Mercurial > repos > bgruening > deeptools_compute_gc_bias

annotate computeGCBias.xml @ 13:7cdfa7e071d7 draft

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planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 13910e1a5ebcfc740c1bc5e38fc676592ef44f11
author bgruening
date Mon, 15 Feb 2016 10:05:37 -0500
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children 80ffcbf079b7
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1 <tool id="deeptools_compute_gc_bias" name="computeGCBias" version="@WRAPPER_VERSION@.0">
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2 <description>Determine the GC bias of your sequenced reads</description>
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3 <macros>
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4 <token name="@BINARY@">computeGCBias</token>
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5 <import>deepTools_macros.xml</import>
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6 </macros>
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7 <expand macro="requirements" />
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8 <command>
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9 <![CDATA[
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10 ln -s "$bamInput" "local_bamInput.bam" &&
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11 ln -s "$bamInput.metadata.bam_index" local_bamInput.bam.bai &&
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13 @BINARY@
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14 @THREADS@
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15 --bamfile local_bamInput.bam
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16 --GCbiasFrequenciesFile $outFileName
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17 --fragmentLength $fragmentLength
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18
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19 @reference_genome_source@
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20
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21 #if $effectiveGenomeSize.effectiveGenomeSize_opt == "specific":
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22 --effectiveGenomeSize $effectiveGenomeSize.effectiveGenomeSize
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23 #else:
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24 --effectiveGenomeSize $effectiveGenomeSize.effectiveGenomeSize_opt
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25 #end if
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27 #if str($region).strip() != '':
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28 --region '$region'
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29 #end if
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30
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31 #if $advancedOpt.showAdvancedOpt == "yes":
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32 --sampleSize '$advancedOpt.sampleSize'
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33 --regionSize '$advancedOpt.regionSize'
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34
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35 #if $advancedOpt.filterOut:
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36 --filterOut $advancedOpt.filterOut
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37 #end if
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39 #if $advancedOpt.extraSampling:
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40 --extraSampling $advancedOpt.extraSampling
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41 #end if
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42 #end if
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43
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44 #if str($image_format) != 'none':
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45 --biasPlot $outImageName
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46 --plotFileFormat $image_format
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47 #end if
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48 ]]>
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49 </command>
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50 <inputs>
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51 <param name="bamInput" format="bam" type="data" label="BAM file"
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52 help="The BAM file must be sorted."/>
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53
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54 <expand macro="reference_genome_source" />
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55 <expand macro="effectiveGenomeSize" />
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56 <expand macro="fragmentLength" />
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57 <expand macro="region_limit_operation" />
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58
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59 <conditional name="advancedOpt">
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60 <param name="showAdvancedOpt" type="select" label="Show advanced options" >
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61 <option value="no" selected="true">no</option>
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62 <option value="yes">yes</option>
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63 </param>
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64 <when value="no" />
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65 <when value="yes">
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66 <param name="sampleSize" type="integer" value="50000000" min="1"
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67 label="Number of sampling points to consider" help="(--sampleSize)" />
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68 <param name="regionSize" type="integer" value="300" min="1"
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69 label="Region size"
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70 help ="To plot the reads per GC over a region, the size of the region is
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71 required (see below for more details about the method). By default, the bin size
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72 is set to 300 bases, which is close to the standard fragment size of many sequencing
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73 applications. However, if the depth of sequencing is low, a larger bin size will
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74 be required, otherwise many bins will not overlap with any read. (--regionSize)"/>
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75 <param name="filterOut" type="data" format="bed" optional="true"
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76 label="BED file containing genomic regions to be excluded from the estimation of the correction"
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77 help="Such regions usually contain repetitive regions and peaks that, if included, would
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78 bias the correction. It is recommended to filter out known repetitive regions if multi-reads
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79 (reads that map to more than one genomic position) were excluded. In the case of ChIP-seq data,
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80 it is recommended to first use a peak caller to identify and filter out the identified peaks. (--filterOut)" />
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81 <param name="extraSampling" type="data" format="bed" optional="true"
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82 label="BED file containing genomic regions for which extra sampling is required because they are underrepresented in the genome"
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83 help="(--extraSampling)" />
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84 </when>
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85 </conditional>
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86 <param name="image_format" type="select"
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87 label="GC bias plot"
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88 help="If given, a diagnostic image summarizing the GC bias found on the sample will be created. (--plotFileFormat)">
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89 <option value="none">No image</option>
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90 <option value="png" selected="true">Image in png format</option>
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91 <option value="pdf">Image in pdf format</option>
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92 <option value="svg">Image in svg format</option>
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93 <option value="eps">Image in eps format</option>
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94 </param>
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95 </inputs>
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96 <outputs>
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97 <data name="outFileName" format="tabular" />
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98 <data name="outImageName" format="png" label="${tool.name} GC-bias Plot">
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99 <filter>
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100 ((
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101 image_format != 'none'
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102 ))
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103 </filter>
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104 <change_format>
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105 <when input="image_format" value="pdf" format="pdf" />
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106 <when input="image_format" value="svg" format="svg" />
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107 <when input="image_format" value="eps" format="eps" />
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108 </change_format>
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109 </data>
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110 </outputs>
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111 <tests>
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112 <test>
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113 <param name="bamInput" value="paired_chr2L.bam" ftype="bam" />
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114 <param name="image_format" value="png" />
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115 <param name="showAdvancedOpt" value="yes" />
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116 <param name="regionSize" value="1" />
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117 <param name="ref_source" value="history" />
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118 <param name="input1" value="sequence.2bit" />
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119 <param name="sampleSize" value="10" />
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120 <param name="effectiveGenomeSize_opt" value="specific" />
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121 <param name="effectiveGenomeSize" value="23011544" />
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122 <param name="region" value="chr2L" />
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123 <param name="image_format" value="none" />
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124 <output name="outFileName" file="computeGCBias_result1.tabular" ftype="tabular" />
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125 </test>
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126 </tests>
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127 <help>
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128 <![CDATA[
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129 What it does
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130 ------------------
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131
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132 This tool computes the GC bias using the method proposed in Benjamini and Speed (2012) Nucleic Acids Res. (see below for further details).
