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1 <tool id="blockclust" name="BlockClust" version="1.0.0">
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2
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2 <description>efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles</description>
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0
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3 <requirements>
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4 <requirement type="package" version="1.0">blockclust</requirement>
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5 <requirement type="package" version="1.1">eden</requirement>
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6 <requirement type="package" version="3.0.1">R_3_0_1</requirement>
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7 <requirement type="package" version="0.1.19">samtools</requirement>
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8 <requirement type="package" version="12.135">mcl</requirement>
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9 <requirement type="package" version="1.0">blockclust_rlibs</requirement>
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10 </requirements>
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11 <version_command>echo '1.0'</version_command>
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12 <command>
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13 #if str($tool_mode.operation) == "pre":
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14 BlockClustPipeLine.pl -m PRE -bam $tool_mode.reads_bam -tbed $tags_bed
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15 #elif str($tool_mode.operation) == "clust":
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1
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16 #set $outputdir = $clusters.files_path
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0
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17 #set $accept_bed=list()
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18 #set $reject_bed=list()
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19 ## prepare annotations
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20 #if str($tool_mode.reference) == "hg19":
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21 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/hg19/hg19.accept.bed")
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22 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/hg19/hg19.reject.bed")
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23 #elif str($tool_mode.reference) == "mm10":
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24 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/mm10/mm10.accept.bed")
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25 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/mm10/mm10.reject.bed")
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26 #elif str($tool_mode.reference) == "dm3":
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27 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/dm3/dm3.accept.bed")
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28 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/dm3/dm3.reject.bed")
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29 #elif str($tool_mode.reference) == "rheMac3":
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30 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/rheMac3/rheMac3.accept.bed")
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31 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/rheMac3/rheMac3.reject.bed")
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32 #elif str($tool_mode.reference) == "panTro4":
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33 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/panTro3/panTro4.accept.bed")
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34 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/panTro3/panTro4.reject.bed")
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35 #elif str($tool_mode.reference) == "xenTro3":
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36 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/xenTro3/xenTro3.accept.bed")
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37 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/xenTro3/xenTro3.reject.bed")
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38 #elif str($tool_mode.reference) == "celWS235":
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39 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/celWS235/celWS235.accept.bed")
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40 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/celWS235/celWS235.reject.bed")
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41 #elif str($tool_mode.reference) == "tair10":
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42 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/tair10/tair10.accept.bed")
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43 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/tair10/tair10.reject.bed")
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44 #end if
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45 BlockClustPipeLine.pl -m TEST -f SEQUENCE -c \$BLOCKCLUST_DATA_PATH/blockclust.config
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46 -t $tool_mode.input_bbo
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47 -a #echo ''.join( $accept_bed )
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48 -r #echo ''.join( $reject_bed )
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49 -o $outputdir
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50 #if str($tool_mode.pred.enable_pred) == "yes":
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51 -p
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52 -pm $tool_mode.pred.pred_mode
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53 -md \$BLOCKCLUST_DATA_PATH/models
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54 -x 3;
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55 #if str($tool_mode.pred.pred_mode) == "nearest_neighbour":
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56 cp #echo os.path.join($outputdir,'nearest_neighbour_predictions.txt')# $nearest_neighbour_pred_bed;
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57 #elif str($tool_mode.pred.pred_mode) == "model_based":
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58 cp #echo os.path.join($outputdir,'model_based_predictions.txt')# $model_based_pred_bed;
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59 #end if
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60 #else:
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61 ;
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62 #end if
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63
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64 cp #echo os.path.join($outputdir, 'mcl_clusters','all_clusters.bed')# $clusters;
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65 cp #echo os.path.join($outputdir, 'hclust_tree.pdf')# $hclust_plot;
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66 cp #echo os.path.join($outputdir, 'discretized.gspan.tab')# $sim_tab_out
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67 #elif str($tool_mode.operation) == "post":
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68 BlockClustPipeLine.pl -m POST -cbed $tool_mode.clusters_bed -cm $tool_mode.cmsearch_out -tab $tool_mode.sim_tab_in -rfam \$BLOCKCLUST_DATA_PATH/rfam_map.txt -o ./;
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0
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69 #end if
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70 </command>
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71 <inputs>
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72 <conditional name="tool_mode">
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73 <param name="operation" type="select" label="Select mode of operation">
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74 <option value="pre">Pre-processing </option>
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75 <option value="clust">Clustering and classification</option>
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76 <option value="post">Post-processing</option>
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77 </param>
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78 <when value="pre">
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79 <param name="reads_bam" type="data" format="bam" label="BAM file containing alignments" />
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80 </when>
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81 <when value="clust">
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82 <param name="input_bbo" type="data" format="tabular" label="Input blockgroups file" />
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83 <param name="reference" type="select" label="Select reference genome">
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84 <option value="hg19">Human (hg19)</option>
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85 <option value="mm10">Mouse (mm10)</option>
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86 <option value="dm3">Fly (dm3)</option>
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87 <option value="rheMac3">Monkey (rheMac3)</option>
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88 <option value="panTro4">Chimp (panTro4)</option>
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89 <option value="xenTro3">Frog (xenTro3)</option>
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90 <option value="celWS235">C. elegans (celWS235)</option>
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91 <option value="tair10">Arabidopsis thaliana (tair10)</option>
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92 </param>
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93 <conditional name="pred">
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94 <param name="enable_pred" type="select" label="Would you like to perform classification?">
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95 <option value="no">No</option>
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96 <option value="yes">Yes</option>
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97 </param>
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98 <when value="yes">
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99 <param name="pred_mode" type="select" label="Mode of classification">
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100 <option value="model_based">Model based</option>
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101 <option value="nearest_neighbour">Nearest neighbour</option>
