Mercurial > repos > galaxyp > calisp
changeset 0:6d93529d19d4 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tools/calisp commit 060699366b6dd19ad6c3ef3f332f63cc55d75dce
| author | galaxyp |
|---|---|
| date | Thu, 01 Jun 2023 08:34:14 +0000 |
| parents | |
| children | 867f17ede7f3 |
| files | calisp.xml feather2tsv.py |
| diffstat | 2 files changed, 228 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/calisp.xml Thu Jun 01 08:34:14 2023 +0000 @@ -0,0 +1,193 @@ +<tool id="calisp" name="calisp" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05"> + <description>Estimate isotopic composition of peptides from proteomics mass spectrometry data</description> + <macros> + <token name="@TOOL_VERSION@">3.0.10</token> + <token name="@VERSION_SUFFIX@">0</token> + <token name="@CALISP_REPO@">https://raw.githubusercontent.com/kinestetika/Calisp/208d495674e2b52fe56cf23457c833d1c2527242</token> + <xml name="input_macro" tokens="multiple"> + <!-- According to readme mzid input is not yet implented --> + </xml> + </macros> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">calisp</requirement> + </requirements> + <command detect_errors="aggressive"><![CDATA[ +#import re + +mkdir -p spectra && +#set escaped_specs = re.sub('[^\w\-\.,:]', '_', str($spectrum_file.element_identifier)) +ln -s '$spectrum_file' spectra/'$escaped_specs' && + +mkdir -p psms && +#set escaped_peps = re.sub('[^\w\-\.,:]', '_', str($peptide_file.element_identifier)) +ln -s '$peptide_file' psms/'$escaped_peps' && + +calisp + --spectrum_file spectra/ + --peptide_file psms/ + --output_file calisp-output/ + --mass_accuracy $mass_accuracy + --bin_delimiter '$bin_delimiter' + --threads "\${GALAXY_SLOTS:-1}" + --isotope $isotope + $compute_clumps && +'$__tool_directory__/feather2tsv.py' --calisp_output calisp-output/ + ]]></command> + <inputs> + <param argument="--spectrum_file" type="data" multiple="false" format="mzml" label="Spectrum file"/> + <param argument="--peptide_file" type="data" multiple="false" format="tabular" label="Peptide file" help="Psm file" /> + <param argument="--mass_accuracy" type="float" value="10" label="Mass accuracy" help="The maximum mass difference between theoretical mass and experimental mass of a peptide" /> + <param argument="--bin_delimiter" type="text" value="_" label="Bin delimiter" help="For metagenomic data, the delimiter that separates the bin ID from the protein ID (default: "_"). Use "-" to ignore bins ID entirely."> + <sanitizer invalid_char=""> + <valid initial="string.ascii_letters,string.digits"> + <add value="_" /> + <add value="-" /> + <add value=":" /> + </valid> + </sanitizer> + </param> + <param argument="--isotope" type="select" label="Target isotope"> + <option value="13C" selected="true">13C</option> + <option value="14C">14C</option> + <option value="15N">15N</option> + <option value="17O">17O</option> + <option value="18O">18O</option> + <option value="2H">2H</option> + <option value="3H">3H</option> + <option value="33S">33S</option> + <option value="34S">34S</option> + <option value="36S">36S</option> + </param> + <param argument="--compute_clumps" type="boolean" truevalue="--compute_clumps" falsevalue="" checked="false" label="Compute clumps" help="To compute clumpiness of carbon assimilation. Only use when samples are labeled tosaturation. Estimation of clumpiness takes much additional time." /> + </inputs> + <outputs> + <collection name="output" type="list"> + <discover_datasets pattern="(?P<designation>.*)\.tsv" format="tabular" directory="calisp-output"/> + </collection> + </outputs> + <tests> + <!-- TODO test data to large, avilable from here: https://github.com/kinestetika/Calisp/tree/master/test + if possible inlcude via location in the future + <test expect_num_outputs="1"> + <param name="spectrum_file" value="calisp_test_data.mzML" ftype="mzml"/> + <param name="peptide_file" value="calisp_test_data_TargetPeptideSpectrumMatch.txt" ftype="tabular"/> + <output_collection name="output" count="1"> + <element name="calisp_test_data"> + <assert_contents> + <has_text text="experiment"/> + <has_text text="MKH_260min_1800ng"/> + <has_text text="HOMO"/> + <has_text text="P13645"/> + <has_text text="NHEEEMKDLR"/> + <has_text text="Oxidation"/> + <has_n_columns n="85"/> + <has_n_lines n="24"/> + </assert_contents> + </element> + </output_collection> + </test> + --> + </tests> + <help><![CDATA[ +Calisp (Calgary approach to isotopes in proteomics) is a program that estimates +isotopic composition (e.g. 13C/12C, delta13C, 15N/14N etc) of peptides from +proteomics mass spectrometry data. Input data consist of mzML files and files +with peptide spectrum matches. + +Calisp was originally developed in Java. This Galaxy tool uses the python +reimplementation https://github.com/kinestetika/Calisp. +Note that, in contrast to the Java version the python reimplementation does +not use ``mcl`` . +Compared to Java versions of calisp, the workflow has been simplified. +Calisp does not filter out any isotopic patterns, or adds up isotopic +patterns to reduce noise - like the Java version does. It simply estimates the +ratio for the target isotopes (e.g. 13C/12C) for every isotopic pattern it can +subsample. It estimates this ratio based on neutron abundance and using fast +fourier transforms. The former applies to stable isotope probing experiments. +The latter applies to natural abundances, or to isotope probing experiments with +very little added label (e.g. using substrates with <1% additional 13C). The +motivation for omitting filtering is that keeping all subsampled isotopic +patterns, including bad ones, will enable training of machine learning +classifiers. Also, because it was shown that the median provides better +estimates for species in microbial