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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New IntOGen Somatic Mutations Analysis version available

We are proud to announce the brand new version of the IntOGen Somatic Mutations Analysis (IntOGen SM) pipeline. We call it versionĀ 2.0.0 as it has been completely rewritten from scratch with a strong focus on quality, efficiency and scalability.

The IntOGen SM pipeline addresses the challenge of identifying which somatic mutations are important for the development of tumors. The input for the analysis is a list of somatic mutations detected in a cohort of tumors. Read the rest of this entry »

Condel for prioritization of variants involved in hereditary diseases and transFIC for cancer

We have worked during the last years on assessing the functional impact of non-synonymous variants (nsSNVs). As a result, we have published two new approaches Condel and transFIC. In this post I would like to clarify the differences between one and the other, and give our recommendations on when each of them should be used.

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How to evaluate the performance of computational methods to identify driver mutations?

We have recently published transFIC, a computational method to assess the functional impact of somatic cancer mutations (see this post). To evaluate the performance of transFIC we needed a dataset of driver and passanger mutations. However, we faced a common problem in this field: there is not such dataset that can be trusted and is not biased. Thus, it was a challenge to properly evaluate the performance of transFIC and compare it to other methods with similar aim.

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How to prioritize cancer somatic mutations?

Projects that sequence the genomes of a cohort of tumor samples are faced with the challenge of deciding which somatic mutations are relevant to tumor development (drivers). The exome of an individual tumor sample normally contains a few dozens of somatic mutations, most of which are thought to be passenger, i.e., they do not contribute to the tumor phenotype. Very often, cancer resequencing projects use known tools that assess the functional impact of individual mutations (eg. SIFT, PolyPhen2, Mutation Assessor) or use their recurrence across tumor samples to rank somatic variants. They also resort to accumulated knowledge by focusing on mutations that appear in known cancer genes. There are only few bioinformatics tools available to rank somatic mutations according to their likelihood of promoting tumorigenesis. Amongst them are CanPredict and CHASM. (Here is a comprehensive review.)

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