PSR: polymorphic SSR retrieval.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• High-throughput sequencing enables large-scale microsatellite identification, especially in non-model species.
• Automatic identification of polymorphic microsatellites using sequence redundancy is underexplored.
• Existing genotyping tools struggle with large datasets and SAM/BAM formats, often limited to model organisms.

Purpose of the Study:

• Introduce Polymorphic SSR Retrieval (PSR), a novel tool for detecting microsatellite length polymorphisms.
• Develop an automated solution for identifying polymorphic simple sequence repeats (SSRs) from next-generation sequencing (NGS) data.
• Address the need for efficient microsatellite analysis in non-model species, particularly those with de novo transcriptome assemblies.

Main Methods:

• PSR is a Perl-based tool comprising a read-counting module (PSR_read_retrieval) and a comparative module (PSR_poly_finder).
• Identifies perfect microsatellites and detects heterozygous/homozygous alleles across genotypes.
• Utilizes user-defined thresholds for read overlap and depth to call polymorphisms and minimize false positives.

Main Results:

• PSR successfully identified polymorphic SSRs in de novo assembled plant transcripts and chloroplast genomes.
• Results from PSR were consistent with traditional capillary gel electrophoresis methods.
• The tool effectively handles large-scale NGS data for SSR analysis.

Conclusions:

• PSR automates gene-based and genome-wide polymorphic microsatellite identification from NGS data.
• The tool overcomes limitations of pre-NGS era methods, offering efficiency and scalability.
• PSR is a valuable asset for genomic research in non-model organisms.