Skip Navigation

Briefings in Bioinformatics 2005 6(1):23-33; doi:10.1093/bib/6.1.23
This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
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 (33)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Lee, C.
Right arrow Articles by Wang, Q.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lee, C.
Right arrow Articles by Wang, Q.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© Henry Stewart Publications

Special Issue Papers

Bioinformatics analysis of alternative splicing

Christopher Lee
An Associate Professor of Chemistry and Biochemistry at the University of California Los Angeles

Qi Wang
A graduate student in the Interdepartmental PhD programme of the Molecular Biology Institute, at the University of California Los Angeles


Christopher Lee, Molecular Biology Institute, Center for Genomics and Proteomics, Dept. of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA 900957—1570, USA Tel: +1 310 825 7374 Fax: +1 310 206 7286 E-mail: leec{at}mbi.ucla.edu

Over the past few years, the analysis of alternative splicing using bioinformatics has emerged as an important new field, and has significantly changed our view of genome function. One exciting front has been the analysis of microarray data to measure alternative splicing genomewide. Pioneering studies of both human and mouse data have produced algorithms for discerning evidence of alternative splicing and clustering genes and samples by their alternative splicing patterns. Moreover, these data indicate the presence of alternative splice forms in up to 80 per cent of human genes. Comparative genomics studies in both mammals and insects have demonstrated that alternative splicing can in some cases be predicted directly from comparisons of genome sequences, based on heightened sequence conservation and exon length. Such studies have also provided new insights into the connection between alternative splicing and a variety of evolutionary processes such as Alu-based exonisation, exon creation and loss. A number of groups have used a combination of bioinformatics, comparative genomics and experimental validation to identify new motifs for splice regulatory factors, analyse the balance of factors that regulate alternative splicing, and propose a new mechanism for regulation based on the interaction of alternative splicing and nonsense-mediated decay. Bioinformatics studies of the functional impact of alternative splicing have revealed a wide range of regulatory mechanisms, from NAGNAG sites that add a single amino acid; to short peptide segments that can play surprisingly complex roles in switching protein conformation and function (as in the Piccolo C2A domain); to events that entirely remove a specific protein interaction domain or membrane anchoring domain. Common to many bioinformatics studies is a new emphasis on graph representations of alternative splicing structures, which have many advantages for analysis.

Keywords: alternative splicing, microarrays, comparative genomics, graph algorithms, regulation


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
RNAHome page
J. Y. Ip, A. Tong, Q. Pan, J. D. Topp, B. J. Blencowe, and K. W. Lynch
Global analysis of alternative splicing during T-cell activation
RNA, April 1, 2007; 13(4): 563 - 572.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
D. Bollina, B. T. K. Lee, T. W. Tan, and S. Ranganathan
ASGS: an alternative splicing graph web service.
Nucleic Acids Res., July 1, 2006; 34(Web Server issue): W444 - W447.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
Y. Xing, T. Yu, Y. N. Wu, M. Roy, J. Kim, and C. Lee
An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs
Nucleic Acids Res., June 6, 2006; 34(10): 3150 - 3160.
[Abstract] [Full Text] [PDF]


Home page
Genome ResHome page
G. Thill, V. Castelli, S. Pallud, M. Salanoubat, P. Wincker, P. de la Grange, D. Auboeuf, V. Schachter, and J. Weissenbach
ASEtrap: A biological method for speeding up the exploration of spliceomes
Genome Res., June 1, 2006; 16(6): 776 - 786.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
R. Agrawal and G. D. Stormo
Using mRNAs lengths to accurately predict the alternatively spliced gene products in Caenorhabditis elegans
Bioinformatics, May 15, 2006; 22(10): 1239 - 1244.
[Abstract] [Full Text] [PDF]


Home page
Brief BioinformHome page
L. Florea
Bioinformatics of alternative splicing and its regulation
Brief Bioinform, March 1, 2006; 7(1): 55 - 69.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
A. Magen and G. Ast
The importance of being divisible by three in alternative splicing
Nucleic Acids Res., September 28, 2005; 33(17): 5574 - 5582.
[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.