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annotate egapx_runner.xml @ 7:9c778770514f draft

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planemo upload for repository https://github.com/ncbi/egapx commit 7d4eea19157be8d7b184cb066d05d81c73f13a04-dirty
author fubar
date Sun, 04 Aug 2024 13:21:59 +0000
parents a7304162d737
children 1680e72e27be
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1 <tool name="egapx_runner" id="egapx_runner" version="6.0.1" profile="22.05">
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2 <description>Runs egapx</description>
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3 <requirements>
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4 <requirement version="3.12.3" type="package">python</requirement>
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5 <requirement version="24.04.4-0" type="package">nextflow</requirement>
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6 <requirement version="6.0.1" type="package">pyyaml</requirement>
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7 </requirements>
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8 <version_command><![CDATA[echo "6.0.1"]]></version_command>
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9 <command><![CDATA[mkdir -p ./egapx_config &&
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10 #set econfigfile = $econfig + '.config'
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11 cp '$__tool_directory__/ui/assets/config/executor/$econfigfile' ./egapx_config/ &&
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12 python '$__tool_directory__/ui/egapx.py' '$yamlconfig' -e '$econfig' -o 'egapx_out']]></command>
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13 <inputs>
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14 <param name="yamlconfig" type="data" optional="false" label="egapx configuration yaml file to execute" help="" format="yaml,txt" multiple="false"/>
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15 <param name="econfig" type="select" label="Workflow run configuration to suit the machine in use" help="Docker minimal will run the sample minimal dustmite yaml">
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16 <option value="docker_minimal">Docker_minimal: supports only the minimal dust mite example yaml using 6GB and 4 cores</option>
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17 <option value="singularity">Singularity: requires at least 128GB ram and 32 cores. 256GB and 64 cores recommended</option>
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18 <option value="docker">Docker: requires at least 128GB ram and 32 cores. 256GB and 64 cores recommended</option>
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19 </param>
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20 </inputs>
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21 <outputs>
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22 <collection name="egapx_out" type="list" label="Outputs from egapx">
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23 <discover_datasets pattern="__name_and_ext__" directory="egapx_out" visible="false"/>
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24 </collection>
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25 </outputs>
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28 <tests>
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29 <test>
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30 <output_collection name="egapx_out" count="8"/>
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31 <param name="yamlconfig" value="yamlconfig_sample"/>
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32 <param name="econfig" value="docker_minimal"/>
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33 </test>
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34 </tests>
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38 <help><![CDATA[
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39 Galaxy tool wrapping the Eukaryotic Genome Annotation Pipeline (EGAPx)
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40 =================================================================================================
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42 .. class:: warningmark
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44 **Proof of concept: a quick hack to run a NF workflow inside a specialised Galaxy tool wrapper**
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46 EGAPx is a big, complicated Nextflow workflow, challenging and costly to re-implement **properly**, requiring dozens of new tools and replicating a lot of
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47 complicated *groovy* workflow logic.
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49 It is also very new and in rapid development. Investing developer effort and keeping updated as EGAPx changes rapidly may be *inefficient of developer resources*.
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51 This wrapper is designed to allow measuring how *inefficient* it is in terms of computing resource utilisation, in comparison to the developer effort
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52 required to convert Nextflow DDL into tools and WF logic. Balancing these competing requirements is a fundamental Galaxy challenge.
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55 EGAPx requires very substantial resources to run with real data. *128GB and 32 cores* are the minimum requirement; *256GB and 64 cores* are recommended.
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57 A special minimal example that can be run in 6GB with 4 cores is provided as a yaml configuration and is used for the tool test.
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59 In this implementation, the user must supply a yaml configuration file as initial proof of concept.
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60 History inputs and even a yaml editor might be provided in future.
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62 The NF workflow to tool model tested here may be applicable to other NF workflows that take a single configuration yaml.
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64 .. class:: warningmark
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66 The computational resource cost of typing the wrong SRA identifiers into a tool form is potentially enormous with this tool!
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69 Sample yaml configurations
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70 ===========================
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72 YAML sample configurations can be uploaded into your Galaxy history from the `EGAPx github repository <https://github.com/ncbi/egapx/tree/main/examples/>`_.
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73 The simplest possible example is shown below - can be cut/paste into a history dataset in the upload tool.
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76 *./examples/input_D_farinae_small.yaml* is included in the examples linked above. RNA-seq data is provided as URI to the reads FASTA files.
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77 These FASTA files are a sampling of the reads from the complete SRA read files to expedite testing.
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79 ::
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81 genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
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82 taxid: 6954
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83 reads:
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84 - https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
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85 - https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
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86 - https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
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87 - https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
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91 Purpose
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92 ========
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94 **This is not intended for production**
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96 Just a proof of concept.
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97 It is possibly too inefficient to be useful although it may turn out not to be a problem if run on a dedicated workstation.
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98 At least the efficiency can now be more easily estimated.