communities than the mean, adding up isotopic +patterns to improve precision has lost its purpose. There is more power (and +sensitivity) in numbers. + +Because no data are filtered out and no isotopic patterns get added up, +calisp analyzes at least ten times as many isotopic patterns compared to the +Java version. That means calisp.py is about ten times slower, it takes about +5-10 min per .mzML file on a Desktop computer. For natural +abundance data, it works well to only use those spectra that have a FFT fitting +error ("error_fft") of less than 0.001. Note that this threshold is less +stringent then thew one used by the java program. + +Input +===== + +Calisp needs two inputs: a spectra file in mzML format and tabular peptipe file (PSM). +The PSM file contains a column "Spectrum File" that links the peptides to the +original spectra files. The mzML files are identified by the run id +information stored in the mzML files or the file name. +In order to make the association via the file name work in Galaxy one can either + +- use collections where the element identifiers are equal to the data in the column +- make sure that dataset names are equal to the data in this column + +Output table +============ + +Each row contains one isotopic pattern, defined by the following columns: + +========================================== =================== +Header name Content +========================================== =================== +experiment filename of the peptide spectrum match (psm) file +ms_run filename of the .mzml file +bins bin/mag ids, separated by commas. Calisp expects the protein ids in the psm file to consist of two parts, separated by a delimiter (_ by default). The first part is the bin/mag id, the second part the protein id +proteins the ids of the proteins associated with the pattern (without the bin id) +peptide the aminoacid sequence of the peptide +peptide_mass the mass of the peptide +C # of carbon atoms in the peptide +N # of nitrogen atoms in the peptide +O # of oxygen atoms in the peptide +H # of hydrogen atoms in the peptide +S # of sulfur atoms in the peptide +psm_id psm id +psm_mz psm m over z +psm_charge psm charge +psm_neutrons number of neutrons inferred from custom 'neutron' modifications +psm_rank rank of the psm +psm_precursor_id id of the ms1 spectrum that was the source of the psm +psm_precursor_mz mass over charge of the precursor of the psm +pattern_charge charge of the pattern +pattern_precursor_id id of the ms1 spectrum that was the source of the pattern +pattern_total_intensity total intensity of the pattern +pattern_peak_count # of peaks in the pattern +pattern_median_peak_spacing medium mass difference between a pattern's peaks +spectrum_mass_irregularity a measure for the standard deviation in the mass difference between a pattern's peaks +ratio_na the estimated isotope ratio inferred from neutron abundance (sip experiments) +ratio_fft the estimated isotope ratio inferred by the fft method (natural isotope abundances) +error_fft the remaining error after fitting the pattern with fft +error_clumpy the remaining error after fitting the pattern with the clumpy carbon method +flag_peptide_contains_sulfur true if peptide contains sulfur +flag_peptide_has_modifications true if peptide has no modifications +flag_peptide_assigned_to_multiple_bins true if peptide is associated with multiple proteins from different bins/mags +flag_peptide_assigned_to_multiple_proteins true if peptide is associated with multiple proteins +flag_peptide_mass_and_elements_undefined true if peptide has unknown mass and elemental composition +flag_psm_has_low_confidence true if psm was flagged as having low confidence (peptide identity uncertain) +flag_psm_is_ambiguous true if psm could not be assigned with certainty +flag_pattern_is_contaminated true if multiple patterns have one or more shared peaks +flag_pattern_is_wobbly true if pattern_median_peak_spacing exceeds a treshold +flag_peak_at_minus_one_pos true if a peak was detected immediately before the monoisotopic peak, could indicate overlap with another pattern +i0 - i19 the intensities of the first 20 peaks of the pattern +m0 - m19 the masses of the first 20 peaks of the pattern +c1 - c6 contributions of clumps of 1-6 carbon to ratio_na. These are the outcomes of the clumpy carbon model. These results are only meaningful if the biomass was labeled to saturation. +========================================== =================== + ]]></help> + <citations> + <citation type="doi">10.1186/s40168-022-01454-1</citation> + <citation type="doi">10.1073/pnas.1722325115</citation> + <citation type="doi">10.1101/2021.03.29.437612</citation> + <citation type="doi">10.1093/bioinformatics/bty046</citation> + </citations> +</tool> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/feather2tsv.py Thu Jun 01 08:34:14 2023 +0000 @@ -0,0 +1,35 @@ +#!/usr/bin/env python +""" +based on https://github.com/kinestetika/Calisp/blob/master/benchmarking/sip%20benchmarking.ipynb +""" + +import argparse +import os + +import pandas as pd + + +def load_calisp_data(filename): + + # (1) load data + if os.path.isdir(filename): + file_data = [] + for f in os.listdir(filename): + if not f.endswith(".feather"): + continue + f = os.path.join(filename, f) + file_data.append(pd.read_feather(f)) + base, _ = os.path.splitext(f) + file_data[-1].to_csv(f"{base}.tsv", sep="\t") + data = pd.concat(file_data) + else: + data = pd.read_feather(filename) + base, _ = os.path.splitext(filename) + data.to_csv(f"{base}.tsv", sep="\t") + + +parser = argparse.ArgumentParser(description='feather2tsv') +parser.add_argument('--calisp_output', required=True, help='feather file') +args = parser.parse_args() + +data = load_calisp_data(args.calisp_output)