Abstract
Mass-spectrometry (MS) is a powerful experimental technology for ”sequencing” proteins in complex biological mixtures. Computational methods are essential for the interpretation of MS data, and a number of theoretical questions remain unresolved due to intrinsic complexity of the related algorithms. Here we design an analytical approach to estimate the confidence values of peptide identification in so-called database search methods. The approach explores properties of mass tags — sequences of mass values (m1 m2 ... mn), where individual mass values are distances between spectral lines. We define p-function — the probability of finding a random match between any given tag and a protein database — and verify the concept with extensive tag search experiments. We then discuss p-function properties, its applications for finding highly reliable matches in MS experiments, and a possibility to analytically evaluate properties of SEQUEST X-correlation function.
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Arnold, N., Fridman, T., Day, R.M., Gorin, A.A. (2008). Invited Keynote Talk: Computing P-Values for Peptide Identifications in Mass Spectrometry. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_10
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DOI: https://doi.org/10.1007/978-3-540-79450-9_10
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