This repository contains data, code, and supplementary materials for the doctoral dissertation:

"Meta-analysis of negative crossover interference: variation between species, environments, and sexes"

Author: Shaul Sapielkin
Supervisors: Prof. Abraham B. Korol, Dr. Eyal Privman
Institution: University of Haifa, Department of Evolutionary and Environmental Biology
Year: 2025

Crossover interference (COI) is a fundamental mechanism regulating meiotic recombination distribution along chromosomes. While positive COI has been extensively studied, negative crossover interference (NCOI)—where crossovers cluster closer than expected by chance—remains poorly understood. This dissertation presents the first comprehensive meta-analysis of NCOI across eukaryotic species, revealing its widespread occurrence, sex-specific patterns, and molecular determinants.

Through analysis of 27 species (2,515 chromosomal intervals) spanning plants, arthropods, and fish, we demonstrate that NCOI occurs in 47.7% of intervals with 96.7% statistical significance. Sex-specific analysis reveals dramatic dimorphism: 65.3% NCOI prevalence in males versus 32.9% in females. Phylogenetic analysis shows extraordinary variation from 14.3% NCOI in maize to 77.8% in wheat.

Methodologically, this work introduces an extended gamma-sprinkled model for simultaneous estimation of positive, negative, and neutral interference, and develops a sex-aware maximum likelihood estimation framework that corrects systematic bias in F2 populations (bias magnitude: 0.21-0.46 CoC units). Molecular analysis of wheat epigenome reveals strong correlations between NCOI and chromatin accessibility (r=0.995, p<0.001), histone modification H3K27ac (r=0.883, p<0.05), and DNA methylation (r=-0.68, p<0.01).

This research establishes NCOI as a biologically significant phenomenon with implications for understanding meiotic regulation, genome evolution, and breeding programs.

NCOI-metaanalysis/
├── data/                          # All datasets used in the dissertation
├── figures/                       # Figures organized by dissertation chapters  
├── scripts/                       # Python analysis code
├── tables/                        # Supplementary tables and results
└── README.md                      # This file

Contains raw and processed datasets:

• Segregation data from 27 species (32 independent studies)
• Species metadata (taxonomy, sample sizes, population types)
• Simulation results for sex-aware MLE validation
• Wheat epigenomic features (chromatin accessibility, histone modifications, DNA methylation, transposons, lncRNAs)

Key statistics:

• 27 species analyzed (59% plants, 25% arthropods, 19% fish)
• 2,515 chromosomal intervals
• 1,199 intervals with NCOI (47.7%)
• 1,264 intervals with positive interference (50.3%)

High-resolution figures from the dissertation, organized by chapter:

• Publication-quality versions (300+ dpi)
• Vector formats (PDF/SVG) where available

Python analysis code organized by dissertation chapter:

Data processing and quality control:

• Quality control using MultiPoint software criteria
• Coefficient of Coincidence (CoC) calculations
• Interference type classification

Extended gamma-sprinkled model (Chapter 2):

• Three-component model implementation (ν>1, ν=1, ν<1)
• Maximum likelihood estimation
• Likelihood ratio testing (H₀, H₁, H₂)

Sex-aware MLE framework (Chapter 3):

• Sex-aware maximum likelihood estimation for F2 populations
• Simulation study (N=1,000 to 100,000 individuals)
• Bias quantification and empirical validation

Phylogenetic analysis (Chapter 4):

• NCOI patterns across phylogenetic groups
• CoC summary statistics by species
• Visualization and statistical comparisons

Genomic correlations (Chapter 5):

• Wheat epigenomic feature analysis
• Correlation analysis with NCOI intensity
• Sliding window calculations

Supplementary tables including:

• Complete species and dataset information
• Statistical test results
• Correlation matrices
• Simulation validation data

• NCOI detected in 47.7% of chromosomal intervals (1,199/2,515)
• 96.7% statistical significance (p<0.05) among NCOI intervals
• Present across all major phylogenetic groups: plants, arthropods, fish

• 65.3% NCOI prevalence in males (764/1,169 male-informative intervals)
• 32.9% NCOI prevalence in females (225/683 female-informative intervals)
• 2.0-fold sex bias in NCOI occurrence

• Triticum (wheat): 77.8% NCOI, mean CoC = 3.79
• Zea mays (maize): 14.3% NCOI, mean CoC = 1.89
• Pericentromeric regions show 2.3-fold elevated NCOI

Strong correlations with wheat epigenomic features:

• Chromatin accessibility: r=0.995, p<0.001
• H3K27ac (active chromatin): r=0.883, p<0.05
• DNA methylation: r=-0.68, p<0.01 (negative correlation)
• Transposable elements: Positive association with NCOI intensity

• Sex-aware MLE framework eliminates systematic bias in F2 populations
• Bias magnitude: +0.21 to +0.36 CoC (concordant sex differences), -0.22 to -0.46 CoC (discordant)
• Extended gamma-sprinkled model enables simultaneous estimation of all interference types

• Python 3.8 or higher
• See requirements.txt for complete list of dependencies

# Clone the repository
git clone https://github.com/[username]/NCOI-metaanalysis.git
cd NCOI-metaanalysis

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create conda environment
conda env create -f environment.yml

# Activate environment
conda activate ncoi-metaanalysis

Data processing and QC:

# Example: Quality control and CoC calculation
python scripts/01_data_processing/quality_control.py \
    --input data/raw_segregation_data.csv \
    --output data/processed_intervals.csv \
    --min_individuals 100

