Mercurial > repos > galaxyp > pyprophet_subsample
changeset 0:973e601622a1 draft
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit a83d231286a8df67483df46e76b4b3a2ef90b251"
| author | galaxyp |
|---|---|
| date | Wed, 26 Feb 2020 04:16:12 -0500 |
| parents | |
| children | 2e9d5337885e |
| files | macros.xml pyprophet_subsample.xml test-data/merged.osw test-data/open_swath_output1.osw test-data/open_swath_output2.osw test-data/output.tabular test-data/patient_specific_OSW_optimized_decoys.pqp test-data/peptide1.osw test-data/peptide1.pdf test-data/peptide2.osw test-data/peptide2.pdf test-data/protein1.osw test-data/protein1.pdf test-data/protein2.osw test-data/protein2.pdf test-data/score.osw test-data/score_plots.pdf test-data/score_report.pdf test-data/study_design.tabular test-data/subsample.tabular test-data/test_data.osw |
| diffstat | 21 files changed, 77 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Wed Feb 26 04:16:12 2020 -0500 @@ -0,0 +1,23 @@ +<macros> + <token name="@VERSION@">2.1.4</token> + + <xml name="requirements"> + <requirements> + <requirement type="package" version="2.1.4">pyprophet</requirement> + <yield/> + </requirements> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">10.1038/nmeth.4398</citation> + <citation type="doi">10.1038/nbt.3908</citation> + <citation type="doi">10.1093/bioinformatics/btu686</citation> + <citation type="doi">10.1038/nmeth.1584</citation> + <yield/> + </citations> + </xml> + + <token name="@link@">http://openswath.org/en/latest/docs/pyprophet.html</token> + +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pyprophet_subsample.xml Wed Feb 26 04:16:12 2020 -0500 @@ -0,0 +1,50 @@ +<tool id="pyprophet_subsample" name="PyProphet subsample" version="@VERSION@.0"> + <description> + Subsample OpenSWATH file + </description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements"/> + <command detect_errors="aggressive"> + <![CDATA[ + pyprophet subsample + --in='$input' + --subsample_ratio=$subsample_ratio + $test_mode + --out='./output.osw' + + ]]> + </command> + <inputs> + <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format." /> + <param argument="--subsample_ratio" type="float" value="1" label="Subsample ratio used per input file (between 0 (0 %) and 1 (100 %))" /> + <param name="test_mode" type="boolean" truevalue="--test" falsevalue="--no-test" label="Run in test mode with fixed seed" /> + </inputs> + <outputs> + <data name="output" format="osw" label="${tool.name} on ${on_string}: subsample.osw" from_work_dir="output.osw" /> + </outputs> + <tests> + <test> + <param name="input" value="merged.osw" ftype="osw" /> + <param name="subsample_ratio" value="0.5" /> + <param name="test_mode" value="True" /> + <output name="output" file="subsample.tabular" /> + </test> + </tests> + <help> +<![CDATA[ +**What it does** + +PyProphet: Semi-supervised learning and scoring of OpenSWATH results. + +Subsample OpenSWATH file to minimum for integrated scoring. + +PyProphet is a Python re-implementation of the mProphet algorithm (Reiter 2010 Nature Methods) optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in (Telemann 2014 Bioinformatics) and has since been extended to support new data types and analysis modes (Rosenberger 2017, Nature biotechnology and Nature methods). + +For more information, visit @link@ + +]]> + </help> + <expand macro="citations"/> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.tabular Wed Feb 26 04:16:12 2020 -0500 @@ -0,0 +1,1 @@ +transition_group_id decoy run_id filename RT assay_rt delta_rt iRT assay_iRT delta_iRT Sequence FullPeptideName Charge mz Intensity aggr_prec_Peak_Area aggr_prec_Peak_Apex leftWidth rightWidth peak_group_rank d_score m_score id aggr_Peak_Area aggr_Peak_Apex aggr_Fragment_Annotation ProteinName m_score_peptide_experiment_wide m_score_peptide_global m_score_protein_experiment_wide m_score_protein_global