Efficient isolation of polymorphic microsatellites from high-throughput sequence data based on number of repeats

Area of Science:

• Genomics
• Marine Biology
• Molecular Ecology

Background:

• Microsatellites are valuable genetic markers for candidate gene studies.
• Developing polymorphic microsatellites can be resource-intensive due to extensive primer testing.

Purpose of the Study:

• To efficiently develop polymorphic microsatellites for the peacock blenny (Salaria pavo) using transcriptome data.
• To compare the effectiveness of different selection strategies for identifying polymorphic microsatellites.

Main Methods:

• Mining a peacock blenny transcriptome assembly (62,038 contigs) for microsatellites.
• In silico evaluation of microsatellite polymorphism.
• Selection of microsatellites based on in silico polymorphism, annotation, or repeat number.
• Experimental validation of selected microsatellites through PCR amplification and polymorphism analysis.

Main Results:

• Identified 4190 microsatellites in 3670 unique unigenes.
• Detected in silico polymorphism in 733 microsatellites.
• Successfully amplified 28 microsatellites in 26 individuals, with all but two found to be polymorphic.
• Microsatellite selection based on repeat number yielded higher allelic richness (8.2±3.85) compared to in silico polymorphism (4.56±2.45).

Conclusions:

• Developed the first set of genetic markers for Salaria pavo.
• Selection strategies based on repeat number are more efficient for obtaining polymorphic microsatellites.
• Combining repeat number with in silico polymorphism prediction enhances marker development efficiency and allelic richness.