Shalev Yaacov | M.Sc. Researcher @ Hebrew University (Tabach Lab)
Evolutionary gene clustering, multi-omics integration, and phylogenetic profiling for uncovering IRD & cilia-related disease networks.

This portfolio gathers the computational frameworks, pipelines, and visualization tools I develop for studying Inherited Retinal Diseases (IRD) and Ciliopathies.

My work focuses on addressing missing heritability in genetic disorders. By integrating Normalized Phylogenetic Profiling (NPP) with transcriptomic, phenotypic, and molecular datasets, I aim to uncover evolutionarily conserved gene modules and new disease candidates.

Note: All datasets in this repository are synthetic, transformed, or strictly demonstrative to ensure confidentiality and compliance with institutional agreements.

To quickly explore my work visually, check out the interactive portfolio:

Interactive View: Click here to launch

(It includes evolutionary genomics tools, multi-omics visualizations, and project overviews.)

• Goal: Systematic characterization of precomputed ciliary gene clusters using evolutionary and functional annotations.
• Approach: Modular analytical pipeline evaluating cluster composition, annotations, enrichment, and evolutionary or phenotypic coherence.
• Includes:
  • Automated annotation and GO-term enrichment.
  • Internal-distance and similarity metrics.
  • Detection of submodules and coherent functional subsets.
  • Visualizations of gene-gene relationships and conserved signatures.

• Goal: Identify phenotype-driven gene modules within IRD-associated gene sets.
• Approach: Semantic similarity-based clustering using HPO annotations, designed for integration within a broader multi-omics framework.
• Includes:
  • Silhouette validation and stability assessment.
  • Phenotype similarity matrices and semantic clustering.
  • Prioritization support for unsolved IRD gene candidates (synthetic demonstration only).

• Goal: Create clear, publication-ready heatmaps and similarity visualizations supporting exploratory multi-omics analyses.
• Tools: Custom Python and R scripts for species-aligned matrices, clustered heatmaps, annotation layers, and comparison plots.

• Goal: Demonstrate Local Barcode Segmentation (LBS)-inspired evolutionary profiling using synthetic data.
• Includes:
  • Synthetic NPP matrices and barcode segments.
  • Segmentation and consensus-profile workflows.
  • Signal vs. noise validation and benchmarking experiments.

• Languages: Python (Pandas, Scikit-learn, SciPy), R (ggplot2, pheatmap, ComplexHeatmap).
• Core Methods:
  • Normalized Phylogenetic Profiling (NPP).
  • Comparative genomics & Supervised clustering.
  • Multi-omics data integration.
  • Feature engineering & ML-based prioritization.

• Lab: Prof. Yuval Tabach Lab, Faculty of Medicine.
• Institution: Hebrew University of Jerusalem.
• Role: M.Sc. Candidate in Genomics & Bioinformatics.
• Location: Jerusalem, Israel.

This repository evolves alongside my thesis research and will continue to expand with new analyses, tools, and polished workflows.