UCL Cancer Institute


Computational Cancer Genomics Research Group

Our research group focuses on the design and development of computational methods to analyse tumour sequencing data and to study the cancer evolutionary process.

Group Leader: Dr Simone Zaccaria



Recent sequencing technologies provide an effective way to investigate the cancer evolutionary process, with critical impact on both diagnosis, prognosis, and treatment. Therefore, a large amount of multi-dimensional and multi-omics data are routinely produced by cutting-edge technologies, including single-cell, bulk, and spatial sequencing as well as liquid biopsies and sequencing of metastases. While these data offer an unprecedented view of tumor evolution, the combination of such technologies with formal consolidated methodologies is the key to realize their full potential.


The goal of our lab is to design and develop computational methods that leverage the features of the most recent sequencing technologies to investigate the complex tumor evolution and heterogeneity. Such methods are thus based on rigorous mathematical models and algorithms that we specifically design for analyzing the data produced by different technologies. By framing biological questions as computational problems, we enable the integration of multiple sources of information into the solutions, revealing novel insights about the cancer evolutionary process.

Open positions

No funded open positions are available at this time.

Selected Publications

The full list of publications is available in Google Scholar, ORCID, or Scopus as well as here in Our Publications. Author order generally follows convention in biology, with First Author listed first, and additionally: † indicates Joint First authorship, and * indicates corresponding authorsip.

  1. Zaccaria, S., Raphael, B.J. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nature Biotechnology (2020). https://doi.org/10.1038/s41587-020-0661-6
  2. Zaccaria, S., Raphael, B.J. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nature Communications 11, 4301 (2020). https://doi.org/10.1038/s41467-020-17967-y
  3. Myers, M.A., Zaccaria, S.†, Raphael, B.J. Identifying tumor clones in sparse single-cell mutation data, Bioinformatics 36: i186–i193 (2020). Accepted and presented at ISMB 2020. https://doi.org/10.1093/bioinformatics/btaa449
  4. Satas, G., Zaccaria, S., Mon, G., & Raphael, B. J. SCARLET: Single-Cell Tumor Phylogeny Inference with Copy-Number Constrained Mutation Losses. Cell Systems10(4): 323-332 (2020). Accepted and presented at RECOMB 2020. https://doi.org/10.1016/j.cels.2020.04.001
  5. Zaccaria, S., El-Kebir, M., Klau, G. W., & Raphael, B. J. Phylogenetic copy-number factorization of multiple tumor samples. Journal of Computational Biology25(7): 689-708 (2018). Accepted and presented at RECOMB 2017. https://doi.org/10.1089/cmb.2017.0253
  6. Pirola, Y., Zaccaria, S.†, Dondi, R., Klau, G. W., Pisanti, N., & Bonizzoni, P. HapCol: accurate and memory-efficient haplotype assembly from long reads. Bioinformatics32(11), 1610-1617 (2016). Accepted and presented at ISMB-HITSeq 2015. https://doi.org/10.1093/bioinformatics/btv495
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