This report serves to describe the mutational landscape and properties of a given individual set, as well as rank genes and genesets according to mutational significance. MutSig vS2N was used to generate the results found in this report.
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Working with individual set: COAD
The input for this pipeline is a set of individuals with the following files associated for each:
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An annotated .maf file describing the mutations called for the respective individual, and their properties.
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A .wig file that contains information about the coverage of the sample.
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MAF used for this analysis:COAD.final_analysis_set.maf
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Significantly mutated genes (q ≤ 0.1): 36
Column Descriptions:
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N = number of sequenced bases in this gene across the individual set
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nnon = number of (nonsilent) mutations in this gene across the individual set
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nnull = number of (nonsilent) null mutations in this gene across the individual set
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nflank = number of noncoding mutations from this gene's flanking region, across the individual set
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nsil = number of silent mutations in this gene across the individual set
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p = p-value (overall)
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q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)
Table 1. Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 36. Number of genes displayed: 35. Click on a gene name to display its stick figure depicting the distribution of mutations and mutation types across the chosen gene (this feature may not be available for all significant genes).
| gene | N | nflank | nsil | nnon | nnull | p | q |
|---|---|---|---|---|---|---|---|
| KRAS | 15965 | 0 | 0 | 59 | 0 | 0 | 0 |
| TP53 | 19530 | 0 | 1 | 77 | 26 | 8e-80 | 7.6e-76 |
| APC | 153140 | 0 | 4 | 163 | 140 | 2.1e-60 | 1.3e-56 |
| BRAF | 40455 | 0 | 0 | 21 | 0 | 4.8e-55 | 2.3e-51 |
| NRAS | 11315 | 0 | 0 | 15 | 0 | 1.4e-38 | 5.4e-35 |
| PIK3CA | 65255 | 0 | 1 | 33 | 0 | 1.6e-21 | 5.1e-18 |
| FAM123B | 48050 | 0 | 1 | 19 | 14 | 7.3e-16 | 1.8e-12 |
| SOX9 | 19375 | 0 | 0 | 9 | 8 | 7.7e-16 | 1.8e-12 |
| CDC27 | 46345 | 0 | 4 | 12 | 2 | 1.8e-15 | 3.7e-12 |
| FAM22F | 17205 | 0 | 1 | 9 | 1 | 1.1e-14 | 2e-11 |
| EYS | 34875 | 1 | 1 | 9 | 3 | 2.2e-11 | 3.7e-08 |
| POTED | 7750 | 0 | 0 | 5 | 0 | 3.4e-10 | 5.3e-07 |
| TNFRSF10C | 11935 | 0 | 0 | 6 | 0 | 1e-09 | 1.5e-06 |
| SMAD4 | 28210 | 0 | 0 | 22 | 5 | 5.3e-09 | 7.2e-06 |
| FBXW7 | 44485 | 0 | 2 | 33 | 7 | 9.2e-09 | 0.000012 |
| ACOT4 | 16430 | 0 | 1 | 6 | 6 | 3.6e-08 | 0.000042 |
| HLA-B | 14880 | 0 | 0 | 5 | 5 | 6.7e-08 | 0.000074 |
| POTEB | 2945 | 1 | 3 | 6 | 2 | 7.3e-08 | 0.000077 |
| TBC1D10C | 10385 | 0 | 0 | 4 | 4 | 8.5e-08 | 0.000084 |
| USP40 | 54405 | 0 | 0 | 9 | 1 | 1.5e-07 | 0.00014 |
| CASP8 | 36425 | 0 | 0 | 11 | 4 | 2.8e-06 | 0.0025 |
| ZNF14 | 37665 | 0 | 0 | 7 | 6 | 6.5e-06 | 0.0056 |
| RBBP7 | 23870 | 0 | 0 | 6 | 0 | 0.000012 | 0.01 |
| ZNF19 | 25420 | 0 | 0 | 7 | 1 | 0.000028 | 0.022 |
| ZC3H13 | 81840 | 0 | 1 | 19 | 12 | 0.000038 | 0.028 |
| ADAM15 | 38750 | 0 | 0 | 7 | 1 | 0.000067 | 0.048 |
| ANKRD20A3 | 34410 | 0 | 0 | 6 | 1 | 7e-05 | 0.049 |
| HCLS1 | 24335 | 0 | 2 | 7 | 3 | 0.000075 | 0.05 |
| SMAD2 | 25420 | 0 | 1 | 11 | 4 | 0.000079 | 0.05 |
| ZNF479 | 31155 | 0 | 1 | 11 | 0 | 8e-05 | 0.05 |
| PSG9 | 24335 | 0 | 1 | 7 | 0 | 0.00011 | 0.066 |
| ASB5 | 17825 | 0 | 0 | 9 | 1 | 0.00013 | 0.075 |
| ARID1A | 90210 | 0 | 3 | 17 | 8 | 0.00015 | 0.084 |
| F5 | 127720 | 0 | 1 | 14 | 1 | 0.00016 | 0.091 |
| DLC1 | 84010 | 0 | 4 | 24 | 0 | 0.00017 | 0.093 |
In brief, we tabulate the number of mutations and the number of covered bases for each gene. The counts are broken down by mutation context category: four context categories that are discovered by MutSig, and one for indel and 'null' mutations, which include indels, nonsense mutations, splice-site mutations, and non-stop (read-through) mutations. For each gene, we calculate the probability of seeing the observed constellation of mutations, i.e. the product P1 x P2 x ... x Pm, or a more extreme one, given the background mutation rates calculated across the dataset. [1]
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.