ocean-genomics

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 pipelines, (3) Predicting protein structures via ESMFold2, AlphaFold, or ColabFold, (4) Analyzing environmental DNA (eDNA) from marine samples, (5) Querying marine databases (OBIS, BOLD, DOO, GenBank, UniProt), (6) Using biological foundation models (Evo 2, ESMC/ESMFold2, ProGen3), (7) Setting up 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:

// Add to .claude/settings.json → mcpServers
{
  "gget-mcp": {
    "command": "uvx",
    "args": ["--from", "gget-mcp@latest", "stdio"]
  }
}

2. Install CLI Tools

# 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

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)
   → Fast screening: ESMFold2 single-sequence mode (no MSA) — legacy: ESMFold
   → With ligands / DNA / RNA / modified AA: AlphaFold 3 (leads both subsets); ESMFold2 if MIT required
   → High accuracy: benchmark ESMFold2 vs AlphaFold 3 / ColabFold on your targets
   → 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
Fast structure (no MSA)	ESMFold2 / ESMFold2-Fast	ESMFold2InputBuilder().fold(model, spi, ...)
Fast structure (legacy)	ESMFold	model.infer_pdb(sequence)
High-accuracy structure	ColabFold	colabfold_batch input.fasta output/
Complex w/ ligand/DNA/RNA	AlphaFold 3 (ESMFold2 if MIT req.)	StructurePredictionInput(sequences=[ProteinInput(...), LigandInput(...)])
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)

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
ESM Metagenomic Atlas	esmatlas.com	617M metagenomic protein structures (Meta FAIR, still live)
ESM Atlas	biohub.ai/esm/protein/atlas	6.8B proteins, >1B structures, SAE-feature organized
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
ESMC	300M/600M/6B	Protein	Embeddings, SAE features (current gen)	pip install esm@git+https://github.com/Biohub/esm.git@main
ESMFold2	On frozen ESMC 6B	Protein+DNA+RNA+ligand+modified AA	All-atom structure, binder design	same as ESMC · HF biohub/ESMFold2
ESM-2 (legacy)	15B params	Protein	Embeddings, contacts	pip install fair-esm
ESMFold (legacy)	Based on ESM-2	Protein	Fast structure prediction	pip install fair-esm
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

ESM stewardship: Meta FAIR → EvolutionaryScale → Chan Zuckerberg Biohub. Repo moved to github.com/Biohub/esm and relicensed to MIT (2026-05-27); facebookresearch/esm is archived.

Accuracy is task-dependent, vendor-self-reported, and not independently replicated as of 2026-07-21: ESMFold2 beats AlphaFold 3 head-to-head on protein-protein (76 vs 73) and antibody-antigen (53 vs 47), but AF3 leads protein-ligand and protein-DNA. Benchmark your own targets.

⚠️ Local ESMFold2 needs the Biohub transformers fork (stock transformers has no esmfold2 module) and Python 3.12 only — install via pip install esm@git+https://github.com/Biohub/esm.git@main. ESMC context window: 2048 tokens — chunk longer metagenomic ORFs.

For detailed specifications on each model, see references/foundation-models.md.

Detailed References

• references/tools-inventory.md — Full CLI commands, install instructions, and usage examples for all 30+ tools
• references/marine-databases.md — Database catalog with API access patterns
• references/foundation-models.md — Technical specs, hardware requirements, and code examples for each model
• references/mcp-integration.md — MCP server setup, agent workflow architecture, and skill companion catalog