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102 </param>
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103 </when>
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104 </conditional>
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105 </when>
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106 <when value="post">
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107 <param name="cmsearch_out" type="data" format="tabular" label="Output of cmsearch tool" />
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108 <param name="clusters_bed" type="data" format="bed" label="BED file containing clusters (output of BlockClust)" />
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109 <param name="sim_tab_in" type="data" format="tabular" label="Pairwise similarities file" />
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110 </when>
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111 </conditional>
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112 </inputs>
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113
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114 <outputs>
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115 <data format="bed" name="tags_bed" label="BlockClust: BAM to BED on ${on_string}">
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116 <filter> tool_mode["operation"]=="pre"</filter>
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117 </data>
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118 <data format="pdf" name="hclust_plot" label="BlockClust: Hierarchical clustering plot on ${on_string}" >
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119 <filter> tool_mode["operation"]=="clust"</filter>
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120 </data>
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2
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121 <data format="bed" name="clusters" label="BlockClust: BED of predicted clusters on ${on_string}">
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122 <filter> tool_mode["operation"]=="clust"</filter>
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123 </data>
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2
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124 <data format="bed" name="model_based_pred_bed" label="BlockClust: Model based predictions BED on ${on_string}">
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0
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125 <filter>
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126 ((
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127 tool_mode["operation"] == 'clust' and
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128 tool_mode["pred"]["enable_pred"] == "yes" and
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129 tool_mode["pred"]["pred_mode"] == "model_based"
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130 ))
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131 </filter>
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132 </data>
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133 <data format="bed" name="nearest_neighbour_pred_bed" label="BlockClust: Nearest neighbor predictions BED on ${on_string}">
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0
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134 <filter>
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135 ((
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136 tool_mode["operation"] == 'clust' and
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137 tool_mode["pred"]["enable_pred"] == "yes" and
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138 tool_mode["pred"]["pred_mode"] == "nearest_neighbour"
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139 ))
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140 </filter>
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141 </data>
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142 <data format="tabular" name="sim_tab_out" label="BlockClust: Pairwise similarities on ${on_string}">
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143 <filter> tool_mode["operation"]=="clust"</filter>
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144 </data>
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1
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145 <data format="pdf" name="cluster_dist" from_work_dir="cluster_distribution.pdf" label="BlockClust: Cluster distribution on ${on_string}" >
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146 <filter> tool_mode["operation"]=="post"</filter>
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147 </data>
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148 <data format="pdf" name="cluster_hclust" from_work_dir="hclust_tree_clusters.pdf" label="BlockClust: Hierarchical clustering plot of cluster centroids on ${on_string}" >
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149 <filter> tool_mode["operation"]=="post"</filter>
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150 </data>
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151 </outputs>
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152 <help>
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153
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154 .. class:: infomark
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155
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156 **What it does**
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157
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158 BlockClust is an efficient approach to detect transcripts with similar
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159 processing patterns. We propose a novel way to encode expression profiles
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160 in compact discrete structures, which can then be processed using
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161 fast graph-kernel techniques. BlockClust allows both clustering and
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162 classification of small non-coding RNAs.
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163
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164 BlockClust runs in three operating modes:
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165
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2
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166 1) Pre-processing - converts given mapped reads (BAM) into BED file of tags
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167
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168 2) Clustering and classification - of given input blockgroups (output of blockbuster tool) as explained in the original paper.
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169
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170 3) Post-processing - plots for overview of predicted clusters.
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171
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172 For a thorough analysis of your data, we suggest you to use complete blockclust workflow, which contains all three modes of operation.
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173
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174 **Inputs**
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175
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176 BlockClust input files are dependent on the mode of operation:
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177
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178 1. Pre-processing mode:
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179 * Binary Sequence Alignment Map (BAM) file
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180
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181 2. Clustering and classification:
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182 * A blockgroups file generated by blockbuster tool
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183 * Select reference genome
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184
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185 3. Post-processing:
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186 * Output of cmsearch, searched clusters generated by BlockClust against Rfam
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187 * BED file containing clusters generated by BlockClust
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188 * Pairwise similarities of blockgroups generated by BlockClust
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189
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190 **Outputs**
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191
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192 1. Pre-processing mode:
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193 * BED file of tags with expressions
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194
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195 2. Clustering and classification:
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196 * Hierarchical clustering plot of all input blockgroups by their similarity
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197 * Pairwise similarities of all input blockgroups
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198 * BED file containing predicted clusters
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199 * BED file containing prediction of blockgroups by pre-compiled SVM binary classification model.
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200
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201 3. Post-processing:
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202 * Plot of distribution of ncRNA families per predicted cluster (overview of cluster precissions). The annotation of ncRNA families are retrieved by searching cluster instances against Rfam database.
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203 * Hierarchical clustering made out of centroids of each BlockClust predicted cluster
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204
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205 ------
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206
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207 **References**
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208
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209 Pavankumar Videm, Dominic Rose, Fabrizio Costa, and Rolf Backofen. "BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles." Bioinformatics 30, no. 12 (2014): i274-i282.
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210
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211
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212 </help>
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213 </tool>
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