Skip Navigation



Bioinformatics Advance Access published online on September 8, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti660
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/21/4054    most recent
bti660v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Zhang, X.
Right arrow Articles by Elmagarmid, A. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhang, X.
Right arrow Articles by Elmagarmid, A. K.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 13, 2005
Revised August 1, 2005
Accepted September 1, 2005

Article

Data pre-processing in liquid chromatography-mass spectrometry based proteomics

Xiang Zhang 1*, John M. Asara 2, Jiri Adamec 1, Mourad Ouzzani 3, and Ahmed K. Elmagarmid 3

1 Bindley Bioscience Center, Purdue University, West Lafayette, IN
2 Beth Israel Deaconess Medical Center, Boston, MA
3 Department of Computer Science, Purdue University, West Lafayette, IN

* To whom correspondence should be addressed.
Xiang Zhang, E-mail: zhang100{at}purdue.edu


  Abstract

Motivation: In a liquid chromatography-mass spectrometry (LC-MS) based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment, and data quality assurance.

Results: We have developed an algorithm for spectrum deconvolution. A two-step alignment algorithm is proposed for recognizing peaks generated by the same peptide but detected in different samples. The quality of LC-MS data is evaluated using statistical tests and alignment quality tests.

Availability: Xalign software is available upon request from the author.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BiostatisticsHome page
J. E. Eckel-passow, A. L. Oberg, T. M. Therneau, and H. R. Bergen III
An insight into high-resolution mass-spectrometry data
Biostat., July 1, 2009; 10(3): 481 - 500.
[Abstract] [Full Text] [PDF]

Home page
 Plant Physiol.Home page
M. Cao, A. Koulman, L. J. Johnson, G. A. Lane, and S. Rasmussen
Advanced Data-Mining Strategies for the Analysis of Direct-Infusion Ion Trap Mass Spectrometry Data from the Association of Perennial Ryegrass with Its Endophytic Fungus, Neotyphodium lolii
Plant Physiology, April 1, 2008; 146(4): 1501 - 1514.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
J. W. H. Wong, M. J. Sullivan, and G. Cagney
Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments
Brief Bioinform, March 1, 2008; 9(2): 156 - 165.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.