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<div class="textblock"><p>Data reduction in LC-MS analysis mostly consists of two steps. In the first step, called "peak picking", important information of the mass spectrometric peaks (e.g. peaks' mass centroid positions, their areas under curve and full-width-at-half-maxima) are extracted from the raw LC-MS data. The second data reduction step, called "feature finding", represents the quantitation of all peptides in a proteomic sample. Therefore, the signals in a LC-MS map caused by all charge and isotopic variants of the peptide are detected and summarized resulting in a list of compounds or features, each characterized by mass, retention time and abundance. The classes described in this section can be found in the <em>TRANSFORMATIONS</em> folder.</p>

A peptide feature at different stages of data reduction.</div></div>

Peak picking</h1>

<p>For peak picking, the class <em>PeakPickerCWT</em> or <em>PeakPickerHiRes</em> is used. Because this class detects and extracts mass spectrometric peaks it is applicable to LC-MS as well as MALDI raw data.</p>

<p>The following example (Tutorial_PeakPickerCWT.C) shows how to open a raw map, initialize a PeakPickerCWT object, set the most important parameters (the scale of the wavelet, a peak's minimal height and fwhm), and start the peak picking process.</p>

<div class="fragment"><div class="line"><span class="keywordtype">int</span> <a class="code" href="RNPxl_8C.html#a217dbf8b442f20279ea00b898af96f52">main</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span>** argv)</div>

<div class="line"> <span class="keywordflow">if</span> (argc < 2) <span class="keywordflow">return</span> 1;</div>

<div class="line"> <span class="comment">// the path to the data should be given on the command line</span></div>

<div class="line"> String tutorial_data_path(argv[1]);</div>

<div class="line"> PeakMap exp_raw;</div>

<div class="line"> PeakMap exp_picked;</div>

<div class="line"> MzMLFile mzml_file;</div>

<div class="line"> mzml_file.load(tutorial_data_path + <span class="stringliteral">"/data/Tutorial_PeakPickerCWT.mzML"</span>, exp_raw);</div>

<div class="line"> PeakPickerCWT pp;</div>

<div class="line"> Param param;</div>

<div class="line"> param.setValue(<span class="stringliteral">"peak_width"</span>, 0.1);</div>

<div class="line"> pp.setParameters(param);</div>

<div class="line"> pp.pickExperiment(exp_raw, exp_picked);</div>

<div class="line"> exp_picked.updateRanges();</div>

<div class="line"> cout << <span class="stringliteral">"\nMinimal fwhm of a mass spectrometric peak: "</span> << (<a class="code" href="group__Concept.html#gace75bfb1aba684e874dffee13738bd15">DoubleReal</a>)param.getValue(<span class="stringliteral">"peak_width"</span>)</div>

<div class="line"> << <span class="stringliteral">"\n\nNumber of picked peaks "</span> << exp_picked.getSize() << std::endl;</div>

<div class="line"> <span class="keywordflow">return</span> 0;</div>

</div></p>

<p>The output of the program is: </p>

<div class="fragment"><div class="line">Scale of the wavelet: 0.2</div>

<div class="line">Minimal fwhm of a mass spectrometric peak: 0.1</div>

<div class="line">Minimal intensity of a mass spectrometric peak 500</div>

<div class="line">Number of picked peaks 14</div>

</div><dl class="section note"><dt>Note</dt><dd>A rough standard value for the peak's scale is the average fwhm of a mass spectrometric peak.</dd></dl>

Feature detection</h1>

<p>The FeatureFinders implement different algorithms for the detection and quantitation of peptides from LC-MS maps. In contrast to the previous step (peak picking), we do not only search for pronounced signals (peak) in the LC-MS map but search explicitly for peptides which can be recognized by their isotopic pattern.</p>

<p>OpenMS offers different algorithms for this task. Details of how to apply them are given in the TOPP documentation. Please also refer to our publications on the OpenMS web page. TOPP contains multiple command line programs which allow to execute our algorithms without writing a single line of code.</p>

<p>But you can also write your own FeatureFinder application. This gives you more flexibility and is straightforward to do. A short example (Tutorial_FeatureFinder.C) is given below. First we need to instantiate the FeatureFinder, its parameters and the input/output data:</p>

<div class="fragment"><div class="line"> FeatureFinder ff;</div>

<div class="line"> <span class="comment">// ... set parameters (e.g. from INI file)</span></div>

<div class="line"> Param parameters;</div>

<div class="line"> <span class="comment">// ... set input data (e.g. from mzML file)</span></div>

<div class="line"> MSExperiment<> input;</div>

<div class="line"> <span class="comment">// ... set output data structure</span></div>

</div></p>

<p>Then we run the FeatureFinder. The first argument is the algorithm name (here 'simple'). Using the second and third parameter, the peak and feature data is handed to the algorithm. The fourth argument sets the parameters used by the algorithm. <div class="fragment"><div class="line"> <a class="code" href="classOpenMS_1_1FeatureMap.html">FeatureMap<></a> output;</div>

<div class="line"> <span class="comment">// ... set user-specified seeds, if needed</span></div>

<div class="line"> <a class="code" href="classOpenMS_1_1FeatureMap.html">FeatureMap<></a> seeds;</div>

<div class="line"> ff.run(<span class="stringliteral">"simple"</span>, input, output, parameters, seeds);</div>

</div> Now the FeatureMap is filled with the found features. </p>

</div></div>

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<TD><font color="#c0c0c0">OpenMS / TOPP release 1.11.1</font></TD>

<TD align="right"><font color="#c0c0c0">Documentation generated on Thu Nov 14 2013 11:19:24 using doxygen 1.8.5</font></TD>

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