Mathematics and Statistics at EMBL
Computational and evolutionary genomics
High-throughput sequencing is allowing the genome, transcriptome and epigenome of an enormous range of species, including model and non-model organisms, to be studied in exquisite detail. Moreover, as technology develops further, we will move from studying populations of cells to studying regulatory processes at the single-cell level - this will enable numerous insights into developmental processes (e.g. embryogenesis and early-development), neurological processes (e.g., a fine-grained map of gene expression within specific brain regions), and the way in which tumours develop. However, to make the most of these opportunities, appropriate computational tools for managing, analyzing, visualizing and downloading the data are essential. With this in mind, our work focuses on the development of statistical methods that will exploit these data to the fullest extent.
Marioni J.C., Mason C.E., Mane S.M., Stephens M., Gilad Y. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 2008 Sep; 18(9):1509-17.
Pickrell J.K., Marioni J.C., Pai A.A., Degner K.F., Engelhardt E.B., Nkadori E., Veyrieras J-B., Stephens M., Gilad Y., Pritchard J.K. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 2010 Apr; 464(7289):768-72.
Perry G.H.*, Marioni J.C.*, Melsted P., Gilad Y. Genomic-scale capture and sequencing of endogenous DNA from feces. Molecular Ecology (in press) (* joint first authors)