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1 <tool id="hfp_bettercallsal_konda" name="bettercallsal" version="1.0.0+galaxy21">
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2 <description>An automated workflow to assign Salmonella serotype based on NCBI Pathogen Detection Project for Salmonella.</description>
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3 <requirements>
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4 <requirement type="package" version="24.04.3">nextflow</requirement>
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5 <requirement type="package" version="1.5.8">micromamba</requirement>
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6 <requirement type="package">graphviz</requirement>
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7 </requirements>
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8 <version_command>nextflow -version</version_command>
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9 <command detect_errors="exit_code"><![CDATA[
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10 input_path=\$(pwd)"/cpipes-input";
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11 mkdir -p "\${input_path}" || exit 1;
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12 #import re
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13 #if (str($input_read_type_cond.input_read_type) == "single_long" or str($input_read_type_cond.input_read_type) == "long_long"):
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14 #for _, $unpaired in enumerate($input_read_type_cond.input):
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15 #set read1 = str($unpaired.name)
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16 #if not str($unpaired.name).endswith(('.fastq', '.fastq.gz')):
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17 #set read1_ext = re.sub('fastqsanger', 'fastq', str($unpaired.ext))
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18 #set read1 = str($unpaired.name) + str('.') + $read1_ext
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19 #end if
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20 ln -sf '$unpaired' "\${input_path}/$read1";
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21 #end for
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22 #elif (str($input_read_type_cond.input_read_type) == "paired"):
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23 #for _, $pair in enumerate($input_read_type_cond.input_pair)
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24 #set read_R1 = re.sub('\:forward', '_forward', str($pair.forward.name))
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25 #set read_R2 = re.sub('\:reverse', '_reverse', str($pair.reverse.name))
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26 #set read_R1_ext = re.sub('fastqsanger', 'fastq', str($pair.forward.ext))
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27 #set read_R2_ext = re.sub('fastqsanger', 'fastq', str($pair.reverse.ext))
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28 #if not str($pair.forward.name).endswith(('.fastq', '.fastq.gz')):
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29 #set read_R1 = $read_R1 + str('.') + $read_R1_ext
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30 #end if
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31 #if not str($pair.reverse.name).endswith(('.fastq', '.fastq.gz')):
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32 #set read_R2 = $read_R2 + str('.') + $read_R2_ext
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33 #end if
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34 ln -sf '$pair.forward' "\${input_path}/$read_R1";
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35 ln -sf '$pair.reverse' "\${input_path}/$read_R2";
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36 #end for
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37 #end if
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38 $__tool_directory__/1.0.0/cpipes
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39 #if (str($input_read_type_cond.input_read_type) == "long_long"):
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40 --pipeline bettercallsal_lr
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41 #else
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42 --pipeline bettercallsal
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43 #end if
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44 --input \${input_path}
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45 --output cpipes-output
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46 --fq_suffix '${input_read_type_cond.fq_suffix}'
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47 #if (str($input_read_type_cond.input_read_type) == "long_long"):
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48 --fq_single_end true
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49 #elif (str($input_read_type_cond.input_read_type) == "single_long"):
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50 --fq_single_end true
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51 #elif (str($input_read_type_cond.input_read_type) == "paired"):
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52 --fq_single_end false --fq2_suffix '${input_read_type_cond.fq2_suffix}'
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53 #end if
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54 --tuspy_n $tuspy_n
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55 #if ($sourmash_cond.run == "true"):
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56 --sfhpy_fcv $sourmash_cond.sfhpy_fcv
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57 #end if
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58 #if ($bcs_thresholds != 'relax' and str($input_read_type_cond.input_read_type) != "long_long"):
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59 --kmaalign_ID $kma_id
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60 #end if
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61 #if ($sourmash_cond.run == "true"):
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62 --sfhpy_fcv $sourmash_cond.sfhpy_fcv
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63 #end if
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64 --bcs_db_mode $bcs_db_mode
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65 --bcs_thresholds $bcs_thresholds
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66 --fq_filename_delim '${fq_filename_delim}'
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67 --fq_filename_delim_idx $fq_filename_delim_idx
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68 -profile kondagac;
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69 #if (str($input_read_type_cond.input_read_type) == "long_long"):
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70 mv './cpipes-output/bettercallsal_lr-multiqc/CPIPES-Report_multiqc_report.html' './multiqc_report.html' || exit 1;
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71 #else
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72 mv './cpipes-output/bettercallsal-multiqc/CPIPES-Report_multiqc_report.html' './multiqc_report.html' || exit 1;
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73 #end if
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74 rm -rf ./cpipes-output || exit 1;
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75 rm -rf ./work || exit 1;
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76 ]]></command>
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77 <inputs>
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78 <conditional name="input_read_type_cond">
