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).
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| Genome | Tissue | Interactions |
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We quantify the heterogeneity of tumours and reconstruct their evolutionary history. |
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
- Macintyre*, Goranova* et al (2018), Copy-number signatures and mutational processes in ovarian carcinoma, Nature Genetics
- Sivakumar*, de Santiago* et al (2017), Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis, PLoS Medicine, 14(1)
- Marass et al (2017), A phylogenetic latent feature model for clonal deconvolution, AOAS 10(4)
- Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
Software
- VULCAN - infer cofactors from ChIP-seq differential binding
- OncoNEM - Clonal evolution trees from single cell data
- Bitphylogeny - Bayesian framework for intra-tumour phylogenies
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - statistical computing and network visualization
