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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/blockclust commit 1973f3035c10db80883d80847ea254289f5cce2a-dirty
author bgruening
date Thu, 17 Sep 2015 16:49:52 -0400
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1 <tool id="blockclust" name="BlockClust" version="1.0.0">
2 <description>efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles</description>
3 <requirements>
4 <requirement type="package" version="1.0">blockclust</requirement>
5 <requirement type="package" version="1.1">eden</requirement>
6 <requirement type="package" version="3.0.3">R</requirement>
7 <requirement type="package" version="0.1.19">samtools</requirement>
8 <requirement type="package" version="12.135">mcl</requirement>
9 <requirement type="package" version="1.0">blockclust_rlibs</requirement>
10 </requirements>
11 <version_command>echo '1.0'</version_command>
12 <command>
13 <![CDATA[
14 #if str($tool_mode.operation) == "pre":
15 BlockClustPipeLine.pl -m PRE -bam $tool_mode.reads_bam -tbed $tags_bed
16 #elif str($tool_mode.operation) == "clust":
17 #set $outputdir = $clusters.files_path
18 #set $accept_bed=list()
19 #set $reject_bed=list()
20 ## prepare annotations
21 #if str($tool_mode.reference) == "hg19":
22 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/hg19/hg19.accept.bed")
23 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/hg19/hg19.reject.bed")
24 #elif str($tool_mode.reference) == "mm10":
25 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/mm10/mm10.accept.bed")
26 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/mm10/mm10.reject.bed")
27 #elif str($tool_mode.reference) == "dm3":
28 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/dm3/dm3.accept.bed")
29 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/dm3/dm3.reject.bed")
30 #elif str($tool_mode.reference) == "rheMac3":
31 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/rheMac3/rheMac3.accept.bed")
32 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/rheMac3/rheMac3.reject.bed")
33 #elif str($tool_mode.reference) == "panTro4":
34 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/panTro3/panTro4.accept.bed")
35 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/panTro3/panTro4.reject.bed")
36 #elif str($tool_mode.reference) == "xenTro3":
37 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/xenTro3/xenTro3.accept.bed")
38 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/xenTro3/xenTro3.reject.bed")
39 #elif str($tool_mode.reference) == "celWS235":
40 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/celWS235/celWS235.accept.bed")
41 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/celWS235/celWS235.reject.bed")
42 #elif str($tool_mode.reference) == "tair10":
43 $accept_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/tair10/tair10.accept.bed")
44 $reject_bed.append("\$BLOCKCLUST_DATA_PATH/annotations/tair10/tair10.reject.bed")
45 #end if
46 BlockClustPipeLine.pl -m TEST -c \$BLOCKCLUST_DATA_PATH/blockclust.config
47 -t $tool_mode.input_bbo
48 -a #echo ''.join( $accept_bed )
49 -r #echo ''.join( $reject_bed )
50 -o $outputdir
51 #if $tool_mode.nochr:
52 -nochr
53 #end if
54 #if str($tool_mode.pred.enable_pred) == "yes":
55 -p
56 -pm $tool_mode.pred.pred_mode
57 -md \$BLOCKCLUST_DATA_PATH/models;
58 #if str($tool_mode.pred.pred_mode) == "nearest_neighbour":
59 cp #echo os.path.join($outputdir,'nearest_neighbour_predictions.txt')# $nearest_neighbour_pred_bed;
60 #elif str($tool_mode.pred.pred_mode) == "model_based":
61 cp #echo os.path.join($outputdir,'model_based_predictions.txt')# $model_based_pred_bed;
62 #end if
63 #else:
64 ;
65 #end if
66
67 cp #echo os.path.join($outputdir, 'mcl_clusters','all_clusters.bed')# $clusters;
68 cp #echo os.path.join($outputdir, 'hclust_tree.pdf')# $hclust_plot;
69 cp #echo os.path.join($outputdir, 'discretized.gspan.tab')# $sim_tab_out
70 #elif str($tool_mode.operation) == "post":
71 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 ./;
72 #end if
73 ]]>
74 </command>
75 <inputs>
76 <conditional name="tool_mode">
77 <param name="operation" type="select" label="Select mode of operation">
78 <option value="pre">Pre-processing </option>
79 <option value="clust">Clustering and classification</option>
80 <option value="post">Post-processing</option>
81 </param>
82 <when value="pre">
83 <param name="reads_bam" type="data" format="bam" label="BAM file containing alignments" />
84 </when>
85 <when value="clust">
86 <param name="input_bbo" type="data" format="tabular" label="Input blockgroups file" />
87 <param name="reference" type="select" label="Select reference genome">
88 <option value="hg19">Human (hg19)</option>
89 <option value="mm10">Mouse (mm10)</option>
90 <option value="dm3">Fly (dm3)</option>
91 <option value="rheMac3">Monkey (rheMac3)</option>
92 <option value="panTro4">Chimp (panTro4)</option>
93 <option value="xenTro3">Frog (xenTro3)</option>
94 <option value="celWS235">C. elegans (celWS235)</option>
