Dr Anthony G Doran
This person is a member of Sanger Institute Alumni.
Anthony works on the Mouse Genomes Project; the de novo genome assemblies of 16 of the most commonly studied inbred laboratory mouse strains. His interests are exploring the genomic landscape to understand the influence that variation has on biological processes and phenotypic outcomes with a particular focus on human and mouse disease.
My background lies at a junction between computer science, mathematics and biology. Today, most of my work focuses on utilising these three disciplines to ask, investigate and answer questions related to genetic variation and diversity. In particular, I am interested in integrating different data types derived from several independent sources to investigate the functional basis of complex traits.
I joined the Sanger Institute in 2014. While here, I’ve worked on whole genome sequencing data from 36 inbred laboratory mouse strains to identify variation (SNPs, indels, SVs) within and across each of these strains. Refining our knowledge of the variation that is unique to a single strain is particularly important to help us understand the unique genetic differences underlying susceptibility and resistance to a wide range of human diseases that these strains are used to study. In parallel to this, I have been working on the Mouse Genomes Project; a large collaborative effort to produce de novo genome assemblies of 16 of these inbred laboratory mouse strains.
Previously, I obtained my PhD working on unravelling the genetic basis of bovine muscle growth and development under the umbrella of systems biology. The mechanisms regulating many traits are undoubtedly complex, and as such require the integration of data from many sources to fully comprehend. Systems biology is an integrative approach whose constituent disciplines include genomics, transcriptomics, proteomics, metabolomics and bioinformatics. As such, within the scope of muscle growth, I worked on developing novel software and statistical approaches to investigate the effect of variation in non-model organisms as well as the analysis of raw sequencing (RNA and DNA sequencing) and genotyping data (used for GWAS).