Mercurial > repos > matthias > dada2_dada

annotate dada2_dada.xml @ 11:812ec9c53638 draft

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planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit b59ed2476188176bb5b2973f59899b7d733e7641-dirty
author matthias
date Thu, 29 Aug 2019 09:06:57 -0400
parents 4a770a261b16
children
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1 <tool id="dada2_dada" name="dada2: dada" version="@DADA2_VERSION@+galaxy@WRAPPER_VERSION@" profile="19.09">
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2 <description>Remove sequencing errors</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements"/>
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7 <expand macro="stdio"/>
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8 <expand macro="version_command"/>
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9 <command detect_errors="exit_code"><![CDATA[
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10 #if $batch_cond.batch_select == "no"
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11 mkdir output &&
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12 #end if
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13 Rscript '$dada2_script' \${GALAXY_SLOTS:-1}
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14 ]]></command>
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15 <configfiles>
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16 <configfile name="dada2_script"><![CDATA[
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17 library(ggplot2, quietly=T)
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18 library(dada2, quietly=T)
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20 args <- commandArgs(trailingOnly = TRUE)
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21 nthreads <- as.integer(args[1])
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22
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23 #if $batch_cond.batch_select == "no"
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24 derep <- list()
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25 #for $d in $batch_cond.derep:
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26 derep[["$d.element_identifier"]] <- readRDS('$d')
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27 #end for
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28 #else
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29 derep <- readRDS('$batch_cond.derep')
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30 #end if
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31
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32 err <- readRDS('$err')
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33
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34 #if $batch_cond.batch_select == "yes":
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35 pool <- F
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36 #else
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37 #if $batch_cond.pool == "TRUE"
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38 pool <- T
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39 #else if $batch_cond.pool == "FALSE"
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40 pool <- F
3
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41 #else
0
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42 pool <- 'pseudo'
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43 #end if
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44 #end if
3
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45 dada_result <- dada(derep, err,
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46 ## not needed for end user: errorEstimationFunction = $errfoo, selfConsist = $selfconsist,
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47 pool = pool, multithread = nthreads)
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48
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49 #if $batch_cond.batch_select == "no":
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50 #if len($batch_cond.derep) > 1:
0
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51 for( id in names(dada_result) ){
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52 saveRDS(dada_result[[id]], file=file.path("output" ,paste(id, "dada2_dada", sep=".")))
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53 }
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54 #else
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55 #for $d in $batch_cond.derep:
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56 saveRDS(dada_result, file=file.path("output" ,paste('$d.element_identifier', "dada2_dada", sep=".")))
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57 #end for
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58 #end if
0
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59 #else
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60 saveRDS(dada_result, file='$dada')
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61 #end if
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62
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63 ## for( id in names(dada_result) ){
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64 ## tab <- dada_result[[id]]\$clustering
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65 ## tabnames <- names(tab)
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66 ## tab\$sample <- id
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67 ## tabnames<-c("sample", tabnames)
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68 ## tab <- tab[,tabnames]
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69 ## write.table(tab, file = '$dada', append = T, quote = F, sep = "\t", row.names = F, col.names = F)
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70 ## }
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71 ]]></configfile>
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72 </configfiles>
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73 <inputs>
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74 <conditional name="batch_cond">
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75 <param name="batch_select" type="select" label="Process samples in batches" help="process samples jointly (default) or in independent jobs (see also below)">
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76 <option value="no">no</option>
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77 <option value="yes">yes</option>
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78 </param>
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79 <when value="yes">
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80 <param argument="derep" type="data" format="dada2_derep" label="Dereplicated reads"/>
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81 </when>
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82 <when value="no">
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83 <param argument="derep" type="data" multiple="true" format="dada2_derep" label="Dereplicated reads"/>
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84 <param argument="pool" type="select" label="Pool samples">
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85 <option value="FALSE">process samples individually</option>
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86 <option value="TRUE">pool samples</option>
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87 <option value="pseudo">pseudo pooling between individually processed samples</option>
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88 </param>
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89 </when>
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90 </conditional>
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91 <param argument="err" type="data" format="dada2_errorrates" label="Error rates"/>
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92 <!-- not needed for end user I guess
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93 <expand macro="errorEstimationFunction"/>
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94 <param name="selfconsist" type="boolean" checked="false" truevalue="TRUE" falsevalue="FALSE" label="Alternate between sample inference and error rate estimation until convergence"/>-->
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95 </inputs>
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96 <outputs>
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97 <data name="dada" format="dada2_dada">
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98 <filter>batch_cond['batch_select']=="yes"</filter>
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99 </data>
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100 <collection name="data_collection" type="list">
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101 <discover_datasets pattern="(?P&lt;name&gt;.+)\.dada2_dada" format="dada2_dada" directory="output" />
