diff joint_snv_mix.xml @ 0:a1034918ab9b draft

Uploaded

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+++ b/joint_snv_mix.xml	Thu Jun 20 00:03:08 2013 -0400
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+<tool id="joint_snv_mix" name="Joint SNV Mix" version="0.7.5">
+  <description>classify germline and somatic mutations</description>
+  <requirements>
+  	<requirement type="package" version="2.7">python</requirement>
+  	<requirement type="package" version="0.19.1">cython</requirement>
+  	<requirement type="package" version="0.5">pysam</requirement>
+  	<requirement type="package" version="0.1.18">samtools</requirement>
+  	<requirement type="package" version="0.7.5">jointsnvmix</requirement>
+  </requirements>
+  <command interpreter="perl">
+  
+  	joint_snv_mix.pl
+  	
+  	"ACTION::${option.option}" 
+  	
+  	"REFGENOME::$refFile.fields.path" 
+  	"BAMNORMAL::$normal_file"
+  	"BAMTUMOR::$tumor_file"
+  	
+  	
+  	#if str($option.option) == "classify":
+  		#if ($option.parameters):
+		  	"OPTION::--parameters_file $option.parameters"  		
+	  	#end if
+  		"OPTION::--out_file $output"
+  		"OPTION::--somatic_threshold $option.somatic_threshold"
+  		                              
+  	#end if
+  	 
+  	#if str($option.option) == "train":
+  		#if ($option.priors):
+  			"OPTION::--priors_file $option.priors" 
+  		#end if
+  		"OUTPUT::$output" 
+  		"OPTION::--convergence_threshold $option.convergence_threshold"
+  		"OPTION::--max_iters $option.max_iters"
+  		
+  	#end if
+  	#if ($positions_file):
+		"OPTION::--positions_file $positions_file" 
+	#end if
+	
+	"OPTION::--min_base_qual $min_base_quality"
+	"OPTION::--min_map_qual $min_map_quality"
+	"OPTION::--model $model"
+	#if ($chromosome):
+		"OPTION::--chromosome $chromosome" 
+	#end if
+	
+	
+  	
+  </command>
+  <inputs>
+  	<param name="refFile" type="select" label="Select a reference genome" optional="false">
+		<options from_data_table="all_fasta">
+			<filter type="sort_by" column="2" />
+			<validator type="no_options" message="No indexes are available" />
+		</options>
+	</param>
+	<param name="normal_file" type="data" format="bam" label="Normal Sample " help="Bam" />
+  	<param name="tumor_file" type="data" format="bam" label="Tumor Sample" help="Bam" />	
+	<param name="model" type="select" label="Model" help="" optional="true">
+			<option value="binomial">binomial</option>
+			<option value="snvmix2" selected="true">snvmix2</option>
+			<option value="beta_binomial">beta binomial</option>
+	</param>
+	<param name="positions_file" type="data" format="txt" label="Positions file" help="Filter positions" optional="true"/>	
+	<param name="min_map_quality" type="text" label="Min map quality" help="Filter reads" value="0"/>	
+  	<param name="min_base_quality" type="text" label="Min base quality" help="Filter reads" value="0"/>
+  	<param name="chromosome" type="text" label="Chromosome" help="a chromosome to analyse, leave blank for all"/>
+
+
+  	<conditional name="option">
+		<param name="option" type="select" label="Action" help="" optional="true">
+			<option value="train" selected="true">Train</option>
+			<option value="classify">Classify</option>
+		</param>
+	
+		<when value="train">
+			
+			<param name="priors" type="data" format="txt" label="Prior Probabilities" optional="true"/>
+			<param name="initial_parameters" type="data" format="txt" label="Initial Parameters" optional="true"/>
+			<param name="convergence_threshold" type="text" label="Convergence Threshold"  value="1e-6"/>
+			<param name="max_iters" type="text" label="Max number of training iterations"  value="1000"/>
+			
+		</when>
+		<when value="classify">
+			
+			<param name="parameters" type="data" format="txt" label="Classify Parameters" help="" optional="true" />
+			<param name="somatic_threshold" type="text" label="Somatic Threshold" help="filter by probability" value="0.0"/>
+		</when>
+	
+	</conditional>
+	
+	
+  </inputs>
+  <outputs>
+	<data type="data" format="txt" name="output" label="${tool.name} result on ${on_string}"/>
+  </outputs>
+  	
+  <help> 
+
+.. class:: infomark
+
+**What it does**
+
+::
+
+  JointSNVMix implements a probabilistic graphical model to analyse sequence data 
+  from tumour/normal pairs. The model draws statistical strength by analysing both 
+  genome jointly to more accurately classify germline and somatic mutations. 
+
+
+  Train
+
+  The SnvMix family of models are complete generative models of the data. 
+  As such the model parameters can be learned using the Expectation Maximisation 
+  (EM) algorithm. The train command allows this to be done.
+
+  All methods require that a file with the parameters for the prior densities, 
+  and an initial set of parameters be passed in. Templates for these files can 
+  be found in the config/ directory which ships with the package. If you are 
+  unsure about setting the   priors or parameter values these files should suffice.
