Computational Biology

Every assay the lab runs produces a genome-wide table, and none of them explains regulation on its own. The question is what happens when they are read together: whether sequence, accessibility, histone marks and 3D contacts taken jointly predict when a gene is expressed.

We build the pipelines and models that make that possible — 3D genome reconstructions from Hi-C contact maps, nucleosome-landscape analysis that has surfaced genes the annotation missed, and machine learning trained across data types to predict expression and prioritise regulatory elements. The methods are developed on Plasmodium and carry over to the other apicomplexans the lab works on.