Computational and evolutionary genomics

Marioni group figure (EMBL-EBI) Classifying genes by their regulatory function. We used RNA-seq data generated from F0 mice and their F1 hybrids to classify genes into sets depending upon their regulatory mechanism.  [Goncalves et al., Genome Research 2012.]

Our research focusses on developing the computational and statistical tools necessary to exploit high-throughput genomics data in order to understand the regulation of gene expression and to model developmental and evolutionary processes. 

Within this context, we focus on work on three specific areas. Firstl, we want to understand how the divergence of gene expression levels is regulated. By associating changes in expression with a specific regulatory mechanism, critical insights into speciation and differences in phenotypes between individuals can be obtained. Second, we want to use gene expression as a definition of the molecular fingerprint of individual cells to study the evolution of cell types. By comparing the molecular fingerprint associated with a particular tissue across species, it is possible to decipher whether specific cell types arise de novo during speciation or whether they have a common evolutionary ancestor. Thirdly, we want to model spatial variability in gene expression levels within a tissue or organism. By modelling such variability, heterogeneous patterns of expression within a cell-type can be identified, potentially allowing new cell-types, perhaps with novel functions, to be uncovered. Additionally, the extent of heterogeneity present across a tumour can also be studied using such approaches.

These three strands of research are brought together by single-cell sequencing technologies. By studying variability in gene expression (and other genome-wide characteristics) at a single-cell level, our ability to assay regulatory variation, molecular fingerprints and spatial patterns of expression will be revolutionised. As a key member of the Sanger-EBI Single Cell Genomics Centre we are closely involved in data generation and in using these data, especially single-cell RNA-sequencing, to answer numerous exciting biological questions. However, to exploit these data to the fullest extent, it is critical to develop the appropriate statistical and computational tools - this is one of the key challenges we face in the next few years. 

Marioni group news

  • New paper on single-cell analysis in Nature Methods; highlighted in Nature Biotechnology

Postdocs

EBPOD postdoctoral programme

Selected publications

Goncalves, A. et al. (2012) Extensive compensatory cis-trans regulation in the evolution of mouse gene expression. Genome Res.

Fonseca, N.A. et al. (2012) Tools for mapping high-throughput sequencing data. Bioinformatics.

Kim J.K. and Marioni J.C. (2013) Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol.

Brennecke, P. et al. (2013) Accounting for technical noise in single-cell RNA-sequencing experiments. Nat Methods