Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Sequence Simulation Serversimulate paired-end Illumina reads at 30x coverage for this sequence"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Sequence Simulation Server
An MCP (Model Context Protocol) server for simulating DNA and amino acid sequences using various evolutionary models and algorithms. This server provides powerful tools for sequence generation, mutation simulation, evolutionary modeling, and phylogenetic analysis.
Features
🧬 DNA Sequence Generation
Random DNA Generation: Generate sequences with specified GC content
Markov Chain Models: Context-dependent sequence generation
Codon-Biased Generation: Realistic protein-coding sequences
Customizable Parameters: Length, GC content, seed for reproducibility
📊 FASTQ Sequencing Simulation
NGS Read Simulation: Generate realistic next-generation sequencing reads
Platform-Specific Models: Illumina, 454, Ion Torrent, and PacBio quality models
Paired-End Support: Both single-end and paired-end sequencing reads
Error Modeling: Configurable sequencing error rates with realistic quality scores
Coverage Control: Generate reads to achieve specified coverage depths
NEAT-Based: Implementation inspired by published NEAT methodology
🧭 Protein Sequence Generation
Random Protein Generation: Uniform amino acid distribution
Hydrophobic-Biased: Membrane protein-like sequences
Disorder-Prone: Intrinsically disordered protein sequences
Custom Composition: User-defined amino acid frequencies
🔬 Sequence Mutation
Substitution Mutations: Point mutations with transition/transversion bias
Insertion/Deletion Events: Indel mutations
Multiple Iterations: Track changes over time
Both DNA and Protein: Support for nucleotide and amino acid sequences
🌳 Evolutionary Simulation
Population Evolution: Simulate populations over generations
Selection Pressure: Configurable fitness functions
Lineage Tracking: Follow individual evolutionary paths
Fitness Functions: GC content, length, hydrophobicity targets
🌲 Phylogenetic Simulation
Tree-Based Evolution: Simulate sequences on phylogenetic trees
Multiple Substitution Models: JC69, K80, HKY85, GTR
Molecular Clock: Uniform or variable evolutionary rates
Multiple Output Formats: FASTA, NEXUS, PHYLIP
Installation
Usage
With Claude Code
Manual Configuration
Add to your Claude Code MCP configuration:
Available Tools
1. Generate DNA Sequence
Generate random DNA sequences with various models.
Parameters:
length(required): Sequence lengthgcContent(optional): GC content ratio (0-1, default: 0.5)count(optional): Number of sequences (default: 1)model(optional): "random", "markov", or "codon-biased"seed(optional): Random seed for reproducibilityoutputFormat(optional): "fasta" or "plain"
Example:
2. Generate Protein Sequence
Generate random protein sequences with various biases.
Parameters:
length(required): Sequence lengthcount(optional): Number of sequences (default: 1)model(optional): "random", "hydrophobic-bias", or "disorder-prone"composition(optional): Custom amino acid frequenciesseed(optional): Random seedoutputFormat(optional): "fasta" or "plain"
Example:
3. Simulate FASTQ File
Simulate FASTQ sequencing reads with realistic quality scores and error models.
Parameters:
referenceSequence(required): Reference DNA sequence to generate reads fromreadLength(required): Length of each sequencing read (50-300 bp)coverage(required): Target sequencing coverage depth (1-1000x)readType(optional): "single-end" or "paired-end" (default: "single-end")insertSize(optional): Mean insert size for paired-end reads (default: 300)insertSizeStd(optional): Standard deviation of insert size (default: 50)errorRate(optional): Base calling error rate 0-0.1 (default: 0.01)qualityModel(optional): "illumina", "454", "ion-torrent", or "pacbio" (default: "illumina")mutationRate(optional): Rate of true mutations 0-0.05 (default: 0.001)seed(optional): Random seed for reproducibilityoutputFormat(optional): "fastq" or "json" (default: "fastq")
Example:
Citation: Based on Stephens et al. (2016) PLOS ONE 11(11): e0167047.
4. Mutate Sequence
Apply mutations to existing sequences.
Parameters:
sequence(required): Input sequencesequenceType(required): "dna" or "protein"substitutionRate(optional): Substitution rate (default: 0.01)insertionRate(optional): Insertion rate (default: 0.001)deletionRate(optional): Deletion rate (default: 0.001)transitionBias(optional): Transition vs transversion bias for DNA (default: 2.0)iterations(optional): Number of mutation rounds (default: 1)seed(optional): Random seedoutputFormat(optional): "fasta" or "plain"
Example:
5. Evolve Sequence
Simulate sequence evolution over multiple generations.
Parameters:
sequence(required): Starting sequencegenerations(required): Number of generationspopulationSize(required): Population sizemutationRate(required): Mutation rate per generationselectionPressure(optional): Selection strength (0-1)fitnessFunction(optional): "gc-content", "length", "hydrophobic", or "custom"targetValue(optional): Target value for fitness functiontrackLineages(optional): Track individual lineagesseed(optional): Random seedoutputFormat(optional): "summary", "detailed", or "fasta"
Example:
6. Simulate Phylogeny
Simulate sequence evolution on phylogenetic trees.
Parameters:
rootSequence(required): Ancestral sequencetreeStructure(optional): Newick format tree or "random"numTaxa(optional): Number of taxa for random tree (default: 5)mutationRate(optional): Mutation rate per branch length (default: 0.1)branchLengthVariation(optional): Branch length variation (default: 0.2)molecularClock(optional): Use molecular clock (default: true)substitutionModel(optional): "JC69", "K80", "HKY85", or "GTR"seed(optional): Random seedoutputFormat(optional): "fasta", "nexus", or "phylip"
Example:
Output Formats
FASTA Format
Standard FASTA format with descriptive headers containing simulation parameters.
Statistics
All tools provide detailed statistics including:
Sequence composition analysis
Mutation counts and types
Evolutionary parameters
Phylogenetic tree statistics
Specialized Formats
NEXUS: For phylogenetic analysis software
PHYLIP: For phylogenetic analysis
JSON: Structured data with full simulation details
Use Cases
Research Applications
Molecular Evolution Studies: Simulate sequence evolution under different models
Phylogenetic Analysis: Generate test datasets with known evolutionary history
Algorithm Testing: Create benchmark datasets for bioinformatics tools
Educational Purposes: Demonstrate evolutionary principles
Bioinformatics Development
Algorithm Validation: Test sequence analysis tools with controlled data
Statistical Analysis: Generate null distributions for statistical tests
Performance Benchmarking: Create datasets of varying complexity
Method Comparison: Compare tools on simulated vs real data
Technical Details
Evolutionary Models
Jukes-Cantor (JC69): Equal substitution rates
Kimura 2-Parameter (K80): Transition/transversion bias
HKY85: Unequal base frequencies with transition bias
GTR: General time-reversible model
Sequence Generation
Markov Chains: Context-dependent nucleotide selection
Codon Usage Bias: Realistic protein-coding sequences
Amino Acid Properties: Hydrophobicity and disorder propensity
Mutation Models
Point Mutations: Single nucleotide/amino acid changes
Indels: Insertion and deletion events
Transition Bias: Realistic DNA mutation patterns
Dependencies
@modelcontextprotocol/sdk: MCP framework
zod: Schema validation
typescript: Type safety
Node.js: Runtime environment
Contributing
This server provides a comprehensive framework for sequence simulation. Extensions could include:
Additional substitution models
Recombination simulation
Population genetics models
Structural constraints
Codon usage tables for different organisms
License
See LICENSE file for details.