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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tools/calisp commit 060699366b6dd19ad6c3ef3f332f63cc55d75dce

<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: &quot;_&quot;). Use &quot;-&quot; 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&lt;designation&gt;.*)\.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>