LABSCO SUMMARY
Test data for a pipeline, with the ground truth known.
Benchmarking a variant caller or an assembler needs reads whose true answer you already hold. This generates the sequence, applies the evolutionary model, and simulates the reads on top — so the expected result exists before the pipeline runs.
What it is
A simulation server for molecular sequence data. DNA and protein sequences are generated under models you choose, then mutated, evolved across populations, or dropped down a phylogenetic tree — and sequencing reads can be simulated on top of any of it.
What you get
- DNA generated at a specified GC content, from a Markov chain model, or with codon bias for realistic coding sequences
- Protein sequences generated uniformly, hydrophobic-biased, disorder-prone, or from an amino acid composition you supply
- NGS reads simulated with Illumina, 454, Ion Torrent or PacBio quality models, single-end or paired-end, to a coverage depth you set
- Substitutions with transition/transversion bias plus insertions and deletions, tracked over multiple iterations
- Population evolution over generations with configurable fitness functions and lineage tracking
- Sequences evolved on a phylogenetic tree under JC69, K80, HKY85 or GTR, with a uniform or variable molecular clock, written as FASTA, NEXUS or PHYLIP
Requirements
Node with npm install and npm run build, then one stdio entry pointing at the built server. No account and no key — everything is generated locally, and a seed makes a run reproducible.
Setup effort
Build from source — git clone https://github.com/coreymhudson/mcp-sequence-simulation.git
USE CASES
Benchmarking a pipeline against known truth
Generate the reference, then simulate reads off it at a set coverage — the correct answer is the sequence you started from.
Testing how a caller handles a platform's errors
Illumina, 454, Ion Torrent and PacBio quality models, single-end or paired-end, with the error rate under your control.
Producing sequences with realistic composition
GC content, codon bias, hydrophobic or disorder-prone amino acid distributions — rather than uniform random strings no tool has to work for.
Simulating an alignment for a phylogenetics method
Sequences evolved down a tree under JC69, K80, HKY85 or GTR, written out as FASTA, NEXUS or PHYLIP.
Watching selection act on a population
Populations evolved over generations against a fitness target, with individual lineages tracked through it.
BEFORE YOU INSTALL
What to know first
Requires
node on PATH
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Server setup
git clone https://github.com/coreymhudson/mcp-sequence-simulation.git
cd mcp-sequence-simulation
npm install
npm run buildMCP Clients
Claude Code
{
"mcpServers": {
"sequence-simulation": {
"command": "node",
"args": [
"<YOUR_PATH_TO_MCP_SEQUENCE_SIMULATION>/dist/server.js"
]
}
}
}<YOUR_PATH_TO_MCP_SEQUENCE_SIMULATION> is the absolute path of your clone. dist/server.js exists after npm run build.