Mutation Analysis (MutSig v1.5)
Skin Cutaneous Melanoma (Metastatic)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1RR1XCZ
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C1RR1XCZ
Overview
Introduction

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 v1.5 was used to generate the results found in this report.

  • Working with individual set: SKCM-TM

  • Number of patients in set: 290

Input

The input for this pipeline is a set of individuals with the following files associated for each:

  1. An annotated .maf file describing the mutations called for the respective individual, and their properties.

  2. A .wig file that contains information about the coverage of the sample.

Summary

  • MAF used for this analysis:SKCM-TM.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 63

  • Mutations seen in COSMIC: 797

  • Significantly mutated genes in COSMIC territory: 58

  • Significantly mutated genesets: 2

  • Significantly mutated genesets: (excluding sig. mutated genes):0

Mutation Preprocessing

  • Read 290 MAFs of type "Broad"

  • Total number of mutations in input MAFs: 276749

  • After removing 22 mutations outside chr1-24: 276727

  • After removing 1815 blacklisted mutations: 274912

  • After removing 9302 noncoding mutations: 265610

  • After collapsing adjacent/redundant mutations: 240711

Mutation Filtering

  • Number of mutations before filtering: 240711

  • After removing 11114 mutations outside gene set: 229597

  • After removing 615 mutations outside category set: 228982

  • After removing 8 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 217245

Results
Breakdown of Mutations by Type

Table 1.  Get Full Table Table representing breakdown of mutations by type.

type count
Frame_Shift_Del 975
Frame_Shift_Ins 256
In_Frame_Del 259
In_Frame_Ins 34
Missense_Mutation 135897
Nonsense_Mutation 8321
Nonstop_Mutation 66
Silent 77117
Splice_Site 5600
Translation_Start_Site 457
Total 228982
Breakdown of Mutation Rates by Category Type

Table 2.  Get Full Table A breakdown of mutation rates per category discovered for this individual set.

category n N rate rate_per_mb relative_rate exp_ns_s_ratio
(C/T)p*C->T 104115 2314828135 0.000045 45 2.5 1.6
(A/G)p*C->T 11836 1943471236 6.1e-06 6.1 0.34 1.9
A->G 5813 4105447683 1.4e-06 1.4 0.078 2.3
transver 14581 8363747054 1.7e-06 1.7 0.096 5
indel+null 14959 8363747054 1.8e-06 1.8 0.099 NaN
double_null 556 8363747054 6.6e-08 0.066 0.0037 NaN
Total 151860 8363747054 0.000018 18 1 3.5
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom). If the figure is unpopulated, then full coverage is assumed (e.g. MutSig CV doesn't use WIGs and assumes full coverage).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: SKCM-TM.patients.counts_and_rates.txt

Lego Plots

The mutation spectrum is depicted in the lego plots below in which the 96 possible mutation types are subdivided into six large blocks, color-coded to reflect the base substitution type. Each large block is further subdivided into the 16 possible pairs of 5' and 3' neighbors, as listed in the 4x4 trinucleotide context legend. The height of each block corresponds to the mutation frequency for that kind of mutation (counts of mutations normalized by the base coverage in a given bin). The shape of the spectrum is a signature for dominant mutational mechanisms in different tumor types.

Figure 3.  Get High-res Image SNV Mutation rate lego plot for entire set. Each bin is normalized by base coverage for that bin. Colors represent the six SNV types on the upper right. The three-base context for each mutation is labeled in the 4x4 legend on the lower right. The fractional breakdown of SNV counts is shown in the pie chart on the upper left. If this figure is blank, not enough information was provided in the MAF to generate it.

Figure 4.  Get High-res Image SNV Mutation rate lego plots for 4 slices of mutation allele fraction (0<=AF<0.1, 0.1<=AF<0.25, 0.25<=AF<0.5, & 0.5<=AF) . The color code and three-base context legends are the same as the previous figure. If this figure is blank, not enough information was provided in the MAF to generate it.

CoMut Plot

Figure 5.  Get High-res Image The matrix in the center of the figure represents individual mutations in patient samples, color-coded by type of mutation, for the significantly mutated genes. The rate of synonymous and non-synonymous mutations is displayed at the top of the matrix. The barplot on the left of the matrix shows the number of mutations in each gene. The percentages represent the fraction of tumors with at least one mutation in the specified gene. The barplot to the right of the matrix displays the q-values for the most significantly mutated genes. The purple boxplots below the matrix (only displayed if required columns are present in the provided MAF) represent the distributions of allelic fractions observed in each sample. The plot at the bottom represents the base substitution distribution of individual samples, using the same categories that were used to calculate significance.

