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Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

MPG-Autoren
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Junqueira,  Magno
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Waridel,  Patrice
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Surendranath,  Vineeth
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Thomas,  Henrik
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Shevchenko,  Andrej
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Zitation

Junqueira, M., Spirin, V., Balbuena, T. S., Waridel, P., Surendranath, V., Kryukov, G., et al. (2008). Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification. Journal of Proteome Research, 7(8), 3382-3395.


Zitierlink: https://hdl.handle.net/21.11116/0000-0001-0E89-6
Zusammenfassung
Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.