Robust peak detection and alignment of nanoLC-FT mass spectrometry data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4447 LNCS, Issue , 2007, Pages 35-46

  Codrea, Marius C ^a   Jiménez, Connie R ^b   Piersma, Sander ^b   Heringa, Jaap ^a   Marchiori, Elena ^a  

^a Department of Computer Science

^b VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKERS; CLUSTERING ALGORITHMS; COMPUTATIONAL METHODS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY;

EXPRESSION PROTEOMICS; PEAK ALIGNMENT; POTENTIAL BIOMARKER DETECTION;

PROTEINS;

EID: 38049079787     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71783-6_4     Document Type: Conference Paper

References (20)

1. R. Aebersold and M. Mann. Mass spectrometry-based proteomics. Nature, • 422(6928): 198-207, March 2003.
2. A.H. America, et al. Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional lc-ms. Proteomics, 6(2):641-53, 2006.
3. M. Bellew, et al. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution lc-ms. Bioinformatics, 2006.
4. D. Bylund, et al. Chromatographic alignment by warping and dynamic programming as a pre-processing tool for parafac modelling of liquid chromatography-mass spectrometry data. J. Chromatography, 961:237-244, 2002.
5. S.J. Callister, et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res., 5:277-86, 2006.
6. X. Hu and L. Xu. Investigation on several model selection criteria for determining the number of cluster. Neural Inform. Proces. - Lett. and Reviews, 4(1):1-10, 2004.
7. M. Katajamaa, et al. Mzmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 2006.
8. B.M. Kohli, et al. An alternative sampling algorithm for use in liquid chromatography/tandem mass spectrometry experiments. Rapid Commun. Mass Spectrometry, 19(5):589-596, 2005.
9. E. Lange, et al. High accuracy peak-picking of proteomics data using wavelet techniques. In Proc. Pacific Symposium on Biocomputing (PSB-06), 243-254, 2006.
10. J. Listgarten and A. Emili. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics, 4:419-434, 2005.
11. J. Listgarten, et al. Difference detection in lc-ms data for protein biomarker discovery. Bioinformatics, 2006. in print.
12. J. Listgarten, et al. Multiple alignment of continuous time series. In Advances in Neural Information Processing Systems 2005, (NIPS 2004), 2005.
13. N.-P. Vest Nielsen, et al. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. J. Chromatography, 805(1-2):17-35, 1998.
14. D. Radulovic, et al. Informatics platform for global proteomic profiling and bio-marker discovery using liquid-chromatography-tandem mass spectrometry. Mol. Cell. Proteomics, 3(10):984-97, 2004.
15. C.A. Smith, et al. Xcms: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem., 78:779-787, 2006.
16. R. Tibshirani, et al. Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics, 20(17):3034-44, 2004.
17. P. Wang, et al. A statistical method for chromatographic alignment of lc-ms data. Biostatistics, doi:10.1093/biostatistics/kxl015, 2006.
18. W. Wang, et al. Quantification of proteins and metabolites by mass spectrometry without isotope labeling or spiked standards. Anal. Chem., 75:4818-26, 2003.
19. Y. Yasui, et al. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics, 4(3):449-63, 2003.
20. X. Zhang, et al. Data pre-processing in liquid chromatography-mass spectrometry-based proteomics. Bioinformatics, 21(21):4054-9, 2005.