Significantly Mutated Genes

Column Descriptions:

  • N = number of sequenced bases in this gene across the individual set

  • n = number of (nonsilent) mutations in this gene across the individual set

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • nsil = number of silent mutations in this gene across the individual set

  • n1 = number of nonsilent mutations of type: (C/T)p*C->T

  • n2 = number of nonsilent mutations of type: (A/G)p*C->T

  • n3 = number of nonsilent mutations of type: A->G

  • n4 = number of nonsilent mutations of type: transver

  • n5 = number of nonsilent mutations of type: indel+null

  • n6 = number of nonsilent mutations of type: double_null

  • p_ns_s = p-value for the observed nonsilent/silent ratio being elevated in this gene

  • p = p-value (overall)

  • q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 3.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 63. 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).

rank gene description N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 BRAF v-raf murine sarcoma viral oncogene homolog B1 643778 169 145 19 5 14 17 5 129 4 0 2.3e-11 <1.00e-15 <1.76e-11
2 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 169887 90 87 10 1 2 2 36 48 2 0 7.2e-10 3.11e-15 1.76e-11
3 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 261451 46 42 24 2 16 1 1 2 26 0 0.00014 3.33e-15 1.76e-11
4 TP53 tumor protein p53 351504 53 48 43 2 22 3 4 4 19 1 5.6e-07 3.89e-15 1.76e-11
5 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 326008 25 25 23 0 2 0 3 5 15 0 0.0085 6.77e-15 2.45e-11
6 LCE1B late cornified envelope 1B 104550 15 15 15 0 10 0 0 3 2 0 0.024 4.15e-10 1.25e-06
7 RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 178685 20 20 9 1 17 0 0 3 0 0 0.00085 7.83e-10 2.02e-06
8 LUZP2 leucine zipper protein 2 289644 28 27 25 4 19 1 1 2 5 0 0.012 2.69e-09 6.08e-06
9 SLC38A4 solute carrier family 38, member 4 453632 37 33 31 4 29 1 1 4 2 0 0.00049 9.31e-09 1.87e-05
10 GRXCR1 glutaredoxin, cysteine rich 1 253738 26 23 22 4 16 2 2 1 5 0 0.026 4.31e-08 7.78e-05
11 PRB2 proline-rich protein BstNI subfamily 2 362114 45 35 42 2 39 1 1 3 1 0 0.027 5.01e-07 0.000822
12 SPATA8 spermatogenesis associated 8 95614 16 13 11 0 14 0 0 1 1 0 0.0026 6.72e-07 0.00101
13 HIST1H2AA histone cluster 1, H2aa 115998 11 11 9 1 6 1 0 3 1 0 0.095 8.05e-07 0.00112
14 AGXT2 alanine-glyoxylate aminotransferase 2 425343 26 23 22 2 19 1 0 3 3 0 0.0029 1.62e-06 0.00202
15 NAP1L2 nucleosome assembly protein 1-like 2 396425 29 25 27 3 20 2 3 2 2 0 0.0067 1.68e-06 0.00202
16 HBD hemoglobin, delta 130987 17 16 13 3 15 1 0 1 0 0 0.0031 2.22e-06 0.00240
17 ZNF98 zinc finger protein 98 (F7175) 280620 24 22 21 4 18 1 2 1 2 0 0.031 2.35e-06 0.00240
18 NMS neuromedin S 143259 14 13 13 1 9 1 0 0 4 0 0.014 2.41e-06 0.00240
19 NRK Nik related kinase 724055 49 44 46 7 37 2 0 3 7 0 0.0024 2.52e-06 0.00240
20 RUNX1T1 runt-related transcription factor 1; translocated to, 1 (cyclin D-related) 533396 45 36 40 6 31 3 1 4 6 0 0.001 2.74e-06 0.00248
21 DEFB119 defensin, beta 119 149908 15 15 14 2 14 1 0 0 0 0 0.083 3.47e-06 0.00299
22 MPP7 membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7) 502610 39 32 32 6 30 1 2 2 3 1 0.0024 3.83e-06 0.00314
23 GLRB glycine receptor, beta 422429 34 30 27 7 23 2 2 3 4 0 0.0097 5.10e-06 0.00369
24 VEGFC vascular endothelial growth factor C 345773 23 20 20 2 16 1 0 0 6 0 0.0031 5.11e-06 0.00369
25 GML glycosylphosphatidylinositol anchored molecule like protein 141803 12 11 11 1 8 1 0 2 1 0 0.046 5.26e-06 0.00369
26 PSG4 pregnancy specific beta-1-glycoprotein 4 371289 37 31 31 6 29 2 0 4 2 0 0.00092 5.32e-06 0.00369
27 ACSM2B acyl-CoA synthetase medium-chain family member 2B 505016 55 42 43 10 40 4 1 2 8 0 0.00012 5.90e-06 0.00395
28 RBM11 RNA binding motif protein 11 187430 13 13 11 1 8 1 0 1 3 0 0.19 6.37e-06 0.00411
29 GK2 glycerol kinase 2 481691 40 34 33 6 30 1 1 4 3 1 0.015 7.11e-06 0.00443
30 CDH9 cadherin 9, type 2 (T1-cadherin) 680990 47 37 41 5 38 2 1 4 1 1 0.0013 9.26e-06 0.00558
31 PPP6C protein phosphatase 6, catalytic subunit 291462 21 20 15 3 14 0 0 2 5 0 0.11 1.10e-05 0.00642
32 ZNF679 zinc finger protein 679 245322 28 27 24 6 18 2 1 6 1 0 0.12 1.74e-05 0.00980
33 SNAP91 synaptosomal-associated protein, 91kDa homolog (mouse) 444714 41 31 39 6 28 3 3 2 5 0 0.011 1.93e-05 0.0106
34 USP17L2 ubiquitin specific peptidase 17-like 2 390651 25 22 24 3 16 0 2 3 4 0 0.0021 2.52e-05 0.0134
35 DEFB118 defensin, beta 118 109403 10 10 9 1 8 0 0 1 1 0 0.18 2.60e-05 0.0134
BRAF

