Multiscale SSR Tag Cloud Visualization for Biomarker Discovery

Abstract

Tag cloud is an efficient visualization technique to help users rapidly understand the significant components within a tag based dataset. To make practical use of the visualization method, we proposed a cross-comparison mechanism for discovering Simple Sequence Repeat (SSR) biomarkers by annotating retrieved tag entries in various colors and font sizes to differentiate conservative status of two species groups. The identified SSR tag entries can represent the conservative and exclusive features among various species with respect to a specified functional gene set. Grouped SSR features of the same kind species can be classified and displayed simultaneously in order to compare evolutionary relationships. In addition, we applied different parameter settings for SSR discovery algorithms to generate multiscale tag clouds which provide different levels of significance of discovered repeat segments. In this study, several significant SSR patterns within eight skeletal development genes from 10 different species were illustrated and classified as notable features for the previously defined two groups of mammalian and fishery species. The proposed multiscale SSR tag clouds were demonstrated to be able to facilitate biologists on searching notable SSR biomarkers or putative gene regulatory elements in this study.