Characterization of gene regulatory elements using multi-omic data

Computational analysis of multi-omic single cell data from 4 species (mouse/marmoset/macaque/human)

Hundreds of duplicated genes in the human genome are duplicated and many are known to be associated with a number of human diseases. However, the short read lengths of current sequencing technologies make the analysis of such genes difficult. We have developed novel tools to genotype the copy number of duplicated genes using whole-genome sequencing. The goal of this project is to analyze large-scale sequencing datasets (using cloud computing platforms) for Mendelian and complex human diseases to identify novel disease associations. 

Long-read sequencing technologies have the potential to overcome some of the key limitations of short-read sequencing, particular in long repetitive regions of the human genome, but require the development of new algorithms. We have previously developed computational methods for variant calling (Longshot, Nature Communications 2019) and read mapping in segmental duplications (Duplomap, Nucleic Acids Research 2020) using long-read sequencing technologies. The goal of this project is to implement a haplotype-based model for variant calling using long reads that automatically identifies genomic regions that can be called with high confidence.

Malaria remains a major problem for 40% of the world's population and drug resistance is widespread.  One mechanism for identifying drug resistance determinants is by identifying regions that show unexpected homozygosity in whole genome sequences.  The rotation student will work with physician scientists to align short read sequences to the P. falciparum genome, call variants, annotate variants, run population genetics analyses and produce reports.