Clustering of MS/MS spectra for glycan biomarker discovery Anoop Mayampurath, Chuan-Yih Yu.

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Clustering of MS/MS spectra for glycan biomarker discovery Anoop Mayampurath, Chuan-Yih Yu

Motivation Currently, glycomic profiles only use MS data, which give us only the general view of glycan chain in terms of abundance.

Analysis of MALDI-TOF Mass Spectrometry Data for Discovery of Peptide and Glycan Biomarkers of Hepatocellular Carcinoma This paper introduces computational methods for quantitative comparison of peptides and glycans in serum for biomarker discovery using MS data only. J. Proteome Res., 2008, 7 (2), pp 603–610 Publication Date (Web): January 12, 2008 (Article) DOI: /pr HCC: Hepatocellular Carcinoma ( Cancer of liver) CLD: Chronic liver disease

High Energy CID TOF/TOF has high-energy dissociation that causes cross-ring dissociation. Data will contain X, and A series ions. We will be able gather glycan linkage information from this data.

Our goal We want to enhance this method by extending the analysis to tandem MS data which give us structural glycan information. The goal is find out whether the linkage of glycans will be the causes of disease or not. Furthermore, we world like to build a reliable protocol incorporate this kind of data.

Concept Pair-wise correlation Find the differences of two spectra Health spectra (H 1, H 2, H 3 …H k ) Disease spectra (D 1, D 2, D 3 …D k ) Remove the least significant component. Repeat until all the score above threshold.

Flowchart

Dataset We contacted to the author to ask for the data set. If we don’t have it in time, we will create our simulated date.

Take two sample MS/MS spectra. Do pair-wise correlation/covariance between all MS/MS spectra. Perform PCA to identify spectra with significant differences.