Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 678971, 11 pages
http://dx.doi.org/10.1155/2014/678971
Research Article

Identification of Simple Sequence Repeat Biomarkers through Cross-Species Comparison in a Tag Cloud Representation

1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan
2Graduate Institute of Basic Medical Science, China Medical University, Taichung City 40402, Taiwan
3Department of Computer Science and Information Engineering, Asia University, Taichung City 41354, Taiwan
4Department of Aquaculture, National Taiwan Ocean University, Keelung 20224, Taiwan
5Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan

Received 22 November 2013; Revised 27 February 2014; Accepted 27 February 2014; Published 31 March 2014

Academic Editor: Jose C. Nacher

Copyright © 2014 Jhen-Li Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Simple sequence repeats (SSRs) are not only applied as genetic markers in evolutionary studies but they also play an important role in gene regulatory activities. Efficient identification of conserved and exclusive SSRs through cross-species comparison is helpful for understanding the evolutionary mechanisms and associations between specific gene groups and SSR motifs. In this paper, we developed an online cross-species comparative system and integrated it with a tag cloud visualization technique for identifying potential SSR biomarkers within fourteen frequently used model species. Ultraconserved or exclusive SSRs among cross-species orthologous genes could be effectively retrieved and displayed through a friendly interface design. Four different types of testing cases were applied to demonstrate and verify the retrieved SSR biomarker candidates. Through statistical analysis and enhanced tag cloud representation on defined functional related genes and cross-species clusters, the proposed system can correctly represent the patterns, loci, colors, and sizes of identified SSRs in accordance with gene functions, pattern qualities, and conserved characteristics among species.