We assembled a training dataset with mutational features computed by intOGen-pipeline for each cancer gene/tumor type, including clusters/hotspots, domains enriched by mutations, biases towards mutation consequences, plus others such as post-translational modif. and conservation

IntOGen is a join work of many people in @bbglab

Two of the major results of the paper are:. 1) There's no magic recipe. But we give all the results and there are some patterns, so we hope that this can help the community. 2) The Union works surprisingly well to find cancer driver genes (maybe due to the robustness of IntOgen?)

We are pleased to announce that a new release of intOGen ( is out today! 🎉 Here is a thread summarizing the main updates: 👇

Using InPheRNo, we can identify cancer-specific TRNs. Below is a #BreastCancer specific TRN subnetwork, where transcript factors are the red nodes and the genes they regulate is green. Nodes with a blue border are listed as BRCA drivers by IntOGen and DriverDBv2.

First, cancer driver genes. We used the fantastic intOGen from @bbglab to detect cancer driver genes in each variant call set. As you can see, depending on which variant call set you use, you get very different results.

Conference time! First up ⁦@nlbigas⁩ telling us about intogen and boostDM which aim provide a compendium of mutations in cancer by screening all mutations in silico to determine “driverness” of mutations per cancer and per gene. #EACRbioinformatics #EACRvirtualevents

In het donker, @machinefabriek met zeer fraaie intogen en subtiele klanken. #hans19

Almost 5000 new samples are included, totalling 33019 tumors analyzed by intOGen. Among the tumor types that have the major increase are Pleural Mesothelioma and Burkitt Lymphoma. Also to highlight, the increase in Lung Adenocarcinoma, with 459 new samples.

We developed the Integrative OncoGenomics (IntOGen) pipeline that takes somatic mutations in a cohort as input, runs and combines 7 methods to find signals of positive selection, and outputs a catalog of cancer driver genes in the cohort alongside their mutational features 6/15

We integrated OpenVariant as the first step in the IntOGen pipeline (, processing 257,898,749 somatic mutations across 33,218 tumor samples represented through 271 cohorts sequenced by different sources, and stored in different data formats.

We are very happy to announce that a dual new release of intOGen ( and boostDM ( is out today! 🎉 Here is a thread summarizing the main updates:👇

We have mutations, now what? Our lab and others have developed methods to detect positive selection in the mutational pattern of genes across tumors. We applied them to blood mutations using IntoGen, which combines several discovery methods. Many thanks to method developers!(8/n)

So we have mutations, now what🧐?Our lab and others have developed methods to detect positive selection in genes across cancer samples. We applied them to blood mutations using IntoGen, which runs several discovery methods and combines them. Many thanks to methods developers🙌!

The new boostDM updates include:.✅ Model implementation changes.✅ Improved integration with intOGen.✅ Updated data dependencies.✅ Bug fixes

We have updated several databases used by intOGen pipeline (e.g. Ensembl, Oncotree, CGC, etc.), and we have developed and included a data parser, OpenVariant (. The pipeline is open source and available at

In this release we have:.✅ Increased the number of cohorts and samples.✅ Made small updates to the IntOGen pipeline.✅ Updated key third-party dependencies. ▶️ We produced a new list of 618 driver genes -

A compendium of mutational cancer driver genes.By @nlbigas & colleagues .They discuss the Integrative Oncogenomics (IntOGen) platform of annotating cancer driver mutations. Eg: “Mutation cloud” from ~900 #PancreaticCancer cases👇🏽.

Applying the IntOGen pipeline to the 221 tumor cohorts (28,076 samples) we generated the current snapshot of the compendium of cancer driver genes across 66 cancer types, which includes 568 genes. Available at 7/15