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MutSig2CV

MutSig2CV (Lawrence et al., 2014).

Overview

MutSig2CV analyzes somatic point mutations discovered in DNA sequencing, identifying genes mutated more often than expected by chance given inferred background mutation processes. MutSig2CV consists of three independent statistical tests, described briefly below:

  • Abundance (CV): The most important step for inferring genes' mutational significance is to properly classify whether the gene is highly mutated relative to some background mutation rate (BMR), which varies on a macroscopic level across patients and genes and on a microscopic level across sequence contexts. MutSig accounts for all three of these to renormalize BMR on a per-gene, -patient, and -context level.

  • Clustering (CL): Genes often harbor mutational hotspots, specific sites that are frequently mutated. While abundance calculations bin mutations on the gene level, clustering bins mutations on the local site level, which allows MutSig to differentiate between genes with uniformly distributed mutations and genes with localized hotspots, assigning higher significance to the latter.

  • Conservation (FN): MutSig uses evolutionary conservation as a proxy for determining the functional significance of a mutated site. It assumes that genetic sites highly conserved across vertebrates have greater functional significance than weakly conserved sites. MutSig assigns a higher significance to genes that experience frequent mutations in highly conserved sites.

For detailed descriptions of the algorithms employed in the MutSig2CV suite for each of these tests, please visit https://www.broadinstitute.org/cancer/cga/mutsig

Installing

MutSig is implemented in MATLAB. If you have a MATLAB installation and wish to run MutSig interactively on the MATLAB console, skip to the Running section below. If you do not have MATLAB installed, or do not wish to run interactively, MutSig can be run as a standalone executable. The standalone executable is available for 64 bit Linux systems only, and requires that the MATLAB R2013a runtime be installed. You can download and install the runtime environment from here. Runtime installation instructions can be found here.

Once the runtime is successfully installed, you must add it to your LD_LIBRARY_PATH.

MCRROOT=<path to runtime you specified when installing>
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/bin/glnxa64/
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64/server
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64/native_threads
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/sys/os/glnxa64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/bin/glnxa64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/runtime/glnxa64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/lib

MutSig requires ~3 GB of reference files. Since these files are too large to include in a GitHub repository, they are hosted elsewhere. Please download them from here, and copy the reference directory into this folder.

Running

To run on the MATLAB console, start MATLAB in this directory, and run:

MutSig2CV(<path to mutations>, <path to output directory>, [params file])

To run the standalone application, cd to this directory, and run:

bin/MutSig2CV <path to mutations> <path to output directory> [params file]

MutSig looks for its reference files relative to this directory, so it is essential it is run here.

Each input is decribed below.

Description of inputs

  • Mutations file: Absolute path to the set of mutations to analyze. Format is specified in the following section, Mutation Input Format

  • Output directory: Absolute path to the directory where MutSig will save its output. Will be created if necessary. NB: Any previous MutSig results in this directory will be overwritten! A description of the output files and formats are in the following section, Outputs.

  • Params file: MutSig can take an optional parameters file, which allows for configuration of algorithm/run parameters. User-configurable options are in the following section, MutSig Configuration.

Mutation Input Format

As input, MutSig takes a tab-delimited file with each line annotating a single mutation in a single patient. Columns can be in any order, with names and formats as follows. To provide maximal input flexibility, MutSig accepts synonyms for each column name. Column names are case sensitive.

  • chr: Chromosome of the mutation. MutSig only analyzes mutations on autosomal or sex chromosomes, and does not consider the mitochondrial chromosome or unplaced/alternate contigs.

    • Range: (chr)?[1..24XY]
    • Synonyms: Chromosome
  • pos hg19 position of the mutation, 1-indexed.

    • Regex: [0-9]+
    • Synonyms: Position, start, Start_position
  • gene: HUGO symbol for the gene containing this mutation, or "Unknown" for IGR mutations.

    • Regex: [A-Za-z0-9]+
    • Synonyms: Hugo_Symbol, Gene_name
  • patient: Unique identifier for the patient.

    • Regex: [A-Za-z0-9]+
    • Synonyms: Tumor_Sample_Barcode, Patient_name
  • ref_allele: hg19 reference base(s) for the position. In the case of insertions, must be "-".

    • Regex: (-|[ACGT]+)
    • Synonyms: Reference_Allele
  • newbase: Observed variant allele at the position. In the case of deletions, must be "-".

    • Regex: (-|[ACGT]+)
    • Synonyms: Tumor_Allele, Tum_allele, Alt_allele, Alternate_allele, Tumor_Seq_Allele2
  • type: Effect of mutation (e.g. missense, silent, nonsense, splice, UTR). Full list of effects can be obtained as follows: awk 'NR > 1 { print $2 }' reference/mutation_type_dictionary.v4.txt | sort -u

    • Regex: N/A
    • Synonyms: Variant_Classification
  • classification: Mutation substitution type (e.g. SNP, INS, DEL.)

    • Regex: (Complex_substitution|INS|DEL|[SDTO]NP)
    • Synonyms: Variant_Type

If your input mutation data are missing gene/ref_allele/type/classification fields, we recommend annotating using Oncotator, which will produce a MutSig-ready MAF from a variety of input data. Oncotator is available here: https://www.broadinstitute.org/cancer/cga/oncotator

Outputs

A MutSig run outputs several files:

sig_genes.txt

A tab-delimited file containing all genes considered for analysis, sorted by p-/q-values. Columns are as follows:

  • rank: Position of the gene as sorted ascending by p-/q-value.

