Figure. Xena mutation view supports examination of both coding and non-coding mutations from whole genome analysis. The new visualizations support viewing mutations from both gene- or coordinate- centric perspective. In the gene-centric view, it also supports dynamic toggle to show or hide introns from the view. This figure shows the frequent intron mutations in 321 samples from the ICGC lymphoma cohorts (https://xenabrowser.net/heatmap/?bookmark=d2a79e46e22456036a732c49c2e4c5b3). These 'pile-ups' would be not be visible if viewing mutations only in the exome. These intron mutations overlap with known enhancers regions (Mathelier 2015).
Mathelier A, Lefebvre C, Zhang AW, Arenillas DJ, Ding J, Wasserman WW, Shah SP. Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas Genome Biology. 2015; 16:84.
View Mutations Side-by-side with Other Data
View mutations side-by-side with other data, such as gene expression. As shown above, lower TP53 expression correlates with nonsense, frame-shift mutations; and higher expression is associated with missense mutations.
How to Read a Xena Mutation Column
In our Visual spreadsheet each row is a sample. A mutation column shows the transcript along the top with alternating light and dark gray exons in the order of transcription. We buffer our exons to include 5bp splice sites on each side of the exons. We also include 1kb upstream of the transcription start site to show promoter mutations. Mutations are colored by their functional impact with mutations predicted to not make a protein in red, mutations predicted to make a protein but perhaps with altered function in blue, and synonymous mutations in green. Learn more information about how we characterize mutations.
Get started by going to our Visualization tab, following the wizard to enter your gene of interest into the Visual spreadsheet, which is our default view. Be sure to chose a 'Somatic Mutation SNPs and small INDELs' datasets.