integration

Install gget-MCP for immediate access to BLAST, AlphaFold, sequence retrieval, and 9 other genomics tools:

```json

// Add to .claude/settings.json → mcpServers

{

"gget-mcp": {

"command": "uvx",

"args": ["--from", "gget-mcp@latest", "stdio"]

}

}

```

### 2. Install CLI Tools

```bash

# Core tools

brew install blast mmseqs2 samtools minimap2 nextflow

pip install biopython biotite gget

# Nanopore basecalling (runs on Apple Silicon)

brew install nanoporetech/dorado/dorado

# eDNA pipeline (conda recommended)

conda create -n edna -c bioconda -c conda-forge qiime2 kraken2 fastp fastqc

```

### 3. Install Companion Skills

```bash

npx skills add adaptyvbio/protein-design-skills@alphafold -g -y

npx skills add anthropics/life-sciences@nextflow-development -g -y

npx skills add gptomics/bioskills@bioskills -g -y

npx skills add davila7/claude-code-templates@gget -g -y

```

## Core Workflows

### Workflow A: eDNA Species Inventory

**Trigger**: User has sequencing data from marine water samples and needs species identification.

```

Input: FASTQ files (amplicon sequencing of CO1/16S/18S marker gene)

1. Quality control

→ fastp: trim adapters, filter low-quality reads

→ FastQC + MultiQC: generate QC reports

2. Denoise and classify

→ QIIME2 import → DADA2 denoise → ASV table

→ Taxonomy: classify-sklearn against BOLD (CO1) or SILVA (16S/18S)

3. Diversity analysis

→ Alpha diversity: Shannon, Simpson, Chao1

→ Beta diversity: Bray-Curtis, PCoA ordination

→ Rarefaction curves

4. Species report

→ Taxa count table (species × samples)

→ Novel species flags (CO1 divergence >3% from nearest reference)

→ Upload-ready format for OBIS

Output: Species inventory, diversity metrics, novel species candidates

```

**Nextflow shortcut**: `nextflow run nf-core/ampliseq --input samplesheet.csv --FW_primer <fwd> --RV_primer <rev>`

### Workflow B: Metagenomic Community Profiling

**Trigger**: User has shotgun metagenomic data and needs taxonomic + functional profiles.

```

Input: FASTQ files (shotgun whole-metagenome sequencing)

1. QC → fastp

2. Taxonomic classification → Kraken2 + Bracken (abundance estimation)

3. Functional profiling → HUMAnN3 (metabolic pathways)

4. Assembly (optional) → SPAdes/MEGAHIT → QUAST quality check

5. Binning (optional) → MetaBAT2 → CheckM quality → MAG annotation

Output: Taxonomic profiles, functional pathways, metagenome-assembled genomes (MAGs)

```

**Nextflow shortcut**: `nextflow run nf-core/mag --input samplesheet.csv --assembly_type megahit`

### Workflow C: Novel Protein Characterization

**Trigger**: User has protein sequences (from metagenomics or genome annotation) and needs structure/function prediction.

```

Input: Protein sequences (FASTA)

1. Homology search

→ BLAST/MMseqs2 against UniProt, GenBank nr

→ If match found: annotate function from homolog

2. Structure prediction (if novel / no homolog)

→ Check confidence: pLDDT >70 = reliable fold

3. Structural search

→ Foldseek against AlphaFold DB + PDB

→ Find structural homologs even without sequence similarity

4. Functional annotation

→ Domain analysis: InterPro scan

→ Pathway mapping: gget enrichr

→ Interaction networks: STRING database

5. Report

→ Structure visualization (PDB files)

→ Functional predictions with confidence scores

→ Comparison to known extremophile enzymes

Output: 3D structures, functional annotations, drug/enzyme candidates

```

### Workflow D: Variant Effect Analysis

**Trigger**: User wants to understand impact of mutations in marine organism genomes.

```

Input: DNA sequences + variants of interest

1. Score variants with Evo 2

→ Zero-shot pathogenicity prediction

→ No fine-tuning needed — works across all species

2. Structural impact (if protein-coding)

→ Translate → AlphaFold → compare wild-type vs mutant structure

→ Stability prediction (ΔΔG estimation)

3. Conservation analysis

→ Multiple sequence alignment (MUSCLE/MAFFT)

→ Conservation scores across species

Output: Variant effect scores, structural impact, conservation context

