Mercurial > repos > galaxytrakr > mitokmer

annotate mitokmer.xml @ 2:dd206296acbf draft

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author galaxytrakr
date Mon, 14 Sep 2026 19:47:05 +0000
parents e6b5e7a0d7e7
children ecf96ad08611
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1 <tool id="mitokmer" name="mitoKmer" version="2.0+galaxy0.1" python_template_version="3.5" profile="21.05">
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2 <description>Identify metagenomic mitochondrial reads by k-mer database matching</description>
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3 <requirements>
1
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4 <container type="docker">quay.io/galaxytrakr/mitokmer:927c4ee</container>
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5 </requirements>
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6
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7 <command detect_errors="exit_code"><![CDATA[
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8 ## ── All paths are hardcoded in kmer_read_m7.py: ──────────────────────
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9 ## database: ./mitochondria7/<multiple .txt files>
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10 ## jobs file: ./jobs7m/jobs7m.txt
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11 ## output: ./jobs7m/jobs7m.csv
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12 ## binary: ./kmerread7 (called as subprocess by the Python script)
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13 mkdir -p ./mitochondria7 ./jobs7m &&
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14
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15 ## ── Link database files from the data table directory ─────────────────
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16 ## The data table path points to a directory containing all the
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17 ## mitochondria7 .txt files; symlink each one into ./mitochondria7/
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18 ln -sf '${probe_db.fields.path}'/* ./mitochondria7/ &&
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19
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20 ## ── Symlink kmerread7 into the working directory ──────────────────────
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21 ## kmer_read_m7.py calls ./kmerread7 (relative path), so it must exist
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22 ## in the Galaxy job working directory
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23 ln -sf /usr/local/bin/kmerread7 ./kmerread7 &&
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24
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25 ## ── Write the jobs file ───────────────────────────────────────────────
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26 ## Format: sample_name num_files
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27 ## /abs/path/to/read1
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28 ## /abs/path/to/read2 ...
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29 #set sample_name = $reads[0].element_identifier.replace(' ', '_').split('_')[:-1] | join('_')
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30 printf '${sample_name}\t${reads|length}\n' > ./jobs7m/jobs7m.txt &&
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31 #for read in $reads
2
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32 printf '${read.file_name}\n' >> ./jobs7m/jobs7m.txt &&
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33 #end for
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34
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35 ## ── Run the Python orchestrator (no arguments) ────────────────────────
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36 ## kmer_read_m7.py reads jobs7m/jobs7m.txt, calls ./kmerread7,
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37 ## and writes results to jobs7m/jobs7m.csv
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38 python3 /opt/mitokmer2/kmer_read_m7.py &&
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39
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40 ## ── Copy output CSV to Galaxy output path ─────────────────────────────
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41 cp ./jobs7m/jobs7m.csv '${results_csv}'
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42 ]]></command>
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43
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44 <inputs>
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45 <!-- Probe database directory selected from Galaxy data table -->
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46 <param name="probe_db"
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47 type="select"
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48 label="Mitochondrial k-mer probe database"
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49 help="Select a pre-installed mitochondrial k-mer probe database.
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50 Databases are managed by your Galaxy administrator via the
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51 mitokmer_probe_db data table.">
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52 <options from_data_table="mitokmer_probe_db">
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53 <filter type="sort_by" column="1" />
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54 <validator type="no_options"
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55 message="No mitochondrial k-mer probe databases are
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56 currently installed. Please contact your
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57 Galaxy administrator." />
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58 </options>
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59 </param>
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60
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61 <!-- One or more FASTQ/FASTA files for a single sample -->
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62 <param name="reads"
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63 type="data_collection"
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64 collection_type="list"
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65 format="fastq,fastq.gz,fasta,fasta.gz"
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66 label="Input reads (FASTQ or FASTA, gzipped or plain)"
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67 help="Provide one or more read files for a single sample as a Galaxy
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68 list collection. Paired-end files (R1 + R2) should both be
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69 included in the same collection. Light quality trimming of
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70 read ends is performed internally; pre-trimming is optional." />
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71 </inputs>
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72
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73 <outputs>
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74 <data name="results_csv"
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75 format="csv"
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76 label="mitoKmer results for ${on_string}" />
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77 </outputs>
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78
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79 <tests>
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80 <test>
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81 <param name="probe_db" value="mitoch_probes_sample" />
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82 <param name="reads">
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83 <collection type="list">
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84 <element name="Plodia_R1" value="test/Plodia_R1.fastq.gz" />
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85 <element name="Plodia_R2" value="test/Plodia_R2.fastq.gz" />
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86 </collection>
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87 </param>
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88 <output name="results_csv">
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89 <assert_contents>
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90 <has_text text="Plodia" />
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91 </assert_contents>
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92 </output>
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93 </test>
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94 </tests>
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95
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96 <help><![CDATA[
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97 **mitoKmer** — Metagenomic Mitochondrial Read Identification by K-mer Database
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98 ===============================================================================
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99
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100 Overview
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101 --------
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102 mitoKmer identifies the taxonomic origin of short-read shotgun sequencing data
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103 by matching reads against a database of species-specific mitochondrial k-mer
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104 probes. It reports the relative abundance of taxa at multiple taxonomic ranks
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105 (e.g. order, species) along with the number of matching reads and supporting
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106 unique k-mers.
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107
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108 Inputs
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109 ------
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110 **Mitochondrial k-mer probe database**
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111 Select a pre-installed probe database from the dropdown. Databases are
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112 managed by your Galaxy administrator and registered in the
2
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113 ``mitokmer_probe_db`` data table. The database consists of a directory
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114 of supporting ``.txt`` files.
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115
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116 **Input reads**
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117 One or more FASTQ or FASTA files (gzipped or plain) for a single sample,
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118 supplied as a Galaxy list collection. Both paired-end files (R1 and R2)
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119 should be included in the same collection.
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120
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121 * Pre-trimming is optional — the tool performs simple window-based quality
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122 trimming of read ends internally.
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123
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124 Output
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125 ------
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126 **Results CSV**
2
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127 A comma-separated file with columns: taxid, reads, abundance, uniq.
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128 Only taxa with non-zero relative abundance are reported.
0
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129
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130 Example output for the "Plodia" test sample::
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131
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132 Rank Taxon Reads Rel_Abund(%) Unique_kmers
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133 Order Lepidoptera 10 0.1 329
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134 Species Plodia interpunctella 462 99.9 466
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135
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136 Citation
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137 --------
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138 Please cite the mitoKmer GitHub repository if you use this tool in published work.
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139 ]]></help>
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140
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141 <citations>
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142 <citation type="bibtex">
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143 @misc{githubmitokmer2,
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144 author = {Mammel, Mark},
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145 title = {mitoKmer2},
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146 year = {2024},
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147 publisher = {GitHub},
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148 journal = {GitHub repository},
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149 url = {https://github.com/mmammel8/mitokmer2},
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150 }
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151 </citation>
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152 </citations>
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153 </tool>