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Building a standard operating procedure for the analysis of mass spectrometry data
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Mass spectrometry (MS) is used in peptidomics to find novel endogenous peptides that may lead to the discovery of new biomarkers. Identifying endogenous peptides from MS is a time-consuming and challenging task; storing identified peptides in a database and comparing them against unknown peptides from other MS runs avoids re-doing identification. MS produce large amounts of data, making interpretation difficult. A platform for helping the identification of endogenous peptides was developed in this project, including a library application for storing peptide data. Machine learning methods were also used to try to find patterns in peptide abundance that could be correlated to a specific sample or treatment type, which can help focus the identification work on peptides of high interest.

Place, publisher, year, edition, pages
2012. , 68 p.
UPTEC X, 12 021
Keyword [en]
mass spectrometry, machine learning, peptidomics
Keyword [sv]
masspectrometri, maskinlärning, peptidomics
National Category
Bioinformatics and Systems Biology
URN: urn:nbn:se:uu:diva-182149OAI: diva2:558583
Educational program
Molecular Biotechnology Engineering Programme
Available from: 2012-10-04 Created: 2012-10-04 Last updated: 2012-10-04Bibliographically approved

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Bioinformatics and Systems Biology

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