Mohamed Abuelanin

I build computational methods for genomic problems that become difficult when the data are large, repetitive, or poorly represented by existing references. That work draws on bioinformatics, software engineering, and genome biology: I find where existing methods fail, build something better, and use it to answer biological questions that were previously hard to reach.

My path into genomics began in Cairo, where I studied electrical and biomedical engineering while working as an R&D software engineer. I wanted to work on medical problems without giving up the part of engineering I enjoyed most: programming and building things. As an undergraduate, I taught myself enough bioinformatics to develop a machine-learning classifier directly from DNA sequences. That project was my first experience of treating biological data as both a scientific problem and a computational one, and it changed the direction of my career.

At Nile University, bioinformatics became the center of my research. I worked on k-mer algorithms for searching, partitioning, and clustering large sequence collections, and later began collaborating with Titus Brown’s lab at UC Davis. During my PhD, I became increasingly interested in problems where a good biological idea was not enough. The computation also had to survive biobank-scale sequencing data, imperfect inputs, and users other than the person who wrote the code. That experience still shapes how I approach research: I care about the method, but also whether it remains useful when the problem becomes larger or messier than the original experiment.

Today, in the Dennis Lab, I work on genome assembly and genetic variation in duplicated and other difficult regions of the genome. My research has ranged from building chromosome-scale mammalian genomes to studying rare and de novo variation in autism families. The biological questions differ, but I keep returning to the same problem: important information is often already present in the data, yet our references, representations, or algorithms prevent us from seeing it clearly. I enjoy finding those blind spots, building the computational machinery needed to get past them, and then returning to the biology with a view that was not available before.

I also care strongly about what happens to a method after the paper is finished. Not every analysis needs to become a software package. Still, when an approach is broadly useful, I want other researchers to be able to inspect it, reproduce it, adapt it to another organism or cohort, and improve it. That is why open-source software, documented workflows, and public genomic resources are an important part of how I work. A method becomes more valuable when it can leave the project that produced it and become part of how other people do science.