  • gene: HUGO symbol of the gene (RefSeq hg19)

  • longname: HUGO description of the gene

  • codelen: ORF length of the gene

  • nnei: Number of neighboring genes in the bagel used to estimate BMR

  • nncd: Number of noncoding mutations

  • nsil: Number of silent (synonymous) mutations in the gene

  • nmis: Number of missense mutations in the gene

  • nstp: Number of nonsense mutations in the gene

  • nspl: Number of splice site mutations in the gene (defined as +/- 2 bases from the donor/acceptor site)

  • nind: Number of insertions or deletions in the gene

  • nnon: Number of nonsilent mutations in the gene (including all indels and splice site mutations, even if the codon change is synonymous in the latter case)

  • npat: Number of patients with mutations in the gene

  • nsite: Number of uniquely mutated sites in the gene (does not multiply count recurrently mutated positions)

  • pCV: Abundance p-value

  • pCL: Clustering p-value

  • pFN: Functional (conservation) p-value

  • p: Overall p-value obtained from Fisher combination of pCV, pCL, and pFN

  • q: FDR-corrected (Benjamini-Hochberg) overall p-value

final_analysis_set.maf

MutSig does not necessarily consider all mutations for analysis; for instance, mutations in genes determined to be poorly covered, mutations determined to belong to duplicate patients, or deep IGR mutations will be discarded. final_analysis_set.maf contains only those mutations actually used for significance analysis. Note that this file preserves the original input MAF's columns but also contains columns used internally by MutSig.

patient_counts_and_rates.txt

A tab-delimited file summarizing each patient's mutation counts, one patient per line. For each patient, fields are as follows:

  • name: Patient name

  • nmut: Total number of mutations in original input

  • n_coding: Total number of coding mutations

  • n_coding_nonsilent: Number of nonsilent coding mutations

  • callscheme_name: Coverage model (e.g. whole genome, exome capture, exomeplus, TCGA coding only), inferred from distribution of patient's mutations

  • cov_idx: Numerical index for this coverage model

  • N_tot: Total number of covered bases for this coverage model

  • nsil_tot: Number of silent mutations

  • nnon_tot: Number of nonsilent mutations

  • n_tot: Number of mutations considered for analysis

  • rate_sil: Silent mutation rate

  • rate_non: Nonsilent mutation rate

  • rate_tot: Overall mutation rate

  • log_rate_tot: -log10 of overall mutation rate

  • n_ind: Number of indels

  • N_ind: Total number of covered coding bases for this coverage model

  • rate_c: Overall coding mutation rate

  • rate_ind: Indel mutation rate

mutcategs.txt, mutcateg_discovery.txt

MutSig accounts for mutation rate heterogeneity across trinucleotide contexts when calculating BMRs. To increase statistical power, it clusters the 96 base substitutions+trinucleotide contexts into k mutually exclusive categories via entropy minimization with default k = 5. mutcategs.txt and mutcateg_discovery.txt contain the definitions of each category.

  • left: Set of upstream bases: [ACGT]+

  • from: Set of original (reference) bases: [ACGT]+

  • change: Base substitution (transition, flip transversion, skew transversion): [tfs]

  • right: Set of downstream base: [ACGT]+

  • n: Number of mutations in this category

  • N: Total number of genomic positions in this category times number of possible substitutions assigned to this category

  • rate: Mutation rate for this category (inferred from binomial MLE for n successes and N trials)

  • ci_low: Lower bound of 95% confidence interval for rate parameter estimation

  • ci_high: Upper bound of 95% confidence interval for rate parameter estimation

  • relrate: N*rate/n

  • autoname: Used internally for parsing category names

  • name: Human-readable name for this category

  • type: Reserved for future use

per_gene.mutation_counts.txt

Tab-delimited file summarizing each gene's mutation counts on a per-patient basis, one gene per line. For each gene, fields are as follows:

  • name: HUGO symbol

  • cov_gidx: Numerical index for each gene

  • longname: HUGO description

  • chr: Chromosome

  • start: Transcript start

  • end: Transcript end

  • len: Coding length

  • gc: GC content

and then one column per patient, displaying the total number of mutations in this gene for that patient.

sample_sig_gene_table.txt

The same as per_gene.mutation_counts.txt, but only for genes with q-value <= 0.1.

results.mat

MATLAB binary version of sig_genes.txt, containing much more detail. It can be loaded into MATLAB by typing

  load('results.mat', 'G')

MutSig Configuration

MutSig's algorithm and run parameters are configured via a two column, tab- delimited text file. Here is a list of all available parameters, possible options, and default values. A sample parameters file (set to defaults) can be found in test/input/params.txt.

  • number_of_categories_to_discover: Integer. Number of SNV categories to discover (see "mutcategs.txt" section above for details.) Default: 5

  • skip_permutations: Boolean. If true, MutSig will not perform clustering/functional significance tests; pCL and pFN will be set to NaN. Default: false

  • maxperm: Integer. Specifies maximum number of permutations to perform for pCL/pFN permutation tests. Default: 1e5

  • remove_duplicate_patients: Boolean. Finds and removes duplicate patients in the cohort by comparing mutation overlap between patients. This should be disabled when high levels of overlap are expected between samples (e.g. primary/met combined cohorts). Default: true

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MutSig2CV from Lawrence et al. 2014

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