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99
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100 This tool is not recommended for public deployment because of the resource demands.
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101
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102 EGAPx Overview
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103 ===============
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104
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105 .. image:: $PATH_TO_IMAGES/Pipeline_sm_ncRNA_CAGE_80pct.png
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106
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107 **Warning:**
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108 The current version is an alpha release with limited features and organism scope to collect initial feedback on execution. Outputs are not yet complete and not intended for production use. Please open a GitHub [Issue](https://github.com/ncbi/egapx/issues) if you encounter any problems with EGAPx. You can also write to cgr@nlm.nih.gov to give us your feedback or if you have any questions.
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109
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110 EGAPx is the publicly accessible version of the updated NCBI [Eukaryotic Genome Annotation Pipeline](https://www.ncbi.nlm.nih.gov/genome/annotation_euk/process/).
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111
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112 EGAPx takes an assembly fasta file, a taxid of the organism, and RNA-seq data. Based on the taxid, EGAPx will pick protein sets and HMM models. The pipeline runs `miniprot` to align protein sequences, and `STAR` to align RNA-seq to the assembly. Protein alignments and RNA-seq read alignments are then passed to `Gnomon` for gene prediction. In the first step of `Gnomon`, the short alignments are chained together into putative gene models. In the second step, these predictions are further supplemented by _ab-initio_ predictions based on HMM models. The final annotation for the input assembly is produced as a `gff` file.
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113
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114 **Security Notice:**
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115
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116 EGAPx has dependencies in and outside of its execution path that include several thousand files from the [NCBI C++ toolkit](https://www.ncbi.nlm.nih.gov/toolkit), and more than a million total lines of code. Static Application Security Testing has shown a small number of verified buffer overrun security vulnerabilities. Users should consult with their organizational security team on risk and if there is concern, consider mitigating options like running via VM or cloud instance.
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117
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118
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119 *To specify an array of NCBI SRA datasets in yaml*
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120
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121 ::
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122
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123 reads:
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124 - SRR8506572
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125 - SRR9005248
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126
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127
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128 *To specify an SRA entrez query*
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129
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130 ::
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131
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132 reads: 'txid6954[Organism] AND biomol_transcript[properties] NOT SRS024887[Accession] AND (SRR8506572[Accession] OR SRR9005248[Accession] )'
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133
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134
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135 **Note:** Both the above examples will have more RNA-seq data than the `input_D_farinae_small.yaml` example. To make sure the entrez query does not produce a large number of SRA runs, please run it first at the [NCBI SRA page](https://www.ncbi.nlm.nih.gov/sra). If there are too many SRA runs, then select a few of them and list it in the input yaml.
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136
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137 Output
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138 =======
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139
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140 EGAPx output will appear as a collection in the user history. The main annotation file is called *accept.gff*.
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141
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142 ::
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143
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144 accept.gff
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145 annot_builder_output
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146 nextflow.log
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147 run.report.html
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148 run.timeline.html
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149 run.trace.txt
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150 run_params.yaml
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151
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152
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153 The *nextflow.log* is the log file that captures all the process information and their work directories. ``run_params.yaml`` has all the parameters that were used in the EGAPx run. More information about the process time and resources can be found in the other run* files.
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154
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155 ## Intermediate files
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156
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157 In the log, each line denotes the process that completed in the workflow. The first column (_e.g._ `[96/621c4b]`) is the subdirectory where the intermediate output files and logs are found for the process in the same line, _i.e._, `egapx:miniprot:run_miniprot`. To see the intermediate files for that process, you can go to the work directory path that you had supplied and traverse to the subdirectory `96/621c4b`:
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158
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159 ::
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160
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161 $ aws s3 ls s3://temp_datapath/D_farinae/96/
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162 PRE 06834b76c8d7ceb8c97d2ccf75cda4/
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163 PRE 621c4ba4e6e87a4d869c696fe50034/
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164 $ aws s3 ls s3://temp_datapath/D_farinae/96/621c4ba4e6e87a4d869c696fe50034/
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165 PRE output/
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166 2024-03-27 11:19:18 0
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167 2024-03-27 11:19:28 6 .command.begin
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168 2024-03-27 11:20:24 762 .command.err
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169 2024-03-27 11:20:26 762 .command.log
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170 2024-03-27 11:20:23 0 .command.out
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171 2024-03-27 11:19:18 13103 .command.run
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172 2024-03-27 11:19:18 129 .command.sh
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173 2024-03-27 11:20:24 276 .command.trace
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174 2024-03-27 11:20:25 1 .exitcode
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175 $ aws s3 ls s3://temp_datapath/D_farinae/96/621c4ba4e6e87a4d869c696fe50034/output/
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176 2024-03-27 11:20:24 17127134 aligns.paf
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177
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178
1
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179 ]]></help>
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180 <citations>
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181 <citation type="doi">10.1093/bioinformatics/bts573</citation>
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182 </citations>
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183 </tool>
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184