Figure S1.  This figure depicts the distribution of mutations and mutation types across the BRAF significant gene.

NRAS

Figure S2.  This figure depicts the distribution of mutations and mutation types across the NRAS significant gene.

CDKN2A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

TP53

Figure S4.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

PTEN

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

LCE1B

Figure S6.  This figure depicts the distribution of mutations and mutation types across the LCE1B significant gene.

RAC1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the RAC1 significant gene.

LUZP2

Figure S8.  This figure depicts the distribution of mutations and mutation types across the LUZP2 significant gene.

SLC38A4

Figure S9.  This figure depicts the distribution of mutations and mutation types across the SLC38A4 significant gene.

GRXCR1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the GRXCR1 significant gene.

SPATA8

Figure S11.  This figure depicts the distribution of mutations and mutation types across the SPATA8 significant gene.

HIST1H2AA

Figure S12.  This figure depicts the distribution of mutations and mutation types across the HIST1H2AA significant gene.

AGXT2

Figure S13.  This figure depicts the distribution of mutations and mutation types across the AGXT2 significant gene.

NAP1L2

Figure S14.  This figure depicts the distribution of mutations and mutation types across the NAP1L2 significant gene.

HBD

Figure S15.  This figure depicts the distribution of mutations and mutation types across the HBD significant gene.

ZNF98

Figure S16.  This figure depicts the distribution of mutations and mutation types across the ZNF98 significant gene.

NRK

Figure S17.  This figure depicts the distribution of mutations and mutation types across the NRK significant gene.

RUNX1T1

Figure S18.  This figure depicts the distribution of mutations and mutation types across the RUNX1T1 significant gene.

MPP7

Figure S19.  This figure depicts the distribution of mutations and mutation types across the MPP7 significant gene.

GLRB

Figure S20.  This figure depicts the distribution of mutations and mutation types across the GLRB significant gene.

GML

Figure S21.  This figure depicts the distribution of mutations and mutation types across the GML significant gene.

PSG4

Figure S22.  This figure depicts the distribution of mutations and mutation types across the PSG4 significant gene.

ACSM2B

Figure S23.  This figure depicts the distribution of mutations and mutation types across the ACSM2B significant gene.

RBM11

Figure S24.  This figure depicts the distribution of mutations and mutation types across the RBM11 significant gene.

GK2

Figure S25.  This figure depicts the distribution of mutations and mutation types across the GK2 significant gene.

CDH9

Figure S26.  This figure depicts the distribution of mutations and mutation types across the CDH9 significant gene.

PPP6C

Figure S27.  This figure depicts the distribution of mutations and mutation types across the PPP6C significant gene.

