Mutation Assessor - Rectum Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Rectum Adenocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1CV4H6H

Overview

Introduction

This report serves to summarize the functional impact of missense mutations in each gene as determined by Mutation Assessor[1].

Summary

High Functional Impact Missense Mutations: 1112
Medium Functional Impact Missense Mutations: 6924
Low Functional Impact Missense Mutations: 6870
Neutral Functional Impact Mutations: 4568

Results

Functional Impact by Gene

Table 1. Get Full Table A gene-level breakdown of missense mutation functional impact, ordered by MutSig rank. Includes missense mutation counts broken down by level of functional impact (high, medium, low, neutral), median functional impact score and level, and most common level(s) of functional impact (mode) per gene.

MutSig Rank	Gene	High Functional Impact Count	Medium Functional Impact Count	Low Functional Impact Count	Neutral Functional Impact Count	Median Functional Impact Score	Median Functional Impact Level	Mode Functional Impact Level
1	APC	0	6	30	12	1.0800	low	low
2	CRIPAK	0	0	0	6	0.0000	neutral	neutral
3	TP53	0	73	7	1	3.0750	medium	medium
4	NRAS	5	6	0	0	3.3250	medium	medium
5	FMN2	0	1	2	1	1.5025	low	low
8	ARID1A	0	1	3	1	1.0400	low	low
9	CDH1	2	5	2	1	2.5950	medium	medium
11	HAUS6	0	1	0	0	1.9350	medium	medium
12	CTNNB1	1	9	7	4	1.8450	low	medium
13	GABRP	0	0	0	2	0.0000	neutral	neutral

Methods & Data

Input

READ-TP.maf.annotated
sig_genes.txt
Mutation Assessor Scores Release 2:

hg18
hg19

A lookup is done against the relevant Mutation Assessor Scores table for each missense mutation in a given MAF file, and available functional impact score and level are appended as two new columns to generate READ-TP.maf.annotated. These are summarized in Table 1, sorted by MutSig rank.

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] Boris Reva, Yevgeniy Antipin, and Chris Sander, Predicting the functional impact of protein mutations: application to cancer genomics, Nucl. Acids Res. 39(17):e118 (2011)

Made with Nozzle