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author galaxytrakr
date Thu, 30 Apr 2026 19:52:07 +0000
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1 <tool id="plasmidtrakr" name="PlasmidTrakr" version="0.2.3">
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2 <description>Screens assemblies against a Mash database and predicts isolate source using a trained machine learning model</description>
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3
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4 <requirements>
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5 <requirement type="package" version="2.3">mash</requirement>
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6 <requirement type="package" version="2.3.3">pandas</requirement>
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7 <requirement type="package" version="1.6.1">scikit-learn</requirement>
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8 </requirements>
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9
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10 <command detect_errors="exit_code"><![CDATA[
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11 ## 1. Setup names and database
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12 #set $input_name = $assembly_input.element_identifier.replace(" ", "_").replace(".", "_")
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13 ln -s '$mash_database.fields.path' queries.msh &&
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15 ## 2. Run Mash Screen
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16 mash screen
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17 -w
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18 -i $threshold
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19 queries.msh
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20 '$assembly_input'
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21 > '${input_name}_mash.tabular'
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22 &&
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23
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24 ## 3. Conditional Logic: Check if Mash produced hits
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25 if [ \$(wc -l < '${input_name}_mash.tabular') -ge 2 ]; then
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26 python $__tool_directory__/predict_source.py
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27 -i '${input_name}_mash.tabular'
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28 -b '$model_selection.fields.path'
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29 -t $threshold
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30 -o '$prediction_output';
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31
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32 ## Optional: If the script ran but produced no results (e.g. filtered out)
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33 ## ensure we still provide the "No Prediction" fallback
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34 if [ ! -s '$prediction_output' ] || [ \$(wc -l < '$prediction_output') -le 1 ]; then
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35 echo -e "Run\tPredicted_Source\tConfidence_Score" > '$prediction_output';
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36 echo -e "${input_name}\tNo Prediction\t0.0" >> '$prediction_output';
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37 fi
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38 else
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39 ## Skip Python and create the fallback file immediately
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40 echo -e "Run\tPredicted_Source\tConfidence_Score" > '$prediction_output';
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41 echo -e "${input_name}\tNo Prediction\t0.0" >> '$prediction_output';
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42 fi
0
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43 ]]></command>
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45 <inputs>
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46 <param name="assembly_input" type="data" format="fasta,fasta.gz,fastq,fastq.gz" label="Genome Assembly / Reads" help="The FASTA/FASTQ file containing the isolate sequence."/>
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47
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48 <param name="mash_database" type="select" label="Select Mash Database" help="Choose the pre-computed Mash sketch database to screen against.">
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49 <options from_data_table="mash_sketches">
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50 <validator type="no_options" message="No Mash databases are configured. Please contact your Galaxy administrator." />
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51 </options>
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52 </param>
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53
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54 <param name="model_selection" type="select" label="Select Prediction Model" help="Choose which trained model to use for prediction.">
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55 <options from_data_table="plasmidtrakr_models">
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56 <validator type="no_options" message="No prediction models are configured. Please contact your Galaxy administrator." />
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57 </options>
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58 </param>
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60 <param name="threshold" type="float" value="0.95" min="0.0" max="1.0" label="Mash Identity Threshold" help="Filter plasmid hits below this identity. Must match the threshold used for model training."/>
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61 </inputs>
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62
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63 <outputs>
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64 <data name="prediction_output" format="tabular" label="Prediction for ${on_string}" />
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65 </outputs>
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66
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67 <help><![CDATA[
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68 **What it does**
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69
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70 This tool performs a complete workflow in a single step: it screens a genome assembly or read set against a built-in plasmid database using **mash screen**, and then feeds those plasmid hits into a pre-trained **machine learning model** to predict the original source of the isolate.
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71
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72 **Workflow**
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73 1. Provide your **genome assembly (FASTA)** or raw reads.
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74 2. Select your **Mash database** from the server's configured list.
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75 3. Select the desired prediction model.
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76 4. Execute to screen and predict in one step.
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77
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78 **Output**
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79 A tabular file containing the isolate ID, the predicted source, and a confidence score.
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80 ]]></help>
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81
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82 <citations>
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83 <citation type="bibtex">
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84 @misc{strain_2026_plasmidtrakr,
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85 author = {Strain, Errol},
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86 title = {PlasmidTrakr: A tool for predicting isolate source from plasmid profiles},
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87 year = {2026},
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88 publisher = {GitHub},
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89 journal = {GitHub repository},
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90 howpublished = {\url{https://github.com/estrain/plasmidtrakr}}
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91 }
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92 </citation>
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93 </citations>
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94 </tool>