ZNF679

Figure S28.  This figure depicts the distribution of mutations and mutation types across the ZNF679 significant gene.

SNAP91

Figure S29.  This figure depicts the distribution of mutations and mutation types across the SNAP91 significant gene.

USP17L2

Figure S30.  This figure depicts the distribution of mutations and mutation types across the USP17L2 significant gene.

COSMIC analyses

In this analysis, COSMIC is used as a filter to increase power by restricting the territory of each gene. Cosmic version: v48.

Table 4.  Get Full Table Significantly mutated genes (COSMIC territory only). To access the database please go to: COSMIC. Number of significant genes found: 58. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 53 356 49 103240 5066 0 0
2 STK19 serine/threonine kinase 19 19 2 8 580 16 1.2e-14 2.8e-11
3 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 15 5 12 1450 17904 3e-14 4.5e-11
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 90 33 88 9570 108786 1.7e-13 1.9e-10
5 BRAF v-raf murine sarcoma viral oncogene homolog B1 169 89 162 25810 2112084 3.4e-13 2.7e-10
6 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 46 332 46 96280 1770 3.6e-13 2.7e-10
7 EPHA6 EPH receptor A6 78 8 6 2320 6 7.5e-12 4.8e-09
8 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 25 767 24 222430 423 1.2e-11 6.8e-09
9 OR4S2 olfactory receptor, family 4, subfamily S, member 2 19 1 4 290 4 3.1e-11 1.6e-08
10 EPHA7 EPH receptor A7 54 13 5 3770 5 1.2e-08 5.3e-06

Note:

n - number of (nonsilent) mutations in this gene across the individual set.

cos = number of unique mutated sites in this gene in COSMIC

n_cos = overlap between n and cos.

N_cos = number of individuals times cos.

cos_ev = total evidence: number of reports in COSMIC for mutations seen in this gene.

p = p-value for seeing the observed amount of overlap in this gene)

q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Geneset Analyses

Table 5.  Get Full Table A Ranked List of Significantly Mutated Genesets. (Source: MSigDB GSEA Cannonical Pathway Set).Number of significant genesets found: 2. Number of genesets displayed: 10

rank geneset description genes N_genes mut_tally N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 ST_G_ALPHA_S_PATHWAY The G-alpha-s protein activates adenylyl cyclases, which catalyze cAMP formation. ASAH1, BF, BFAR, BRAF, CAMP, CREB1, CREB3, CREB5, EPAC, GAS, GRF2, MAPK1, RAF1, SNX13, SRC, TERF2IP 12 BFAR(2), BRAF(169), CAMP(2), CREB3(1), CREB5(9), MAPK1(4), RAF1(12), SNX13(5), SRC(2), TERF2IP(2) 4766845 208 163 56 34 42 20 6 133 7 0 0.000057 2e-15 1.2e-12
2 SA_G1_AND_S_PHASES Cdk2, 4, and 6 bind cyclin D in G1, while cdk2/cyclin E promotes the G1/S transition. ARF1, ARF3, CCND1, CDK2, CDK4, CDKN1A, CDKN1B, CDKN2A, CFL1, E2F1, E2F2, MDM2, NXT1, PRB1, TP53 15 CCND1(2), CDK2(1), CDK4(8), CDKN1A(3), CDKN1B(2), CDKN2A(46), CFL1(2), E2F1(6), E2F2(5), MDM2(4), NXT1(2), PRB1(14), TP53(53) 3612573 148 99 111 21 65 8 7 16 51 1 2.1e-09 6e-06 0.0018
3 TERTPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. HDAC1, MAX, MYC, SP1, SP3, TP53, WT1, ZNF42 7 HDAC1(2), MAX(2), MYC(5), SP1(5), SP3(1), TP53(53), WT1(10) 3030379 78 59 67 9 36 7 7 5 22 1 3e-06 0.022 1
4 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(12) 306695 12 12 11 3 10 1 1 0 0 0 0.13 0.26 1
5 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(5) 219519 5 5 5 1 5 0 0 0 0 0 0.37 0.38 1
6 SA_REG_CASCADE_OF_CYCLIN_EXPR Expression of cyclins regulates progression through the cell cycle by activating cyclin-dependent kinases. CCNA1, CCNA2, CCND1, CCNE1, CCNE2, CDK2, CDK4, CDKN1B, CDKN2A, E2F1, E2F2, E2F4, PRB1 13 CCNA1(16), CCND1(2), CCNE1(4), CCNE2(6), CDK2(1), CDK4(8), CDKN1B(2), CDKN2A(46), E2F1(6), E2F2(5), E2F4(3), PRB1(14) 3848861 113 85 85 22 60 2 5 15 31 0 5e-05 0.5 1
7 HSA00401_NOVOBIOCIN_BIOSYNTHESIS Genes involved in novobiocin biosynthesis GOT1, GOT2, TAT 3 GOT1(6), GOT2(6), TAT(21) 1124647 33 21 32 9 24 3 2 1 3 0 0.012 0.91 1
8 RNAPATHWAY dsRNA-activated protein kinase phosphorylates elF2a, which generally inhibits translation, and activates NF-kB to provoke inflammation. CHUK, DNAJC3, EIF2S1, EIF2S2, MAP3K14, NFKB1, NFKBIA, PRKR, RELA, TP53 9 CHUK(2), DNAJC3(3), EIF2S1(1), NFKB1(6), NFKBIA(2), RELA(4), TP53(53) 4134379 71 60 61 11 32 6 5 6 21 1 0.00017 0.94 1
9 BOTULINPATHWAY Blockade of Neurotransmitter Relase by Botulinum Toxin CHRM1, CHRNA1, SNAP25, STX1A, VAMP2 5 CHRM1(5), CHRNA1(8), SNAP25(9), VAMP2(1) 1396359 23 21 21 4 16 1 0 3 3 0 0.0023 0.96 1
10 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(4), TPI1(1) 708066 5 5 4 1 3 0 0 2 0 0 0.34 0.98 1

