SSREnricher: a computational approach for large-scale identification of polymorphic microsatellites based on...

Area of Science:

• Genomics and Bioinformatics
• Molecular Genetics
• Plant and Animal Breeding

Background:

• Microsatellite (SSR) markers are crucial for genetic analysis and molecular breeding in plants and animals.
• Current SSR development methods are time-consuming, expensive, and often yield a low frequency of polymorphic markers.

Purpose of the Study:

• To develop an efficient software tool, SSREnricher, for identifying polymorphic SSR markers.
• To overcome the limitations of existing SSR development techniques, particularly the low frequency of polymorphic SSRs.

Main Methods:

• Developed SSREnricher software with six core procedures: SSR mining, sequence clustering, sequence modification, enrichment of polymorphic SSRs, false-positive removal, and results output with multiple sequence alignment.
• Applied the software to transcriptome sequences for identifying polymorphic SSRs.
• Conducted validation experiments to assess the accuracy and efficiency of the identified markers.

Main Results:

• SSREnricher successfully identified a large number of polymorphic SSRs from transcriptome sequences.
• Validation confirmed that over 90% of markers identified by SSREnricher as putative polymorphic markers were indeed polymorphic.
• The frequency of polymorphic SSRs identified by SSREnricher was significantly higher (P < 0.05) than that achieved by traditional and high-throughput sequencing (HTS) approaches.

Conclusions:

• SSREnricher is an effective tool for the rapid and efficient development of polymorphic SSR markers.
• The software significantly improves the yield of polymorphic SSRs, making genetic analysis and selective breeding more accessible.
• The SSREnricher software package is publicly available on GitHub for broader scientific use.