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79 <param name="input_read_type" type="select" label="Select the read collection type">
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80 <option value="single_long" selected="true">Single-End short reads</option>
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81 <option value="paired">Paired-End short reads</option>
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82 <option value="long_long">Long reads</option>
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83 </param>
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84 <when value="single_long">
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85 <param name="input" type="data_collection" collection_type="list" format="fastq,fastq.gz"
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86 label="Dataset list of unpaired short reads" />
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87 <param name="fq_suffix" value=".fastq.gz" type="text" label="Suffix of the Single-End FASTQ"/>
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88 </when>
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89 <when value="long_long">
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90 <param name="input" type="data_collection" collection_type="list" format="fastq,fastq.gz"
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91 label="Dataset list of long reads" />
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92 <param name="fq_suffix" value=".fastq.gz" type="text" label="Suffix of the long read FASTQ"/>
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93 </when>
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94 <when value="paired">
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95 <param name="input_pair" type="data_collection" collection_type="list:paired" format="fastq,fastq.gz" label="List of Dataset pairs" />
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96 <param name="fq_suffix" value="_R1_001.fastq.gz" type="text" label="Suffix of the R1 FASTQ"
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97 help="For any data sets downloaded from NCBI into Galaxy, change this to _forward.fastq.gz suffix."/>
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98 <param name="fq2_suffix" value="_R2_001.fastq.gz" type="text" label="Suffix of the R2 FASTQ"
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99 help="For any data sets downloaded from NCBI into Galaxy, change this to _reverse.fastq.gz suffix."/>
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100 </when>
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101 </conditional>
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102 <param name="bcs_db_mode" type="select" label="Select the database mode with bettercallsal"
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103 help="Refer to `Database generation` section in our manuscript: https://doi.org/10.3389/fmicb.2023.1200983">
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104 <option value="snp" selected="true">per_snp_cluster</option>
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105 <option value="comp">per_computed_type</option>
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106 </param>
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107 <param name="tuspy_n" optional="true" value="10" type="integer" label="Enter the number of top unique serotypes to retain after initial MASH screen step"
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108 help="The default value of 10 is suitable for almost all scenarios."/>
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109 <param name="bcs_thresholds" type="select" label="Enter the type of base quality thresholds to be set with bettercallsal"
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110 help="The default value sets strictest thresholds that tends to filter out most of the false positive hits.">
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111 <option value="strict" selected="true">strict</option>
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112 <option value="relax">relax</option>
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113 </param>
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114 <param name="kma_id" optional="true" value="10.0" type="text" label="Enter the %ID threshold for KMA alignments of samples against genomes"
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115 help="The default value of 10% works well for enrichment samples tested within FDA. The 'relax' preset for base quality thresholds automatically sets this value to 5%."/>
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116 <conditional name="sourmash_cond">
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117 <param name="run" type="select" label="Run sourmash"
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118 help="Should sourmash be used for additional genome fraction filtering">
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119 <option value="true" selected="true">yes</option>
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120 <option value="false">no</option>
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121 </param>
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122 <when value="true">
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123 <param name="sfhpy_fcv" type="text" value="0.1" label="Enter the minimum coverage match with sourmash before a serotype hit is considered for further processing"
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124 help="The default value is set at 10% coverage threshold."/>
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125 </when>
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126 <when value="false">
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127 <param name="sfhpy_fcv" type="select" label="Enter the minimum coverage match with sourmash before a serotype hit is considered for further processing"
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128 help="THIS OPTION IS IGNORED IF SOURMASH TOOL IS NOT RUN.">
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129 <option value="NA" selected="true">N/A</option>
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130 </param>
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131 </when>
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132 </conditional>
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133 <param name="fq_filename_delim" type="text" value="_" label="File name delimitor by which samples are grouped together (--fq_filename_delim)"
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134 help="This is the delimitor by which samples are grouped together to display in the final MultiQC report. For example, if your input data sets are mango_replicate1.fastq.gz, mango_replicate2.fastq.gz, orange_replicate1_maryland.fastq.gz, orange_replicate2_maryland.fastq.gz, then to create 2 samples mango and orange, the value for --fq_filename_delim would be _ (underscore) and the value for --fq_filename_delim_idx would be 1, since you want to group by the first word (i.e. mango or orange) after splitting the filename based on _ (underscore)."/>
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135 <param name="fq_filename_delim_idx" type="integer" value="1" label="File name delimitor index (--fq_filename_delim_idx)" />
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136 <!-- <param name="runtime_profile" type="select" label="Run time profile">
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137 <option value="kondagac" selected="true">conda</option>
