annotate joint_snv_mix.xml @ 2:26953f1c8af2 draft default tip

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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="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>