CBaSE v1.1 is a tool which derives gene-specific probabilistic estimates of the strength of negative and positive selection in cancer. To use it, please upload a mutation annotation format (maf) file that contains the somatic point mutations that are to be investigated. The file should contain the following four tab-delimited columns in the given order (including a header):

(1) Gene symbol
– corresponds to the official gene symbol as used in the UCSC knownGene track
(2) Mutation effect
– one of {“missense”, “nonsense”, “coding-synon”}, denoting missense, nonsense (stop-gain and stop-loss), and synonymous mutations, respectively; optionally, 3'-UTR and 5'-UTR mutations (with mutation effects {“utr-3”, “utr-5”}) can be included
(3) Alternate allele
– the final allele after the mutation event; one of {A, C, G, T}
(4) Context index
– index of the sequence context of the reference allele; 0-based indices of tri- or pentanucleotide contexts can be looked up here

By default, all six possible models described in Weghorn & Sunyaev, Nature Genetics (2017) are fitted and model selection is done based on the Akaike information criterion ("Compare all"). Alternatively, q-values under one particular of the six model assumptions can be derived by choosing from the drop-down menu.

You can choose to compute the sequence context-dependent mutation matrix from either trinucleotide contexts or pentanucleotide contexts. Note that choosing pentanucleotides is only suitable for sufficiently large mutation data sets.

Use the maf file for head and neck squamous cell carcinoma (HNSC) deposited here as an example for the input format or to try out the algorithm. A desktop version of the algorithm can be downloaded here.

Enter your e-mail address to receive a notification once the results are ready. Alternatively, you can keep the browser window open and check back later. Runtime depends on server usage, model complexity and the size of the data set.

Note that you can also use the newer CBaSE v1.2, which is available for download as a standalone python script with documentation.

Upload batch file:
Query description:
Model selection:
Context selection:
E-mail address: