Large-scale identification of polymorphic microsatellites using an in silico approach

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Simple Sequence Repeat (SSR) markers are crucial for genetic research but developing them experimentally is costly and time-consuming.
• In silico methods offer a cost-effective alternative, yet identifying polymorphic SSRs, especially from expressed sequence tags (ESTs), remains challenging.
• Existing software can identify SSRs but lacks the ability to predict their polymorphism, leading to low success rates in marker development.

Purpose of the Study:

• To develop an efficient in silico pipeline, PolySSR, for identifying polymorphic SSR markers.
• To improve the accuracy and success rate of SSR marker development by considering sequence redundancy and heterozygote information in EST databases.
• To create a searchable database of validated polymorphic SSR markers.

Main Methods:

• Developed PolySSR, a pipeline that analyzes EST databases from heterozygous individuals or multiple genotypes to find SSRs.
• Integrated PCR primer design into the pipeline, accounting for Single Nucleotide Polymorphisms (SNPs) in flanking regions to enhance marker reliability.
• Applied the pipeline to EST data from potato, tomato, rice, Arabidopsis, Brassica, and chicken.

Main Results:

• PolySSR successfully identified a large number of polymorphic SSRs across various species.
• The pipeline's primer design, considering SNPs, improved the potential success rate of markers.
• A higher frequency of polymorphic SSRs was observed in shorter SSRs compared to longer ones.
• Validation confirmed that nearly all markers flagged as putatively polymorphic by PolySSR were indeed polymorphic.

Conclusions:

• PolySSR is a highly effective tool for identifying polymorphic SSRs, significantly enhancing marker development efficiency.
• The pipeline facilitates the development of hundreds of putative markers, stored in a searchable database, with high validation rates.
• PolySSR is particularly valuable for species with low polymorphism levels, like tomato, and has broad applicability with new sequencing technologies.