Population Genomics Methods Development
Overview
The Cresko Laboratory has been at the forefront of developing genomic tools and computational methods that have transformed evolutionary biology research. Our innovations have enabled cost-effective, high-resolution genomic studies in both model and non-model organisms, democratizing population genomics research worldwide.
RAD-seq Technology Development
Revolutionary Innovation
Restriction site Associated DNA sequencing (RAD-seq), developed through a collaboration between the Cresko and Johnson labs at the University of Oregon, has fundamentally changed how we study population genomics:
Core Advantages
- Genome-wide coverage: Thousands to millions of markers across the genome
- De novo discovery: No requirement for prior genomic resources
- Cost effectiveness: Orders of magnitude cheaper than whole-genome sequencing
- Scalability: From individuals to thousands of samples
- Flexibility: Adjustable marker density for different applications
Methodological Refinements
Our continued development of RAD-seq methods includes:
Technical Improvements
- 2bRAD: Simplified library preparation with uniform fragment sizes
- ddRAD: Double-digest RAD for improved repeatability
- Paired-end RAD: Enabling haplotype reconstruction and improved mapping
- HDR-seq: High-density RAD for fine-scale mapping
- Rapture: RAD-capture for targeted sequencing of specific loci
Protocol Optimization
- Enzyme selection strategies for different genome sizes
- Multiplexing approaches for cost reduction
- Size selection protocols for consistent coverage
- Quality control metrics and best practices
- Troubleshooting guides for common issues
Stacks Software Pipeline
Comprehensive Analysis Platform
Stacks, our flagship software package, provides an integrated pipeline for RAD-seq data analysis:
Core Functionality
- De novo assembly: Building loci without a reference genome
- Reference alignment: Mapping reads to existing genome assemblies
- SNP discovery: Identifying and genotyping polymorphic sites
- Population statistics: Calculating FST, π, and other metrics
- Data export: Multiple output formats for downstream analyses
Algorithm Development
Key algorithmic innovations in Stacks include:
Assembly Algorithms
- Maximum likelihood framework for locus assembly
- Error correction for sequencing mistakes
- Paralogy detection and filtering
- Coverage-based genotype calling
- Population-aware genotyping models
Statistical Methods
- Kernel-smoothed FST calculations
- Sliding window analyses
- Bootstrap confidence intervals
- Correction for multiple testing
- Effective population size estimation
Community Impact
Stacks is now used by thousands of researchers worldwide: - 10,000+ citations: Widely adopted across diverse fields - Active user community: Support forum and mailing list - Regular updates: Continuous improvement and bug fixes - Comprehensive documentation: Tutorials, protocols, and examples - Workshop training: Regular courses on RAD-seq analysis
New Computational Approaches
Machine Learning Applications
We’re pioneering the use of machine learning in population genomics:
Deep Learning Models
- Neural networks for genotype imputation
- Convolutional networks for selection detection
- Recurrent networks for demographic inference
- Autoencoders for dimensionality reduction
- Generative models for simulation
Applications
- Predicting adaptive potential
- Classifying selection signatures
- Inferring population structure
- Detecting hybridization
- Identifying technical artifacts
Graph-based Methods
Moving beyond linear reference genomes:
Pangenome Approaches
- Graph construction from population data
- Variation graphs for complex regions
- Structural variant detection
- Haplotype-resolved assembly
- Population-specific references
Network Analyses
- Gene regulatory network inference
- Epistatic interaction networks
- Population connectivity graphs
- Phylogenetic networks
- Coexpression networks
Long-read Sequencing Integration
Hybrid Approaches
Combining short and long-read technologies:
Method Development
- Linked-read RAD-seq protocols
- Long-read validation of RAD loci
- Phasing using long-read data
- Structural variant discovery
- Gap filling in genome assemblies
Computational Tools
- Read simulation and validation
- Hybrid assembly pipelines
- Error correction algorithms
- Variant calling methods
- Visualization tools
Single-cell Genomics Methods
Population-level Single-cell Studies
Extending population genomics to the cellular level:
Technical Development
- Single-cell RAD-seq protocols
- Cell-type-specific genotyping
- Allele-specific expression in single cells
- Somatic mutation detection
- Lineage tracing methods
Analytical Frameworks
- Population genetics of somatic variation
- Cell-type evolution models
- Developmental trajectory inference
- Integration with bulk sequencing
- Multi-modal data integration
Environmental DNA Methods
eDNA for Population Genomics
Developing methods for population studies using environmental samples:
Protocol Development
- Species-specific eDNA capture
- Population-level eDNA genotyping
- Quantitative eDNA methods
- Temporal sampling strategies
- Spatial distribution mapping
Computational Approaches
- Allele frequency estimation from eDNA
- Population size inference
- Community composition analysis
- Contamination detection
- Quality control metrics
Software Ecosystem
Additional Tools Developed
Beyond Stacks, we’ve created numerous specialized tools:
Population Genetics Software
- PopGen Pipeline: Automated population genetics workflows
- Selection Scanner: Genome-wide selection detection
- Demography Inferencer: Coalescent-based demographic modeling
- Hybrid Detector: Identifying admixed individuals
- Phylo Constructor: Phylogenetic tree building from RAD data
Visualization Tools
- RAD Viewer: Interactive exploration of RAD loci
- Population Structure Plotter: Advanced STRUCTURE-like plots
- Genome Scanner: Sliding window visualization
- Network Visualizer: Population connectivity graphics
- Tree Annotator: Phylogenetic tree decoration
Best Practices and Standards
Method Standardization
We contribute to community standards for genomic methods:
Protocol Guidelines
- Sample collection and preservation
- DNA extraction optimization
- Library preparation standards
- Sequencing depth recommendations
- Replication and validation strategies
Analytical Standards
- Quality filtering thresholds
- Statistical significance criteria
- Multiple testing corrections
- Reproducibility requirements
- Data sharing formats
Training and Education
Capacity Building
We’re committed to training the next generation:
Educational Resources
- Online tutorials and videos
- Comprehensive user manuals
- Example datasets and workflows
- Troubleshooting guides
- FAQ compilations
Workshops and Courses
- Annual RAD-seq workshops
- Bioinformatics boot camps
- Software-specific training
- Custom institutional workshops
- Virtual learning modules
Current Development Projects
Active Software Projects
- Cloud-based RAD-seq processing
- Real-time data quality monitoring
- Automated parameter optimization
- Integration with Galaxy platform
- Mobile app for field data collection
Method Innovation
- Ultra-low input protocols
- Ancient DNA applications
- Methylation-sensitive RAD-seq
- Chromatin accessibility mapping
- Expression-RAD integration
Collaborations
Our methods development involves: - Computer scientists and software engineers - Statisticians and mathematicians - Molecular biologists and biochemists - Field biologists and ecologists - Conservation practitioners
Impact and Applications
Our methods have been applied to: - Conservation genetics of endangered species - Agricultural crop improvement - Fisheries stock assessment - Human disease gene mapping - Microbial community analysis - Ancient DNA studies - Forensic genetics - Invasion biology
Future Directions
Ongoing and planned developments include: - Quantum computing applications - Blockchain for data integrity - Federated learning approaches - Automated experimental design - Real-time sequencing analysis
Learn More
For more information about our methods development: - Download Stacks → - Contact us →