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133 The output is used to plot the results and can also be used later on to correct the bias with the tool ``correctGCbias``.
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134 There are two plots produced by the tool: a boxplot showing the absolute read numbers per GC-content bin and an x-y plot depicting the ratio of observed/expected reads per GC-content bin.
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135
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136 Output files
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137 --------------
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138
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139 - Diagnostic plots:
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140 - box plot of absolute read numbers per GC-content bin
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141 - x-y plot of observed/expected read ratios per GC-content bin
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142
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143 - Tabular file: to be used for GC correction with ``correctGCbias``
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144
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145 .. image:: $PATH_TO_IMAGES/computeGCBias_output.png
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146 :width: 600
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147 :height: 455
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148
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149 ---------------------------------------------
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150
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151 Background
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152 -------------
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153
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154 ``computeGCBias`` is based on a paper by `Benjamini and Speed <http://nar.oxfordjournals.org/content/40/10/e72>`_.
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155 The basic assumption of the GC bias diagnosis is that an ideal sample should show a uniform distribution of sequenced reads across the genome, i.e. all regions of the genome should have similar numbers of reads, regardless of their base-pair composition.
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156 In reality, the DNA polymerases used for PCR-based amplifications during the library preparation of the sequencing protocols prefer GC-rich regions. This will influence the outcome of the sequencing as there will be more reads for GC-rich regions just because of the DNA polymerase's preference.
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157
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158 ``computeGCbias`` will first calculate the **expected GC profile** by counting the number of DNA fragments of a fixed size per GC fraction where GC fraction is defined as the number of G's or C's in a genome region of a given length.
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159 The result is basically a histogram depicting the frequency of DNA fragments for each type of genome region with a GC fraction between 0 to 100 percent. This will be different for each reference genome, but is independent of the actual sequencing experiment.
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160
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161 The profile of the expected DNA fragment distribution is then compared to the **observed GC profile**, which is generated by counting the number of sequenced reads per GC fraction.
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162
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163 In an ideal experiment, the observed GC profile would, of course, look like the expected profile.
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164 This is indeed the case when applying ``computeGCBias`` to simulated reads.
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165
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166 .. _computeGCBias_example_image:
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167
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168 .. image:: $PATH_TO_IMAGES/GC_bias_simulated_reads_2L.png
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169
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170 As you can see, both plots based on **simulated reads** do not show enrichments or depletions for specific GC content bins, there is an almost flat line around the log2ratio of 0 (= ratio(observed/expected) of 1). The fluctuations on the ends of the x axis are due to the fact that only very, very few regions in the *Drosophila* genome have such extreme GC fractions so that the number of fragments that are picked up in the random sampling can vary.
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171
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172 Now, let's have a look at **real-life data** from genomic DNA sequencing. Panels A and B can be clearly distinguished and the major change that took place between the experiments underlying the plots was that the samples in panel A were prepared with too many PCR cycles and a standard polymerase whereas the samples of panel B were subjected to very few rounds of amplification using a high fidelity DNA polymerase.
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174 .. image:: $PATH_TO_IMAGES/QC_GCplots_input.png
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175 :width: 600
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176 :height: 452
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177
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178 **Note:** The expected GC profile depends on the reference genome as different organisms have very different GC contents. For example, one would expect more fragments with GC fractions between 30% to 60% in mouse samples (average GC content of the mouse genome: 45 %) than for genome fragments from, for example, *Plasmodium falciparum* (average genome GC content *P. falciparum*: 20%).
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179
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180 For more details, for example about when to exclude regions from the read distribution calculation, go `here <http://deeptools.readthedocs.org/en/latest/content/tools/computeGCBias.html#excluding-regions-from-the-read-distribution-calculation>`_
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183 -----
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185 @REFERENCES@
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186 ]]>
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187 </help>
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188 <expand macro="citations" />
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189 </tool>