diff protease.xml @ 0:c7a363d7ab26 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/protease_prediction commit e933135e5dc9aa8c96800fd10b62b256ac3a8523-dirty

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/protease.xml	Sat Mar 12 19:28:41 2016 -0500
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+<tool id="eden_protease_prediction" name="Protease prediction" version="@VERSION@">
+    <description>based on cleavage sites</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="requirements"/>
+    <expand macro="stdio"/>
+    <version_command>echo "@VERSION@"</version_command>
+    <command><![CDATA[
+    python $__tool_directory__/protease.py
+    #if $selected_tasks.selected_task == 'fit':
+        fit
+        -i $selected_tasks.infile_train
+        --negative-ratio $selected_tasks.options.negative_ratio
+        --shuffle-order $selected_tasks.options.shuffle_order
+        -r  $selected_tasks.options.random_state
+    #else:
+        predict
+        -m $selected_tasks.infile_model
+        -i $selected_tasks.infile_data
+    #end if
+]]>
+    </command>
+    <inputs>
+        <expand macro="loadConditional">
+            <section name="options" title="Advanced Options" expanded="False">
+                <param name="negative_ratio" type="integer" optional="true" value="2" label="Negative to positive instance ratio"
+                    help="Relative size ratio for the randomly permuted negative instances w.r.t. the positive instances." />
+                <param name="shuffle_order" type="integer" optional="true" value="2" label="Order of k-mer shuffling"
+                    help="Order of the k-mer for the random shuffling procedure." />
+                <param name="random_state" type="integer" value="1" label="Random seed" />
+            </section>
+        </expand>
+    </inputs>
+    <outputs>
+        <data format="tabular" name="outfile_predict" from_work_dir="out/predictions.txt">
+            <filter>selected_tasks['selected_task'] == 'predict'</filter>
+        </data>
+        <data format="eden_model" name="outfile_fit" from_work_dir="out/model">
+            <filter>selected_tasks['selected_task'] == 'fit'</filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <param name="infile_train" value="CTSL_train.fasta" ftype="fasta"/>
+            <param name="selected_task" value="fit"/>
+            <param name="shuffle_order" value="3"/>
+            <output name="outfile_fit" file="model" ftpye="eden_model" compare="sim_size" delta="100000"/>
+        </test>
+        <test>
+            <param name="infile_model" value="model" ftype="eden_model"/>
+            <param name="infile_data" value="CTSL_test.fasta" ftype="fasta"/>
+            <param name="selected_task" value="predict"/>
+            <output name="outfile_predict" file="predictions.txt" ftpye="tabular"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+**What it does**
+
+This tool can learn the cleavage specificity of a given class of protease. In a second step this can be used to predict proteases given a cleavage site.
+The method assumes that the candidate cleavage point is between the two amino acids adjacent to the central position.
+The method is based on an efficient string kernel implemented in the Explicit Decomposition with Neighbourhood (EDeN) library.
+This approach uses the notion of k-mers with gaps to enumerate all possible substrings of increasing order which are used as features in an efficient linear binary classification estimator.
+
+**Example Input**
+
+::
+
+  >CTSL1
+  SSFVSNWD
+  >CTSL1
+  SSIQATTA
+  >CTSL1
+  SSLAGCQI
+  >CTSL1
+  SSLGGTVV
+
+
+    ]]></help>
+    <expand macro="eden_citation"/>
+</tool>