Mutual exclusion statistics and data events in Gitools

We’re pleased to announce another incremental release of Gitools, version 2.2. Amongst the many improvements (listed at the bottom of this post) we’d like to highlight the effort that we put into improving performance, specifically with genomic data: mutual exclusion and co-occurrence statistics coupled with a new feature called “data events” – which helps to get a quick grasp of the data.

Low expression events ordered by mutual exclusion

Low expression data events events ordered by mutual exclusion

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Identifying disease related mutations with Condel 2.0

Three years ago, Abel developed and published in the American Journal of Human Genetics an approach to combine the results of several tools aimed at identifying disease-related single nucleotide variants (SNVs). He called the strategy a Consensus Deleteriousness score of SNVs, or Condel. It consisted in computing a weighted average of the scores of five of these tools (SIFT, PolyPhen2, MutationAssessor, LogRE and MAPP). The weights were extracted from the complementary cumulative distributions of the scores of sets of known disease-related and neutral SNVs. He showed that the Consensus score of the five tools outperformed the five individual methods, as well as other approaches to combine them. He presented the Condel of these five tools in one of the first posts of this blog, The making of Condel (CONsensus DELeteriousness Score), published on April 1, 2011.

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How to perform a hierarchical clustering using interactive heatmaps in Gitools

In the latest version of Gitools, version 2.1, we have improved the clustering of heatmaps. Here we explain in detail on how to perform and interpret the hierarchical clustering result – and why it is a bit different than the rest.

Hierarchical clustering in Gitools: The lines in the header represent the hierarchical tree splitting, the root at the bottom, the leafs at the top

Hierarchical clustering in Gitools: The lines in the heatmap header represent the hierarchical tree (Dendrogram) splitting at different levels. The root of the tree is located at the bottom, the leafs at the top. See video at YouTube

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Practical Workshop on “Cancer Omics Data Integration” in Heidelberg

Since last year I am participating in the COST Action BM1204 titled “An integrated European platform for pancreas cancer research: from basic science to clinical and public health interventions for a rare disease”, and in particular in its Work Group focus on “Omics Data Integration”.


As part of this Work Group, we have prepared a training workshop that will take place in Heidelberg, Germany on February 14, from 1-5pm. The covered topics fit into a series of workshops that will be organized within this COST Action.

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Our lab receives funding from La Marató de TV3

We are very honored to announce that we have received funds for a 3-year project from the money raised in the last telethon broadcast La Marató de TV3.



This is an annual television program broadcasted in Catalonia since 1996 which is devoted to diseases that are currently incurable. La Marató has a big repercussion in our country, not only for its ability to raise funds but also because it fulfills the task of informing the public about these diseases, the state of the art of the treatment and the importance of the research to advance in their prevention and cure.

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Our research explained in a short video

The University Pompeu Fabra has produced a video in which we explain in brief our research. We recorded three versions of the video, one in Catalan, one in Spanish and one in English. The videos are distributed through the UPF youtube channel.

I leave you here with the English version of the video. For the Catalan version follow this link. And this link for the Spanish version.


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My slides on: Identification of cancer drivers across tumor types

Yesterday I gave a talk at the PRBB Computational Genomics Seminars Series. In that talk I summarized our work of this year in the lab. Basically, we have developed methods to identify cancer driver genes and we have applied them to thousands of tumor resequenced genomes. Here, I leave you the slides, and I summarize the talk below.


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