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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