```

## Tool Reference

### Sequence Analysis

| Task | Tool | Command |

|------|------|---------|

| Nucleotide BLAST | BLAST+ | `blastn -query input.fasta -db nt -evalue 1e-6 -outfmt 6` |

| Protein BLAST | BLAST+ | `blastp -query proteins.fasta -db nr -evalue 1e-10` |

| Fast homology search | MMseqs2 | `mmseqs search queryDB targetDB resultDB tmp` |

| Short read alignment | BWA | `bwa mem ref.fa reads.fq \| samtools sort -o out.bam` |

| Long read alignment | Minimap2 | `minimap2 -ax map-ont ref.fa reads.fq \| samtools sort -o out.bam` |

| BAM statistics | SAMtools | `samtools flagstat aligned.bam` |

| Variant calling | bcftools | `bcftools mpileup -f ref.fa in.bam \| bcftools call -mv` |

### eDNA & Taxonomy

| Task | Tool | Command |

|------|------|---------|

| Amplicon denoising | QIIME2/DADA2 | `qiime dada2 denoise-paired ...` |

| Taxonomic classification | Kraken2 | `kraken2 --db standard --paired R1.fq R2.fq --report report.txt` |

| Abundance estimation | Bracken | `bracken -d db -i report.txt -o output.txt -r 150 -l S` |

| Basecalling (Nanopore) | Dorado | `dorado basecaller sup pod5_dir/ > calls.bam` |

### Structure Prediction

| Task | Tool | Command/API |

|------|------|-------------|

| High-accuracy structure | ColabFold | `colabfold_batch input.fasta output/` |

| Complex prediction | AlphaFold 3 | `python run_alphafold.py --input_dir inputs/` |

| Structural search | Foldseek | `foldseek easy-search query.pdb afdb result.m8 tmp` |

### Database Queries (via gget)

```python

import gget

gget.blast("ATCGATCG...") # BLAST search

gget.alphafold("MVLSPADKTNVK...") # Structure prediction

gget.seq("ENSG00000012048") # Fetch sequence

gget.info("BRCA1") # Gene metadata

gget.enrichr(["BRCA1", "TP53"]) # Pathway enrichment

gget.pdb("1BNA") # PDB structure

```

## File Format Reference

| Format | Extension | Content | When You See It |

|--------|-----------|---------|-----------------|

| **FASTQ** | `.fq`, `.fastq` | Raw reads + quality | Fresh from sequencer |

| **FASTA** | `.fa`, `.fasta`, `.fna` | Sequences (no quality) | References, assemblies |

| **SAM/BAM** | `.sam`, `.bam` | Aligned reads | After alignment |

| **VCF** | `.vcf`, `.vcf.gz` | Variant calls | After variant calling |

| **BED** | `.bed` | Genomic intervals | Feature coordinates |

| **GFF/GTF** | `.gff`, `.gtf` | Gene annotations | Gene models |

| **PDB/mmCIF** | `.pdb`, `.cif` | 3D protein structures | From AlphaFold/PDB |

| **BIOM** | `.biom` | Taxa count tables | From QIIME2 |

## Marine Databases

| Database | URL | Best For |

|----------|-----|----------|

| **OBIS** | obis.org | Marine species occurrence (100M+ records) |

| **BOLD** | boldsystems.org | CO1 barcode species ID |

| **DOO** | deepoceanomics.org | Deep-sea multi-omics (72 genomes, 1112 metagenomes) |

| **GenBank** | ncbi.nlm.nih.gov/genbank | All nucleotide sequences |

| **UniProt** | uniprot.org | Protein sequences + function |

| **AlphaFold DB** | alphafold.ebi.ac.uk | 200M+ predicted structures |

| **Tara Oceans** | fondationtaraocean.org | Global ocean microbiome |

| **WoRMS** | marinespecies.org | Marine species taxonomy |

## Foundation Models

| Model | Scale | Input | Best For | Install |

|-------|-------|-------|----------|---------|

| **Evo 2** | 40B params | DNA | Variant effects, genome design | `pip install evo2` |

| **AlphaFold 3** | N/A | Protein+DNA+RNA+ligand | High-accuracy complexes | Docker + weights request |

| **ColabFold** | AF2+ESMFold | Protein | Batch structure prediction | `pip install colabfold` |

| **ProGen3** | Billions | Conditioning | Novel protein generation | Proprietary |

For detailed specifications on each model, see [references/foundation-models.md](references/foundation-models.md).

## Detailed References

- **[references/tools-inventory.md](references/tools-inventory.md)** — Full CLI commands, install instructions, and usage examples for all 30+ tools

- **[references/marine-databases.md](references/marine-databases.md)** — Database catalog with API access patterns

- **[references/foundation-models.md](references/foundation-models.md)** — Technical specs, hardware requirements, and code examples for each model

- **[references/mcp-integration.md](references/mcp-integration.md)** — MCP server setup, agent workflow architecture, and skill companion catalog