# User Guide
This comprehensive guide covers all aspects of using RustKmer for genomic analysis and k-mer counting workflows.
## Guide Overview
- **[Counting k-mers](counting-kmers.md)** - Complete guide to k-mer counting operations
- **[Querying Databases](querying.md)** - Database operations and querying strategies
- **[Fuzzy Search](fuzzy-search.md)** - Pattern matching and advanced search techniques
- **[Performance Tips](performance-tips.md)** - Optimization strategies and best practices
## Getting Started
If you're new to RustKmer, start with the [Getting Started](../getting-started/) section for installation and basic operations.
## User Journeys
### For Bioinformatics Researchers
1. Start with [Counting k-mers](counting-kmers.md) to process your data
2. Learn [Querying Databases](querying.md) to analyze results
3. Explore [Performance Tips](performance-tips.md) to optimize large datasets
### For Software Developers
1. Review the [API Reference](../api-reference/) for integration options
2. Check [Python Examples](../api-reference/python/examples.md) for code patterns
3. Learn about [Database Operations](querying.md) for storage strategies
### For Systems Administrators
1. Follow the [Deployment Guide](../deployment/) for production setup
2. Review [Performance Monitoring](../user-guide/performance-tips.md) for system requirements
3. Check [Troubleshooting](../appendix/troubleshooting.md) for common issues
## Key Concepts
### K-mer Fundamentals
- **k-mer size**: Balance between specificity and performance
- **Canonical k-mers**: Reduce memory usage and improve matching
- **Database format**: Efficient binary storage (.rkdb)
### Performance Characteristics
- **Counting speed**: ~1 million k-mers/second
- **Query performance**: ~4 million queries/second
- **Memory efficiency**: Streaming processing with minimal overhead
### Integration Options
- **Python API**: Native bindings for bioinformatics workflows
- **Rust library**: Maximum performance and control
- **Command line**: Batch processing and automation
## Advanced Topics
### Large-Scale Processing
- Processing gigabyte-scale genomes
- Memory management strategies
- Distributed processing approaches
### Specialized Analysis
- Metagenomic classification
- Genome assembly support
- Population genetics applications
### Integration Patterns
- Pipeline integration with other tools
- Cloud and HPC deployment
- Container and orchestration
## Best Practices
### Data Quality
- Validate input file formats
- Handle sequence quality issues
- Manage ambiguous bases and filtering
### Resource Management
- Monitor memory usage during processing
- Optimize database loading strategies
- Plan storage requirements
### Workflow Design
- Choose appropriate k-mer sizes
- Implement error handling
- Create reproducible analyses
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## Quick Navigation
| Basic counting | Beginner | 5 minutes |
| Database querying | Beginner | 10 minutes |
| Fuzzy searching | Intermediate | 15 minutes |
| Performance optimization | Advanced | 30 minutes |
## Need Help?
- **Documentation**: Browse the sidebar for specific topics
- **Examples**: Check the [Tutorials](../tutorials/) section
- **API Reference**: Complete [Python](../api-reference/python/) and [Rust](../api-reference/rust/) documentation
- **Community**: [GitHub Discussions](https://github.com/rustkmer/rustkmer/discussions)
Ready to dive in? Choose your topic from the navigation menu or continue to the next guide!