Issue No. 01 - January-March (2010 vol. 7)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.31
Richard Pelikan , University of Pittsburgh, Pittsburgh
Milos Hauskrecht , University of Pittsburgh, Pittsburgh
Whole-sample mass spectrometry (MS) proteomics allows for a parallel measurement of hundreds of proteins present in a variety of biospecimens. Unfortunately, the association between MS signals and these proteins is not straightforward. The need to interpret mass spectra demands the development of methods for accurate labeling of ion species in such profiles. To aid this process, we have developed a new peak-labeling procedure for associating protein and peptide labels with peaks. This computational method builds upon characteristics of proteins expected to be in the sample, such as the amino sequence, mass weight, and expected concentration within the sample. A new probabilistic score that incorporates this information is proposed. We evaluate and demonstrate our method's ability to label peaks first on simulated MS spectra and then on MS spectra from human serum with a spiked-in calibration mixture.
Machine learning, biology and genetics, heuristics design.
M. Hauskrecht and R. Pelikan, "Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 126-137, 2008.