A new method for alignment of LC-MALDI-TOF data

Proteome Science

Proceedings

Habtom W Ressom¹  Lihua Zhang¹  Amrita K Cheema¹  Zhiqun Tang² 

[1] Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA;Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA;Bioinformatics Institute, 30 Biopolis Street, 138672, Matrix, Singapore;

关键词: Support Vector Regression;    Dynamic Time Warping;    Alignment Method;    Quality Threshold;    Support Vector Regression Model;   

DOI  :  10.1186/1477-5956-9-S1-S10

来源: Springer

【 摘 要 】

BackgroundIn proteomics studies, liquid chromatography coupled to mass spectrometry (LC-MS) has proven to be a powerful technology to investigate differential expression of proteins/peptides that are characterized by their peak intensities, mass-to-charge ratio (m/z), and retention time (RT). The variable complexity of peptide mixtures and occasional drifts lead to substantial variations in m/z and RT dimensions. Thus, label-free differential protein expression studies by LC-MS technology require alignment with respect to both RT and m/z to ensure that same proteins/peptides are compared from multiple runs.MethodsIn this study, we propose a new strategy to align LC-MALDI-TOF data by combining quality threshold cluster analysis and support vector regression. Our method performs alignment on the basis of measurements in three dimensions (RT, m/z, intensity).Results and conclusionsWe demonstrate the suitability of our proposed method for alignment of LC-MALDI-TOF data through a previously published spike-in dataset and a new in-house generated spike-in dataset. A comparison of our method with other methods that utilize only RT and m/z dimensions reveals that the use of intensity measurements enhances alignment performance.

【 授权许可】

Unknown   
© Tang et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.