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#Fri, 22 Jun 2018 09:09:40 -0700
# MutPanning
JVMLevel=
LSID=urn\:lsid\:broad.mit.edu\:cancer.software.genepattern.module.analysis\:00380\:2
author=Ted Liefeld, UCSD School of Medicine.
categories=Mutational Significance Analysis
commandLine=/mutpanning/run-mutpanning-module.sh <job.memory> /mutpanning_temp <mutation.file> <sample.annotation.file> /mutpanning_hg19/Hg19/ <min.samples.per.cluster> <min.mutations.per.cluster> <min.samples.Bayesian.model> <min.mutations.Bayesian.model>
cpuType=any
job.memory=10G
description=MutPanning identifies driver genes based on their abundance of nonsynonymous mutations and their increased number of mutations in unusual nucleotide contexts that deviate from the background mutational process.
fileFormat=maf
language=any
minGenePatternVersion=3.9.11
name=MutPanning
os=any
job.docker.image=genepattern/docker-mutpanning:v0.10
p1_MODE=IN
p1_TYPE=FILE
p1_default_value=
p1_description=The <b>mutation file</b> should be a tab-delimited standard *.maf format and contain the following columns: <b>Hugo_Symbol</b> (gene name), <b>Chromosome</b>, <b>Start_Position</b> (according to Hg19), <b>End_Position</b> (according to Hg19), <b>Strand</b> (1 or -1), <b>Variant_Classification</b> (e.g. Missense_Mutation, Nonsense_Mutation, In_Frame_Del, Silent, etc.), <b>Variant_Type</b> (e.g. SNP, DEL, INS, etc.), <b>Reference_Allele</b> (reference nucleotide in Hg19), <b>Tumor_Seq_Allele1</b> (allele A in tumor), <b>Tumor_Seq_Allele2</b> (allele B in tumor), <b>Tumor_Sample_Barcode</b>.<br/>Names in column <b>Tumor_Sample_Barcode</b> should exactly match the names in the sample file (case-sensitive). If you are unsure about the file format, you can download an example <a href=\"http://storage.googleapis.com/mutpanning_hg19/MutationsSkin.maf\">here</a>. Make sure that <b>Variant_Classification</b> and <b>Variant_Type</b> are annotated by exactly the same terms/names as in the exemplary mutation file (case-sensitive, hyphenation, underscores, etc.)
p1_fileFormat=maf
p1_flag=
p1_name=mutation.file
p1_numValues=1..1
p1_optional=
p1_prefix=
p1_prefix_when_specified=
p1_type=java.io.File
p1_value=
p2_MODE=IN
p2_TYPE=FILE
p2_default_value=
p2_description=The <b>sample annotation file</b> should be a tab-delimited *.txt file that contains at least two columns labeled <b>Sample</b> and <b>Cohort</b>. The Sample column contains unique sample name for each sample in the maf file. No duplicates allowed, all samples in the mutation file must be listed in the sample file. The cohort column contains the subcohort (e.g. cancer type) to which the sample belongs (case-sensitive). If you are unsure about the file format, you can download an exemplary sample file <a href=\"http://storage.googleapis.com/mutpanning_hg19/SamplesSkin.txt\">here</a>
p2_fileFormat=txt
p2_flag=
p2_name=sample.annotation.file
p2_numValues=1..1
p2_optional=
p2_prefix=
p2_prefix_when_specified=
p2_type=java.io.File
p2_value=
p3_MODE=
p3_TYPE=TEXT
p3_default_value=3
p3_description=Minimum number of samples needed per nucleotide context cluster. Unless you are familiar with the MutPanning algorithm, we recommend running MutPanning with standard parameters (default value 3).
p3_fileFormat=
p3_flag=
p3_name=min.samples.per.cluster
p3_numValues=1..1
p3_optional=
p3_prefix=
p3_prefix_when_specified=
p3_type=java.lang.String
p3_value=
p4_MODE=
p4_TYPE=TEXT
p4_default_value=1000
p4_description=Minimum number of mutations needed per nucleotide context cluster. Unless you are familiar with the MutPanning algorithm, we recommend running MutPanning with standard parameters (default value 1000).
p4_fileFormat=
p4_flag=
p4_name=min.mutations.per.cluster
p4_numValues=1..1
p4_optional=
p4_prefix=
p4_prefix_when_specified=
p4_type=java.lang.String
p4_value=
p5_MODE=
p5_TYPE=TEXT
p5_default_value=100
p5_description=Minimum number of samples needed to calibrate the Bayesian background model. Unless you are familiar with the MutPanning algorithm, we recommend running MutPanning with standard parameters (default value 100).
p5_fileFormat=
p5_flag=
p5_name=min.samples.Bayesian.model
p5_numValues=1..1
p5_optional=
p5_prefix=
p5_prefix_when_specified=
p5_type=java.lang.String
p5_value=
p6_MODE=
p6_TYPE=TEXT
p6_default_value=5000
p6_description=Minimum number of mutations needed to calibrate the Bayesian background model. Unless you are familiar with the MutPanning algorithm, we recommend running MutPanning with standard parameters (default value 5000).
p6_fileFormat=
p6_flag=
p6_name=min.mutations.Bayesian.model
p6_numValues=1..1
p6_optional=
p6_prefix=
p6_prefix_when_specified=
p6_type=java.lang.String
p6_value=
privacy=public
publicationDate=06/22/2018 09\:09
quality=production
requiredPatchLSIDs=
requiredPatchURLs=
taskDoc=doc.html
taskType=Mutational Significance Analysis
userid=ted
version=Initial Release