annotate peak_picker_hi_res.xml @ 0:ba86fd127f5a draft

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author galaxyp
date Wed, 19 Dec 2012 00:32:25 -0500
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1 <tool id="openms_peak_picker_hi_res" version="0.1" name="Peak Peaker (Hi Res)">
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2 <description>
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3 </description>
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4 <requirements>
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5 <requirement type="package">openms</requirement>
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6 </requirements>
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7 <command>
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8 openms_wrapper.py --executable 'PeakPickerHiRes' --config $config
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9 </command>
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10 <configfiles>
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11 <configfile name="config">[simple_options]
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12 in=${input}
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13 out=${out}
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14 algorithm!signal_to_noise=${signal_to_noise}
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15 algorithm!ms1_only=${ms1_only}
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16 </configfile>
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17 </configfiles>
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18 <inputs>
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19 <param format="mzML" name="input" type="data" label="Input profile peak list"/>
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20 <param type="float" name="signal_to_noise" value="1" label="Signal-to-noise ratio" help="Minimal signal-to-noise ratio for a peak to be picked (0.0 disables SNT estimation!)" />
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21 <param type="boolean" name="ms1_only" label="MS1 Only" help="If checked, peak picking is only applied to MS1 scans. Other scans are copied to the output without changes." />
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22 </inputs>
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23 <outputs>
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24 <data format="mzML" name="out" />
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25 </outputs>
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26 <help>
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27 **What it does**
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28
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29 This peak-picking algorithm detects ion signals in raw data and reconstructs the corresponding peak shape by cubic spline interpolation. Signal detection depends on the signal-to-noise ratio which is adjustable by the user (see parameter signal_to_noise). A picked peak's m/z and intensity value is given by the maximum of the underlying peak spline. Please notice that this method is still experimental since it has not been tested thoroughly yet.
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30
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31 The algorithm used by this tool is best suited for high-resolution MS data (FT-ICR-MS, Orbitrap). In high-resolution data, the signals of ions with similar mass-to-charge ratios (m/z) exhibit little or no overlapping and therefore allow for a clear separation. Furthermore, ion signals tend to show well-defined peak shapes with narrow peak width. These properties faciliate a fast computation of picked peaks so that even large data sets can be processed very quickly.
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32
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33 **Citation**
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34
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35 For the underlying tool, please cite ``Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert, and Oliver Kohlbacher, 2008. OpenMS – an Open-Source Software Framework for Mass Spectrometry. BMC Bioinformatics 9: 163. doi:10.1186/1471-2105-9-163.``
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37 If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
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38 </help>
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39 </tool>