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 or tissue architecture).
| Genome | Tissue | Interactions | |
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| Evolution | Function | Context | Methods |
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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 quantifying content and composition of the tumour microenvironment. |
We develop computational and statistical methods to visualize, analyze and reconstruct biological networks. |
Publications
- Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
- 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
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
