Tandem mass spectrometry data quality assessment by self-convolution

Background: Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. Results: The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its timereversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the selfconvolution result of a good spectrum ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Keng Wah Choo, Wai Mun Tham
• Keywords: BMCBI 2007, Mass, MS Data, Tandem Ms Data