Significant Gene-Tumor Pairs Identified by MutPanning


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Description

This table displays the significant gene-tumor pairs (i.e., associations between tumor types and significantly mutated genes) identified by MutPanning. For each gene-tumor pair, the table lists the gene name (HUGO nomenclature), the cancer type, in which the genes was found to be significantly mutated, its mutation frequency in the respective tumor type, as well as the false-discovery rate (q-value) returned by MutPanning. Further, we benchmarked our results against previous studies and datasets. For each gene-tumor pair, we searched whether it represented a canoncical cancer gene in the COSMIC cancer gene census, whether it was supported in the same tumor type in the literature or whether it was identified as significantly mutated for the same tumor type in previous computational studies.

On the left sidebar you can select the tumor types for which you would like to display the results. Some sequencing studies did not report synonymous mutations (6.1% of the samples). Cancer types that contain samples without synonymous mutations are marked by asterisks (*). Further, you can select between different FDR thresholds for which results should be included into the table. Additional information on the genes are available, including the q-values of an established recurrence-based approach and the p-values resulting from the different criteria used by MutPanning. These information can be displayed by selecting more columns in the left sidebar. Use the Cntrl and the Shift keys to select multiple columns.


Symbols

Gene reflects a canonical cancer gene in the COSMIC Cancer Gene Census

Previous studies in the literature support this gene in the same tumor type

Gene reported as significantly mutated in the TCGA marker papers

Gene reported as significantly mutated in the Bailey study (Bailey et al. Cell 2018)

Gene reported as significantly mutated on tumorportal.org (Lawrence et al. Nature 2014)

Gene identified using the dNdSCV tool (Martincorena et al. Cell 2017)

Significantly Mutated Genes Identified by MutPanning


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Description

This table displays the genes identified as significantly mutated by MutPanning. The table lists the name of each significant gene (HUGO nomenclature), the number of cancer types, in which the genes was found to be significantly mutated, its maximal mutation frequency across the significantly mutated tumor types, as well as the best (i.e., minimal) false-discovery rate (q-value) returned by MutPanning. Further, we benchmarked our results against previous studies and datasets. For each significantly mutated gene, we searched whether it represented a canoncical cancer gene in the COSMIC Cancer Gene Census, whether it was implicated in cancer in the literature or whether it was identified as significantly mutated for any tumor type in previous computational studies.

On the left sidebar you can select the tumor types for which you would like to display the results. Some sequencing studies did not report synonymous mutations (6.1% of the samples). Cancer types that contain samples without synonymous mutations are marked by asterisks (*). Further, you can select between different FDR thresholds for which results should be included into the table. Additional information on the genes are available, including the names of the tumor entities in which it was significantly mutated, the best q-value of an established recurrence-based approach and the best p-values resulting from the different criteria used by MutPanning. These information can be displayed by selecting more columns in the left sidebar. Use the Cntrl and the Shift keys to select multiple columns.


Symbols

Gene reflects a canonical cancer gene in the COSMIC Cancer Gene Census

Previous studies in the literature support this gene in the same tumor type

Gene reported as significantly mutated in the TCGA marker papers

Gene reported as significantly mutated in the Bailey study (Bailey et al. Cell 2018)

Gene reported as significantly mutated on tumorportal.org (Lawrence et al. Nature 2014)

Gene identified using the dNdSCV tool (Martincorena et al. Cell 2017)

Gene identified using a recurrence-based approch (Lawrence et al. Nature 2013)