Extended gamma-sprinkled model:

# Estimate interference parameters
python scripts/02_gamma_model/extended_gamma_sprinkled.py \
    --data data/processed_intervals.csv \
    --output results/model_estimates.csv

Sex-aware MLE framework (Chapter 3):

# Run sex-aware analysis
python scripts/03_sex_aware_MLE/sex_aware_framework.py \
    --input data/F2_datasets.csv \
    --output results/sex_aware_estimates.csv

Phylogenetic analysis (Chapter 4):

# Analyze phylogenetic patterns
python scripts/04_phylogenetic_analysis/chapter4_main_analysis.py \
    --data data/processed_intervals.csv \
    --output results/phylogenetic_summary.csv

Genomic correlations (Chapter 5):

# Wheat genomic feature correlations
python scripts/05_genomic_correlations/wheat_feature_analysis.py \
    --features data/wheat_genomic_features/ \
    --ncoi data/wheat_ncoi_data.csv \
    --output results/correlation_analysis.csv

Sapielkin, S., Korol, A.B., Privman, E. (2025). Accounting for sex differences in recombination rates reduces bias in crossover interference estimation in F2 populations. BMC Genomics (in press).

• Chapter 2: Extended gamma-sprinkled model for crossover interference
• Chapter 3: Sex-aware maximum likelihood estimation framework
• Chapter 4: Phylogenetic patterns of NCOI across eukaryotes
• Chapter 5: Genomic determinants of NCOI in wheat

If you use this data or code, please cite:

@phdthesis{sapielkin2025ncoi,
  author = {Sapielkin, Shaul},
  title = {Meta-analysis of negative crossover interference: variation between species, environments, and sexes},
  school = {University of Haifa},
  year = {2025},
  doi = {10.5281/zenodo.XXXXXX}
}

@article{sapielkin2025sexaware,
  author = {Sapielkin, Shaul and Korol, Abraham B. and Privman, Eyal},
  title = {Accounting for sex differences in recombination rates reduces bias in crossover interference estimation in F2 populations},
  journal = {BMC Genomics},
  year = {2025},
  note = {in press}
}

All data used in this dissertation are publicly available:

• Raw segregation data: Available in data/ directory and permanently archived on Zenodo (DOI: 10.5281/zenodo.XXXXXX)
• Wheat genomic features: Sourced from IWGSC RefSeq v2.1 and URGI wheat genome browser
• Simulation datasets: Complete simulation data available in data/simulation_data/

Original segregation data were compiled from 32 published studies spanning 27 species. Complete citations and data sources are provided in tables/data_sources.csv and dissertation Chapter 2 (Materials and Methods).

• numpy (≥1.21.0) - Numerical computations
• pandas (≥1.3.0) - Data manipulation
• scipy (≥1.7.0) - Statistical analysis
• statsmodels (≥0.13.0) - Statistical modeling
• scikit-learn (≥1.0.0) - Machine learning utilities

• matplotlib (≥3.4.0) - Plotting
• seaborn (≥0.11.0) - Statistical visualization

• biopython (≥1.79) - Sequence analysis

See requirements.txt for complete list and exact versions.

• data/raw_data/ - Original segregation data from literature sources
• data/processed_data/ - Quality-controlled and standardized datasets
• data/simulation_data/ - Sex-aware MLE simulation results
• data/wheat_genomic_features/ - Epigenomic data for correlation analysis

• scripts/01_data_processing/ - Data cleaning, QC, and CoC calculation
• scripts/02_gamma_model/ - Extended gamma-sprinkled model implementation
• scripts/03_sex_aware_MLE/ - Sex-aware MLE framework (Chapter 3)
• scripts/04_phylogenetic_analysis/ - Phylogenetic patterns (Chapter 4)
• scripts/05_genomic_correlations/ - Genomic feature analysis (Chapter 5)
• scripts/utils/ - Utility functions and helpers

Each subdirectory contains a README.md with detailed information about:

• File contents and formats
• Usage instructions
• Expected inputs and outputs
• Relevant dissertation chapters

Code: MIT License - see LICENSE file for details

Data: Creative Commons Attribution 4.0 International (CC-BY-4.0)

You are free to use, modify, and distribute this code and data with appropriate attribution.

Shaul Sapielkin
PhD Candidate
Department of Evolutionary and Environmental Biology
University of Haifa, Israel

Email: shaul@evo.haifa.ac.il
ORCID: https://orcid.org/0000-0001-9375-3850

Supervisors:

• Prof. Abraham B. Korol ([email@example.com])
• Dr. Eyal Privman ([email@example.com])

I express profound gratitude to my supervisor, Professor Abraham B. Korol, for his unwavering guidance and mentorship. I thank Dr. Eyal Privman for his invaluable insights and collaboration. I am grateful to all researchers who made their segregation data publicly available, enabling this comprehensive meta-analysis. This work was supported by [funding sources].

Special thanks to the University of Haifa and the Department of Evolutionary and Environmental Biology for providing excellent research facilities and environment.

• University of Haifa: https://evolution.haifa.ac.il
• Lab Website: [link to lab website if available]
• Extended Methods: See tables/supplementary_methods.pdf
• Interactive Visualizations: [link if available]

• v1.0.0 (2025-10-28) - Initial release with complete dissertation data and code
• v1.1.0 (TBD) - Post-defense updates and corrections

Found a bug or have a suggestion? Please open an issue on GitHub or contact the author directly.

Contributions are welcome! Please feel free to submit pull requests with improvements or corrections.

Last updated: October 28, 2025