paper in Nature Communications on cancer biogeography

We have published a new paper in Nature Communications where we show how statistical phylogenetics can help to understand tumoral dynamics over time and space:
Fig. 3

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Excellence grant from the Galician government!

We have been awarded an excellence 3-year structural grant for research groups from the Galician government.
So now our GXB group has been stamped as Competitive Reference Group.
We want to thank Xunta de Galicia for this support

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New PhD thesis in the lab!

Congratulations to Nuria for passing with honors her Ph.D. viva!IMG_0028

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New paper on germline evolution of human cancer genes by Alberto Vicens

Screenshot 2018-11-30 at 10.14.40

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Comparing single-cell whole-genome amplification strategies

We finally put it together:


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Welcome to Monica and Fabian!

Monica Valecha (India) and Fabián Crespo (Cuba/Ecuador) just arrived to start as PhD students in the CONTRA Marie Curie Innovative Training Network on Computational Oncology.  They are going to work on different aspects of cancer evolution.


Welcome to the lab!.

Now we have people from three continents!

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Merly got her PhD

Congratulations to Merly for her PhD awarded today on NGS phylogenomics! You’ve been a GREAT student. Good luck for your postdoc at UC Santa Cruz. We’ll miss you…



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NGS coverage and evolutionary inferences from single-cells

New paper just out @GenomeMedicine! We report the impact of sequencing depth (and sampling effort) towards variant detection, genotype accuracy, clonal inference and phylogenetic reconstruction from single-cell cancer data.


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NGSphy: phylogenomic simulation of next-generation sequencing data

NGSphy, our tool for simulating NGS reads along gene trees or genome-wide along a species tree with multiple gene families, is now published in Bioinformatics.

Source code, full documentation, and tutorials including a “Getting started” guide are available at

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Substitution Rates Predicted by Stability-Constrained Models of Protein Evolution Are Not Consistent with Empirical Data

Both stability-constrained and structure-constrained substitution models are required to realistically predict the effect of mutations during protein evolution. A great study with Ugo Bastolla’s lab just published in Molecular Biology and Evolution,

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