Table 6.  Get Full Table A Ranked List of Significantly Mutated Genesets (Excluding Significantly Mutated Genes). Number of significant genesets found: 0. Number of genesets displayed: 10

rank geneset description genes N_genes mut_tally N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(12) 306695 12 12 11 3 10 1 1 0 0 0 0.13 0.26 1
2 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(5) 219519 5 5 5 1 5 0 0 0 0 0 0.37 0.38 1
3 HSA00401_NOVOBIOCIN_BIOSYNTHESIS Genes involved in novobiocin biosynthesis GOT1, GOT2, TAT 3 GOT1(6), GOT2(6), TAT(21) 1124647 33 21 32 9 24 3 2 1 3 0 0.012 0.91 1
4 BOTULINPATHWAY Blockade of Neurotransmitter Relase by Botulinum Toxin CHRM1, CHRNA1, SNAP25, STX1A, VAMP2 5 CHRM1(5), CHRNA1(8), SNAP25(9), VAMP2(1) 1396359 23 21 21 4 16 1 0 3 3 0 0.0023 0.96 1
5 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(4), TPI1(1) 708066 5 5 4 1 3 0 0 2 0 0 0.34 0.98 1
6 HSA00785_LIPOIC_ACID_METABOLISM Genes involved in lipoic acid metabolism LIAS, LIPT1, LOC387787 2 LIAS(2), LIPT1(2) 659766 4 3 4 0 0 2 0 1 1 0 0.33 0.98 1
7 TCRMOLECULE T Cell Receptor and CD3 Complex CD3D, CD3E, CD3G, CD3Z, TRA@, TRB@ 3 CD3D(7), CD3E(2), CD3G(1) 480032 10 7 10 4 8 0 0 0 2 0 0.4 0.99 1
8 INOSITOL_METABOLISM ALDH6A1, ALDOA, ALDOB, ALDOC, TPI1 5 ALDH6A1(4), ALDOA(1), ALDOB(14), ALDOC(4), TPI1(1) 1685752 24 19 21 7 17 2 0 2 3 0 0.034 1 1
9 FOSBPATHWAY FOSB gene expression and drug abuse CDK5, FOSB, GRIA2, JUND, PPP1R1B 5 CDK5(6), FOSB(6), GRIA2(43), JUND(1), PPP1R1B(2) 1573577 58 50 55 22 35 3 2 9 9 0 0.11 1 1
10 TUBBYPATHWAY Tubby is activated by phospholipase C activity and hydrolysis of PIP2, after which it enters the nucleus and regulates transcription. CHRM1, GNAQ, GNB1, GNGT1, HTR2C, PLCB1, TUB 6 CHRM1(5), GNAQ(6), GNB1(1), PLCB1(61), TUB(10) 2651089 83 55 75 29 64 2 2 7 8 0 0.0065 1 1
Methods & Data
Methods

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]

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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