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102 <filter>batch_cond['batch_select']=="no"</filter>
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103 </collection>
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104 </outputs>
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105 <tests>
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106 <!-- default, non batch -->
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107 <test>
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108 <param name="batch_cond|batch_select" value="no"/>
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109 <param name="batch_cond|derep" value="derepFastq_F3D0_R1.Rdata" ftype="dada2_derep" />
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110 <param name="err" value="learnErrors_F3D0_R1.Rdata" ftype="dada2_errorrates" />
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111 <output_collection name="data_collection" type="list">
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112 <element name="derepFastq_F3D0_R1.Rdata" file="dada_F3D0_R1.Rdata" ftype="dada2_dada"/>
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113 </output_collection>
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114 </test>
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115 <!-- default, batch -->
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116 <test>
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117 <param name="batch_cond|batch_select" value="yes"/>
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118 <param name="batch_cond|derep" value="derepFastq_F3D0_R1.Rdata" ftype="dada2_derep" />
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119 <param name="err" value="learnErrors_F3D0_R1.Rdata" ftype="dada2_errorrates" />
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120 <output name="dada" value="dada_F3D0_R1.Rdata" ftype="dada2_dada" />
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121 </test>
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122 <!-- test for creating input for dada results for reverse, not needed for testing -->
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123 <test>
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124 <param name="batch_cond|batch_select" value="no"/>
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125 <param name="batch_cond|derep" value="derepFastq_F3D0_R2.Rdata" ftype="dada2_derep" />
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126 <param name="err" value="learnErrors_F3D0_R2.Rdata" ftype="dada2_errorrates" />
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127 <output_collection name="data_collection" type="list">
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128 <element name="derepFastq_F3D0_R2.Rdata" file="dada_F3D0_R2.Rdata" ftype="dada2_dada"/>
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129 </output_collection>
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130 </test>
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131 <!-- test non-default options -->
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132 <test>
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133 <param name="batch_cond|batch_select" value="no"/>
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134 <param name="batch_cond|derep" value="derepFastq_F3D0_R1.Rdata" ftype="dada2_derep" />
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135 <param name="batch_cond|pool" value="pseudo"/>
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136 <param name="err" value="learnErrors_F3D0_R1.Rdata" ftype="dada2_errorrates" />
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137 <output_collection name="data_collection" type="list">
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138 <element name="derepFastq_F3D0_R1.Rdata" file="dada_F3D0_R1.Rdata" ftype="dada2_dada"/>
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139 </output_collection>
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140 </test>
2
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141 </tests>
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142 <help><![CDATA[
2
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143 Description
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144 ...........
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145
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146 The dada function takes as input dereplicated amplicon sequencing reads and returns the inferred
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147 composition of the sample (or samples). Put another way, dada removes all sequencing errors to
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148 reveal the members of the sequenced community.
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149
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150 Usage
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151 .....
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152
0
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153 **Input:**
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154
2
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155 - A number of dada2_derep datasets computed with *derepFastq* (given as collection or multiple data sets)
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156 - An dada2_errorrates data set computed with learnErrors
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158 You can decide to compute the data jointly or in batches.
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159
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160 - Jointly (Process "samples in batches"=no): A single Galaxy job is started that processes all derep data sets jointly. You may chose different pooling strategies: if the started dada job processes the samples individually, pooled, or pseudo pooled.
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161 - In batches (Process "samples in batches"=yes): A separate Galaxy job is started for earch derep data set. This is equivalent to joint processing and choosing to process samples individually.
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162
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163 While the single dada job (in case of joint processing) can use multiple cores on one compute node, batched processing distributes the work on a number of jobs (equal to the number of input derep data sets) where each can use multiple cores. Hence, if you intend to or need to process the data sets individually, batched processing is more efficient -- in particular if Galaxy has access to a larger number of compute resources.
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165 A typical use case of individual processing of the samples are large data sets for which the pooled strategy needs to much time or memory.
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166
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167 **Output**: a data set of type dada2_dada (which is a RData file containing the output of dada2's dada function).
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169 The output of this tool can serve as input for *dada2: mergePairs*, *dada2: removeBimeraDinovo*, and "dada2: makeSequenceTable"
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170
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171 Details
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172 .......
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173
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174 Briefly, dada implements a statistical test for the notion that a specific sequence was seen too many times to have been caused by amplicon errors from currently inferred sample sequences. Overly abundant sequences are used as the seeds of new partitions of sequencing reads, and the final set of partitions is taken to represent the denoised composition of the sample. A more detailed explanation of the algorithm is found in the dada2 puplication (see below) and https://doi.org/10.1186/1471-2105-13-283. dada depends on a parametric error model of substitutions. Thus the quality of its sample inference is affected by the accuracy of the estimated error rates. All comparisons between sequences performed by dada depend on pairwise alignments. This step is the most computationally intensive part of the algorithm, and two alignment heuristics have been implemented in dada for speed: A kmer-distance screen and banded Needleman-Wunsch alignmemt.
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175
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176 @HELP_OVERVIEW@
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177 ]]></help>
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178 <expand macro="citations"/>
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179 </tool>