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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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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Comprehensive identification of mutational cancer driver genes across 12 tumor types

The Cancer Genome Atlas (TCGA) is an initiative supported by the National Cancer Institute and the National Human Genome Research Institute. Its immediate aim is to obtain a catalog of the molecular changes that occur in hundreds of tumors from the more common cancers in population. One year ago, the Pan-Cancer analysis group was formed within the TCGA, with the goal of analyzing alterations surveyed across several cancer types with an integrative approach. The rationale was that such across tumor types integrative analysis would provide insights to the process of tumorigenesis that could not be obtained from the analysis of each dataset separately. As explained in a previous post, we have had the opportunity to participate in this exciting project, whose marker paper has been recently published in Nature Genetics. On detail, our role was to analyze the mutations of the Pan-Cancer data set to identify genes that drive tumorigenesis in one or more of these cancer types. The results of this analysis have been published today in Nature Scientific Reports.

The Cancer Genome Atlas Pan-Cancer Project

Yesterday the paper describing TCGA Pan-Cancer Project was published in Nature Genetics. We’ve had the opportunity to participate in this exciting project and here I would like to explain our experience and contribution to it.


We have been interested for quite a while in the study of patterns of genomics alterations in cancer across tumor types. Thus a project like the TCGA Pan-Cancer provided a unique opportunity to apply our tools and expertise to a unique collection of data.


In the past few years we have developed computational methodologies to identify cancer drivers by analyzing the patterns of somatic mutations across tumors (i.e OncodriveFM and OncodriveCLUST) as well as tools to facilitate the visual exploration of multidimensional cancer genomics datasets (i.e. GitoolsIntOGen, see our review on this topic if you are interested in this), we now had the opportunity to apply those tools to TCGA Pan-Cancer data.

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Chromatin maintenance and cancer

It is now well established that cancer is a collection of mostly genetic diseases. They progress through the accumulation of alterations, such as point mutations in genes that affect mechanisms, often known as the hallmarks of cancer which ultimately confer the altered cell some advantageous properties with respect to neighboring ‘normal’ cells. Decades of intense research on the molecular biology of cancer have delineated many of such hallmarks. Nevertheless, some others have only begun to appear with the advent of large Cancer Genomics initiatives, such as The Cancer Genome Atlas and the International Cancer Genome Consortium.

One of these novel hallmarks has to do with the alteration of general mechanisms of chromatin regulation and maintenance. It is now clear that these mechanisms become altered in one way or another across many tumor types, and that their alteration in principle could lead to the de-regulation of several cellular functions that promote tumorigenesis. With this in mind, we have examined the mutations that occur in chromatin regulatory factors (CRFs) across 4623 tumor samples representing 31 cancer genome re-sequencing projects from 13 anatomical sites. The results of this study have just been published in Genome Biology.


I want to highlight here the main findings of our study.

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