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138 <option value="cingularitygac">singularity</option>
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139 </param> -->
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140 </inputs>
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141 <outputs>
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142 <data name="multiqc_report" format="html" label="bettercallsal: MultiQC Report on ${on_string}" from_work_dir="multiqc_report.html"/>
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143 </outputs>
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144 <tests>
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145 <!--Test 01: long reads-->
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146 <test expect_num_outputs="2">
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147 <param name="input">
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148 <collection type="list">
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149 <element name="FAL11127.fastq.gz" value="FAL11127.fastq.gz" />
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150 <element name="FAL11341.fastq.gz" value="FAL11341.fastq.gz" />
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151 <element name="FAL11342.fastq.gz" value="FAL11342.fastq.gz" />
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152 </collection>
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153 </param>
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154 <param name="fq_suffix" value=".fastq.gz"/>
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155 <output name="multiqc_report" file="multiqc_report.html" ftype="html" compare="sim_size"/>
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156 <!-- <output name="assembled_mags" file="FAL11127.assembly_filtered.contigs.fasta" ftype="fasta" compare="sim_size"/> -->
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157 </test>
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158 </tests>
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159 <help><![CDATA[
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160
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161 .. class:: infomark
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162
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163 **Purpose**
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164
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165 bettercallsal is an automated workflow to assign Salmonella serotype based on NCBI Pathogen Detection Project for Salmonella.
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166 It uses MASH to reduce the search space followed by additional genome filtering with sourmash. It then performs genome based
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167 alignment with kma followed by count generation using salmon. This workflow can be used to analyze shotgun metagenomics
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168 datasets, quasi-metagenomic datasets (enriched for Salmonella) and target enriched datasets (enriched with molecular baits specific for Salmonella)
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169 and is especially useful in a case where a sample is of multi-serovar mixture.
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170
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171 It is written in Nextflow and is part of the modular data analysis pipelines (CFSAN PIPELINES or CPIPES for short) at CFSAN.
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172
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173
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174 ----
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175
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176 .. class:: infomark
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177
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178 **Testing and Validation**
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179
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180 The CPIPES - bettercallsal Nextflow pipeline has been wrapped to make it work in Galaxy. It takes in either paired or unpaired short reads list as an input
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181 and generates a MultiQC report in the final step. The pipeline has been tested on 2x300 bp MiSeq and 2x150 bp NextSeq simulated reads and has been shown to call multiple
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182 Salmonella serotypes with up to ~95% accuracy. The pipeline has also been tested on metagenomics data sets from Peach and Papaya outbreaks as discussed in
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183 our publication (https://www.frontiersin.org/articles/10.3389/fmicb.2023.1200983/full). All the original testing and validation was
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184 done on the command line on the CFSAN Raven2 HPC Cluster.
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185
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186
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187 ----
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188
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189 .. class:: infomark
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190
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191 **Outputs**
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192
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193 The main output file is a:
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194
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195 ::
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196
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197 - MultiQC Report: Contains a brief summary report including any serotyping and AMR result tables.
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198 Please note that due to MultiQC customizations, the preview (eye icon) will not
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199 work within Galaxy for the MultiQC report. Please download the file by clicking
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200 on the floppy icon and view it in your browser on your local desktop/workstation.
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201 You can export the tables and plots from the downloaded MultiQC report.
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202
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203 ]]></help>
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204 <citations>
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205 <citation type="bibtex">
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206 @article{bettercallsal,
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207 author = {Konganti, Kranti},
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208 year = {2023},
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209 month = {August},
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210 title = {bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting},
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211 journal = {Frontiers in Microbiology},
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212 doi = {10.3389/fmicb.2023.1200983},
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213 url = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1200983/full}}
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214 </citation>
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215 </citations>
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216 </tool>
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