SSRome: an integrated database and pipelines for exploring microsatellites in all organisms

Abstract

Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and non-genic levels in order to identify unique motifs for each class or (iii) missing SSR marker development. In this study, we have developed 'SSRome' as a web-based, user-friendly, comprehensive and dynamic database with pipelines for exploring microsatellites in 6533 organisms. In the SSRome database, 158 million microsatellite motifs are identified across all taxa, in addition to all the mitochondrial and chloroplast genomes and expressed sequence tags available from NCBI. Moreover, 45.1 million microsatellite markers were developed and classified as genic or non-genic. All the stored motif and marker datasets can be downloaded freely. In addition, SSRome provides three user-friendly tools to identify, classify and compare motifs on either a genome- or transcriptome-wide scale. With the implementation of PHP, HTML and JavaScript, users can upload their data for analysis via a user-friendly GUI. SSRome represents a powerful database and mega-tool that will assist researchers in developing and dissecting microsatellite markers on a high-throughput scale.

Figure 1.

The workflow of SSRome database development: Genomic data analysis and Transcriptomic data analysis pipelines.

Figure 2.

Screenshots of the SSRome database (A) SSRome homepage; (B) (1) SSRome search pages and (2) example of search results; (C) Download page; (D) Comparisons page and (E) SSRome Tools pages (1) SSRome Genomic Pipeline page, (2) SSRome Transcriptomic Pipeline page and (3) SSRome Comparative Analysis Pipeline page.