+
+  The train command will produce a parameters file suitable for use with the 
+  classification command. Training is highly recommended to achieve optimal 
+  performance when using SnvMix based model.
+
+  To reduce memory consumption all subcommands of train take an optional --skip-size flag. 
+  This is the number of positions to skip over before sampling a position for the training set. 
+  Smaller values will lead to larger training sets which will require more memory, 
+  but should yield better parameter estimates.
+
+  All subcommands of train also take optional parameters for minimum depth a 
+  position has in the tumour and normal to be used for training. Higher depth 
+  sites should give more robust estimates of the parameters. The default values 
+  of these are likely fine. 
+  
+  
+  Classify
+  
+  The classify command is used for analysing tumour/normal paired data and 
+  computing the posterior probability for each of the nine joint genotypes for 
+  a pair of diploid genomes.
+
+  
+  
+**Models**
+  
+::
+
+  There are currently three models supported by both the train and classify commands. 
+  All models use the JointSNVMix mixture model which jointly analyses the normal and tumour genomes.
+  By default snvmix2 is used but other models can be specified.
+  
+  binomial
+  
+  Uses binomial densities in the mixture model this was previously referred to as the JointSnvMix1 mode. 
+  
+  snvmix2
+  
+  Uses snvmix2 densities in the mixture as described in the original SNVMix paper previously referred to as JointSnvMix2.   
+  
+  beta_binomial 
+  
+  Uses beta-binomial densities in the mixture model new in version 0.8. The beta-binomial is a robust (in the statistical sense) 
+  alternative to binomial model. It can be beneficial when dealing with over-dispersed data. This is useful in cancer genomes 
+  since allelic frequencies at somatic mutations sites may deviate significantly from those expected under diploid model. 
+  
+  
+**Input**
+
+  Bam files containing normal and tumor reads.
+
+
+**Parameters**
+
+
+  Classify
+  
+  chromosome CHROMOSOME
+                        Chromosome to analyse. If not set all chromosomes will
+                        be analysed.
+  
+  min_base_qual MIN_BASE_QUAL
+                        Remove bases with base quality lower than this.
+                        Default is 0.
+  
+  min_map_qual MIN_MAP_QUAL
+                        Remove bases with mapping quality lower than this.
+                        Default is 0.
+  
+  positions_file POSITIONS_FILE
+                        Path to a file containing a list of positions to
+                        create use for analysis. Should be space separated
+                        chrom pos. Additionally for each chromosome the
+                        positions should be sorted. The same format as
+                        samtools.
+  
+  parameters_file PARAMETERS_FILE
+                        Path to a file with custom parameters values for the
+                        model.
+  
+  somatic_threshold SOMATIC_THRESHOLD
+                        Only sites with P(Somatic) = p_AA_AB + p_AA_BB greater
+                        than equal this value will be printed. Default is 0.
+  
+  
+  Train
+  
+  chromosome CHROMOSOME
+                        Chromosome to analyse. If not set all chromosomes will
+                        be analysed.
+  
+  min_base_qual MIN_BASE_QUAL
+                        Remove bases with base quality lower than this.
+                        Default is 0.
+  
+  min_map_qual MIN_MAP_QUAL
+                        Remove bases with mapping quality lower than this.
+                        Default is 0.
+  
+  positions_file POSITIONS_FILE
+                        Path to a file containing a list of positions to
+                        create use for analysis. Should be space separated
+                        chrom pos. Additionally for each chromosome the
+                        positions should be sorted. The same format as
+                        samtools.
+  
+  priors_file PRIORS_FILE
+                        Path to a file with priors for the model parameters.
+  
+  initial_parameters_file INITIAL_PARAMETERS_FILE
+                        Path to a file with initial parameter values for the
+                        model.
+  
+  min_normal_depth MIN_NORMAL_DEPTH
+                        Minimum depth of coverage in normal sample for a site
+                        to be eligible for use in training set. Default 10
+  
+  min_tumour_depth MIN_TUMOUR_DEPTH
+                        Minimum depth of coverage in tumour sample for a site
+                        to be eligible for use in training set. Default 10
+  
+  max_normal_depth MAX_NORMAL_DEPTH
+                        Maximum depth of coverage in normal sample for a site
+                        to be eligible for use in training set. Default 100
+  
+  max_tumour_depth MAX_TUMOUR_DEPTH
+                        Maximum depth of coverage in tumour sample for a site
+                        to be eligible for use in training set. Default 100
+  
+  max_iters MAX_ITERS
+                        Maximum number of iterations to used for training
+                        model. Default 1000
+  
+  skip_size SKIP_SIZE
+                        When subsampling will skip over this number of
+                        position before adding a site to the subsample. Larger
+                        values lead to smaller subsample data sets with faster
+                        training and less memory. Smaller values should lead
+                        to better parameter estimates. Default 1.
+  
+  convergence_threshold CONVERGENCE_THRESHOLD
+                        Convergence threshold for EM training. Once the change
+                        in objective function is below this value training
+                        will end. Default 1e-6
+
+  
+  
+
+  </help>
+</tool>
+
+
+
+