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planemo upload commit cf01f51a17a43906355f9545ece73a4e671e258b
author galaxytrakr
date Thu, 30 Apr 2026 19:52:07 +0000
parents 706b2bbc64ed
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16:706b2bbc64ed 17:36d49a2e6bbe
1 <tool id="plasmidtrakr" name="PlasmidTrakr" version="0.2.2"> 1 <tool id="plasmidtrakr" name="PlasmidTrakr" version="0.2.3">
2 <description>Screens assemblies against a Mash database and predicts isolate source using a trained machine learning model</description> 2 <description>Screens assemblies against a Mash database and predicts isolate source using a trained machine learning model</description>
3 3
4 <requirements> 4 <requirements>
5 <requirement type="package" version="2.3">mash</requirement> 5 <requirement type="package" version="2.3">mash</requirement>
6 <requirement type="package" version="2.3.3">pandas</requirement> 6 <requirement type="package" version="2.3.3">pandas</requirement>
7 <requirement type="package" version="1.6.1">scikit-learn</requirement> 7 <requirement type="package" version="1.6.1">scikit-learn</requirement>
8 </requirements> 8 </requirements>
9 9
10 <command detect_errors="exit_code"><![CDATA[ 10 <command detect_errors="exit_code"><![CDATA[
11 ## 1. Setup names and database 11 ## 1. Setup names and database
12 #set $input_name = $assembly_input.element_identifier.replace(" ", "_") 12 #set $input_name = $assembly_input.element_identifier.replace(" ", "_").replace(".", "_")
13 ln -s '$mash_database.fields.path' queries.msh && 13 ln -s '$mash_database.fields.path' queries.msh &&
14 14
15 ## 2. Run Mash Screen 15 ## 2. Run Mash Screen
16 mash screen 16 mash screen
17 -w 17 -w
18 -i $threshold 18 -i $threshold
19 queries.msh 19 queries.msh
20 '$assembly_input' 20 '$assembly_input'
21 > mash_results.tabular 21 > '${input_name}_mash.tabular'
22 && 22 &&
23 23
24 ## 3. Conditional Logic: Check if Mash produced hits 24 ## 3. Conditional Logic: Check if Mash produced hits
25 ## [ -s file ] checks if file exists and has size > 0 25 if [ \$(wc -l < '${input_name}_mash.tabular') -ge 2 ]; then
26 if [ -s mash_results.tabular ]; then 26 python $__tool_directory__/predict_source.py
27 ## Run the prediction script only if there is data 27 -i '${input_name}_mash.tabular'
28 python $__tool_directory__/predict_source.py 28 -b '$model_selection.fields.path'
29 -i mash_results.tabular 29 -t $threshold
30 -b '$model_selection.fields.path' 30 -o '$prediction_output';
31 -t $threshold
32 -o '$prediction_output';
33 31
34 ## Optional: If the script ran but produced no results (e.g. filtered out) 32 ## Optional: If the script ran but produced no results (e.g. filtered out)
35 ## ensure we still provide the "No Prediction" fallback 33 ## ensure we still provide the "No Prediction" fallback
36 if [ \$(wc -l < '$prediction_output') -le 1 ]; then 34 if [ ! -s '$prediction_output' ] || [ \$(wc -l < '$prediction_output') -le 1 ]; then
37 echo -e "Run\tPredicted_Source\tConfidence_Score" > '$prediction_output'; 35 echo -e "Run\tPredicted_Source\tConfidence_Score" > '$prediction_output';
38 echo -e "${input_name}\tNo Prediction\t0.0" >> '$prediction_output'; 36 echo -e "${input_name}\tNo Prediction\t0.0" >> '$prediction_output';
39 fi 37 fi
40 else 38 else
41 ## Skip Python and create the fallback file immediately 39 ## Skip Python and create the fallback file immediately