95 <option value="tair10">Arabidopsis thaliana (tair10)</option>
96 </param>
97 <param name="nochr" type="boolean" label="My input files have no 'chr' for chromosome names" checked="False"/>
98 <conditional name="pred">
99 <param name="enable_pred" type="select" label="Would you like to perform classification?">
100 <option value="no">No</option>
101 <option value="yes">Yes</option>
102 </param>
103 <when value="yes">
104 <param name="pred_mode" type="select" label="Mode of classification">
105 <option value="model_based">Model based</option>
106 <option value="nearest_neighbour">Nearest neighbour</option>
107 </param>
108 </when>
109 </conditional>
110 </when>
111 <when value="post">
112 <param name="cmsearch_out" type="data" format="tabular" label="Output of cmsearch tool" />
113 <param name="clusters_bed" type="data" format="bed" label="BED file containing clusters (output of BlockClust)" />
114 <param name="sim_tab_in" type="data" format="tabular" label="Pairwise similarities file" />
115 </when>
116 </conditional>
117 </inputs>
118
119 <outputs>
120 <data format="bed" name="tags_bed" label="BlockClust: BAM to BED on ${on_string}">
121 <filter> tool_mode["operation"]=="pre"</filter>
122 </data>
123 <data format="pdf" name="hclust_plot" label="BlockClust: Hierarchical clustering plot on ${on_string}" >
124 <filter> tool_mode["operation"]=="clust"</filter>
125 </data>
126 <data format="bed" name="clusters" label="BlockClust: BED of predicted clusters on ${on_string}">
127 <filter> tool_mode["operation"]=="clust"</filter>
128 </data>
129 <data format="bed" name="model_based_pred_bed" label="BlockClust: Model based predictions BED on ${on_string}">
130 <filter>
131 ((
132 tool_mode["operation"] == 'clust' and
133 tool_mode["pred"]["enable_pred"] == "yes" and
134 tool_mode["pred"]["pred_mode"] == "model_based"
135 ))
136 </filter>
137 </data>
138 <data format="bed" name="nearest_neighbour_pred_bed" label="BlockClust: Nearest neighbor predictions BED on ${on_string}">
139 <filter>
140 ((
141 tool_mode["operation"] == 'clust' and
142 tool_mode["pred"]["enable_pred"] == "yes" and
143 tool_mode["pred"]["pred_mode"] == "nearest_neighbour"
144 ))
145 </filter>
146 </data>
147 <data format="tabular" name="sim_tab_out" label="BlockClust: Pairwise similarities on ${on_string}">
148 <filter> tool_mode["operation"]=="clust"</filter>
149 </data>
150 <data format="pdf" name="cluster_dist" from_work_dir="cluster_distribution.pdf" label="BlockClust: Cluster distribution on ${on_string}" >
151 <filter> tool_mode["operation"]=="post"</filter>
152 </data>
153 <data format="pdf" name="cluster_hclust" from_work_dir="hclust_tree_clusters.pdf" label="BlockClust: Hierarchical clustering plot of cluster centroids on ${on_string}" >
154 <filter> tool_mode["operation"]=="post"</filter>
155 </data>
156 </outputs>
157 <help>
158 <![CDATA[
159
160 **What it does**
161
162 BlockClust is an efficient approach to detect transcripts with similar
163 processing patterns. BlockClust proposes a novel way to encode expression profiles
164 in compact discrete structures which can then be processed using
165 fast graph-kernel techniques. BlockClust allows both clustering and
166 classification of small non-coding RNAs.
167
168 BlockClust runs in three operating modes:
169
170 1) Pre-processing - converts given mapped reads (BAM) into BED file of tags
171
172 2) Clustering and classification - of given input blockgroups (output of blockbuster tool) as explained in the original paper.
173
174 3) Post-processing - plots for overview of predicted clusters.
175
176 For a thorough analysis of your data, we suggest you to use complete blockclust workflow, which contains all three modes of operation.
177
178 **Input**
179
180 BlockClust input files are dependent on the mode of operation:
181
182 1. Pre-processing mode:
183 * Binary Sequence Alignment Map (BAM) file
184
185 2. Clustering and classification:
186 * A blockgroups file generated by blockbuster tool
187 * Select reference genome
188
189 3. Post-processing:
190 * Output of cmsearch, searched clusters generated by BlockClust against Rfam
191 * BED file containing clusters generated by BlockClust
192 * Pairwise similarities of blockgroups generated by BlockClust
193
194 **Output**
195
196 1. Pre-processing mode:
197 * BED file of tags with expressions
198
199 2. Clustering and classification:
200 * Hierarchical clustering plot of all input blockgroups by their similarity
201 * Pairwise similarities of all input blockgroups
202 * BED file containing predicted clusters
203 * BED file containing prediction of blockgroups by pre-compiled SVM binary classification model.
204
205 3. Post-processing:
206 * 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.
207 * Hierarchical clustering made out of centroids of each BlockClust predicted cluster
208
209
210 ]]>
211 </help>
212 <citations>
213 <citation type="doi">10.1093/bioinformatics/btu270</citation>
214 </citations>
215 </tool>