Decoding the "missing heritability" in Inherited Retinal Diseases (IRDs). I design and develop computational frameworks integrating Normalized Phylogenetic Profiling (NPP) (evolutionary co-signal across ~2,000 species) with multi-omics data to uncover novel disease-causing genes across ~2,000 eukaryotic species.
I turn the hypothesis that genes functioning together share evolutionary histories into a predictive model. I construct phylogenetic profiles across ~2,000 eukaryotic species to identify co-evolving modules and prioritize candidate genes for unsolved IRD cases.
The algorithm detects "Hotspots" (Red) where genes are lost or gained together. These anomalies often point to specific disease mechanisms in IRD.
Below are selected projects where I designed and built computational pipelines and visual tools for exploratory genomic analysis.
My computational work is grounded in hands-on wet-lab experience in molecular biology and protein engineering. I’ve worked on recombinant expression of bovine lactoferrin and human keratin using both E. coli and Arabidopsis systems, with practical experience in cloning, transgenic plant transformation and selection, FPLC purification, qPCR, and ELISA.
This work was carried out in collaboration with Miruku in Prof. Oded Shoseyov’s lab, and included co-authoring a book chapter on molecular farming in "Alternative Dairy Products and Technologies".
A behavioral research project on octopus motor learning, where I designed and analyzed experiments to study adaptive learning strategies — an early foundation for my interest in data-driven analysis of complex biological systems.
NPP is an evolutionary analysis method that looks across thousands of species to identify genes that “travel together” through evolution.
Instead of using simple presence-or-absence, NPP measures how strongly each gene is conserved in every organism and normalizes these patterns across the tree of life.
Genes with similar evolutionary signatures often work in the same pathway.
By capturing these hidden patterns, NPP helps reveal functional gene modules and highlight new candidates for diseases such as IRDs and ciliopathies.
1. Evolutionary profiles
Each human gene is mapped to its orthologs across ~2,000 species.
2. Normalization
Conservation scores are normalized to remove biases and expose true evolutionary signatures.
3. Co-evolving modules
Genes with similar profiles cluster into modules that often point to shared function or disease mechanisms.