Using experimentation, computation and theory, we characterize the influence of genomic variation on patient phenotypes like survival and intermediate phenotypes like gene expression, transcription factor binding and tissue architecture.
| Genome | Tissue | Interactions | |
 |
 |
 |
 |
| Evolution | Function | Context | Methods |
|
We quantify the heterogeneity of tumours and reconstruct their evolutionary history. |
We investigate the genetic and protein factors that determine the binding dynamics of nuclear receptors. |
We put genomics in a tissue context by integrating tissue phenotypes with paired genomic profiles. |
We develop computational and statistical methods to visualize, analyze and reconstruct biological networks. |
Publications
- Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
- Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biol, 16:36
- Trinh et al (2014), GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images, Genome Biol, 15:442
Software
- Bitphylogeny - Bayesian framework for intra-tumour phylogenies
- CRImage - tumour tissue analysis
- GoIFISH - quantify single cell heterogeneity from IFISH images
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - bridging the gap between statistical computing and network visualization
