Algorithm | Input data | Description |
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simplest | raw data | This is an algorithm for feature detection in raw data. The following components are used:
Seeding:
Extension:
Modelling: If the model fits well to the data, we report a feature. Otherwise, the seed and the whole region is discarded, and its data points are marked as unused again. (class ModelFitter) See the FeatureFinderAlgorithmSimplest Parameters page for a documented list of configuration options for this algorithm.
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simple | raw data | This is another algorithm for feature detection in raw data. It is similar to the "simplest" algorithm, except for the modeling. Seeding: see "simplest" Extension: see "simplest"
Modelling: See the FeatureFinderAlgorithmSimple Parameters page for a documented list of configuration options for this algorithm.
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picked_peak | peak data | This is an experimental algorithm for feature detection based on peak data. In contrast to the other algorithms, it is based on peak/stick data, which makes it applicable even if no raw data is available. Another advantage is its speed due to the reduced amount of data after peak picking.
Seeding:
Extension:
Modelling: See the FeatureFinderAlgorithmPicked Parameters page for a documented list of configuration options for this algorithm.
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The first step in this pipeline is to find the features of the HPLC-MS map. The FeatureFinder application calculates the features from a raw/peak map.
In the second step, the labeled pairs (light/heavy) are determined by the LabeledMatcher. The LabeledMatcher first determines all possible pairs according to a given optimal shift and deviations in RT and m/z. Then it resolves ambiguous pairs using a greedy-algorithm that prefers pairs with a higher score. The score of a pair is the product of:
Mapping feature maps can be done with the MapAlignment tool. Please have a look at Example 4: Map alignment.
Ole Schulz-Trieglaff, Rene Hussong, Clemens Gr�pl, Andreas Leinenbach, Andreas Hildebrandt, Christian Huber, Knut Reinert "Computational Quantification of Peptides from LC-MS data". Journal of Comptational Biology, 2008. to appear.
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Ole Schulz-Trieglaff, Rene Hussong, Clemens Gr�pl, Andreas Hildebrandt, Knut Reinert "A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry data". In "Proceedings of the Eleventh Annual International Conference on Research in Computational Molecular Biology (RECOMB 2007)", pages 473-487, 2007.
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Bettina Mayr, Oliver Kohlbacher, Knut Reinert, Marc Sturm, Clemens Gr�pl, Eva Lange, Christoph Klein, Christian Huber "Absolute Myoglobin Quantitation in Serum by Combining Two-Dimensional Liquid Chromatography-Electrospray Ionization Mass Spectrometry and Novel Data Analysis Algorithms". Journal of Proteome Research, volume 5, pages 414-421, 2006.
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Clemens Gr�pl, Eva Lange, Knut Reinert, Oliver Kohlbacher, Marc Sturm, Christian G. Huber, Bettina M. Mayr, Christoph L. Klein "Algorithms for the automated absolute quantification of diagnostic markers in complex proteomics samples". In "Proceedings of the 1st International Symposium on Computational Life Science (CompLife05)", pages 151-163, 2005.
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