Streamlining of Simple Sequence Repeat Data Mining Methodologies and Pipelines for Crop Scanning

Abstract

Genetic markers are powerful tools for understanding genetic diversity and the molecular basis of traits, ushering in a new era of molecular breeding in crops. Over the past 50 years, DNA markers have rapidly changed, moving from hybridization-based and second-generation-based to sequence-based markers. Simple sequence repeats (SSRs) are the ideal markers in plant breeding, and they have numerous desirable properties, including their repeatability, codominance, multi-allelic nature, and locus specificity. They can be generated from any species, which requires prior sequence knowledge. SSRs may serve as evolutionary tuning knobs, allowing for rapid identification and adaptation to new circumstances. The evaluations published thus far have mostly ignored SSR polymorphism and gene evolution due to a lack of data regarding the precise placements of SSRs on chromosomes. However, NGS technologies have made it possible to produce high-throughput SSRs for any species using massive volumes of genomic sequence data that can be generated fast and at a minimal cost. Though SNP markers are gradually replacing the erstwhile DNA marker systems, SSRs remain the markers of choice in orphan crops due to the lack of genomic resources at the reference level and their adaptability to resource-limited labor. Several bioinformatic approaches and tools have evolved to handle genomic sequences to identify SSRs and generate primers for genotyping applications in plant breeding projects. This paper includes the currently available methodologies for producing SSR markers, genomic resource databases, and computational tools/pipelines for SSR data mining and primer generation. This review aims to provide a ