Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
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 ISI Web of Science
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 arrow Search for citing articles in:
ISI Web of Science (13)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Lee, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lee, C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 19 no. 8 2003
Pages 999-1008
© 2003 Oxford University Press

Generating consensus sequences from partial order multiple sequence alignment graphs

Christopher Lee 1,2

1 UCLA-DOE Center for Genomics and Proteomics, Molecular Biology Institute
2 Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA

Received on October 3, 2002 ; revised on December 10, 2002 ; accepted on December 20, 2002

Motivation: Consensus sequence generation is important in many kinds of sequence analysis ranging from sequence assembly to profile-based iterative search methods. However, how can a consensus be constructed when its inherent assumption—that the aligned sequences form a single linear consensus—is not true?

Results: Partial Order Alignment (POA) enables construction and analysis of multiple sequence alignments as directed acyclic graphs containing complex branching structure. Here we present a dynamic programming algorithm (heaviest_bundle) for generating multiple consensus sequences from such complex alignments. The number and relationships of these consensus sequences reveals the degree of structural complexity of the source alignment. This is a powerful and general approach for analyzing and visualizing complex alignment structures, and can be applied to any alignment. We illustrate its value for analyzing expressed sequence alignments to detect alternative splicing, reconstruct full length mRNA isoform sequences from EST fragments, and separate paralog mixtures that can cause incorrect SNP predictions.

Availability: The heaviest_bundle source code is available at http://www.bioinformatics.ucla.edu/poa

Contact: leec{at}mbi.ucla.edu

* To whom correspondence should be addressed.


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
 DNA ResHome page
S.-J. Noh, K. Lee, H. Paik, and C.-G. Hur
TISA: Tissue-specific Alternative Splicing in Human and Mouse Genes
DNA Res, January 1, 2006; 13(5): 229 - 243.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
I. M. Wallace, O. Orla, and D. G. Higgins
Evaluation of iterative alignment algorithms for multiple alignment
Bioinformatics, April 15, 2005; 21(8): 1408 - 1414.
[Abstract] [Full Text] [PDF]

Home page
 Genome ResHome page
Y. Xing, A. Resch, and C. Lee
The Multiassembly Problem: Reconstructing Multiple Transcript Isoforms From EST Fragment Mixtures
Genome Res., March 1, 2004; 14(3): 426 - 441.
[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.