Abstract
In liquid chromatography-mass spectrometry (LC-MS) based expression proteomics, samples from different groups are analyzed comparatively in order to detect differences that can possibly be caused by the disease under study (potential biomarker detection). To this end, advanced computational techniques are needed. Peak alignment and detection are two key steps in the analysis process of LC-MS datasets. In this paper we propose an algorithm for LC-MS peak detection and alignment. The goal of the algorithm is to group together peaks generated by the same peptide but detected in different samples. It employs clustering with a new weighted similarity measure and automatic selection of the number of clusters. Moreover, it supports parallelization by acting on blocks. Finally, it allows incorporation of available domain knowledge for constraining and refining the search for aligned peaks. Application of the algorithm to a LC-MS dataset generated by a spike-in experiment substantiates the effectiveness of the proposed technique.
Keywords
- Peak Detection
- Isotopic Peak
- Peak Alignment
- True Peak
- Correlation Optimise Warping
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Codrea, M.C., Jiménez, C.R., Piersma, S., Heringa, J., Marchiori, E. (2007). Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_4
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DOI: https://doi.org/10.1007/978-3-540-71783-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71782-9
Online ISBN: 978-3-540-71783-6
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