Skip Navigation


Bioinformatics Advance Access originally published online on April 13, 2006
Bioinformatics 2006 22(13):1569-1576; doi:10.1093/bioinformatics/btl144
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/13/1569    most recent
btl144v1
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 (2)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Bhasi, K.
Right arrow Articles by Ramanathan, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bhasi, K.
Right arrow Articles by Ramanathan, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

VizStruct for visualization of genome-wide SNP analyses

Kavitha Bhasi 1, Li Zhang 3, Daniel Brazeau 1, Aidong Zhang 2 and Murali Ramanathan 1,*

1 Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA
2 Deparment of Computer Science and Engineering, State University of New York Buffalo, NY 14260, USA
3 Department of Computer Science, Eastern Michigan University Ypsilanti, MI 48197, USA

*To whom correspondence should be addressed.

Motivation: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets.

Results: VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback–Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype.

Availability: Excel and MATLAB code are available at http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/SNP/

Contact: murali{at}Buffalo.edu

Received on March 2, 2006; revised on April 9, 2006; accepted on April 10, 2006

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




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.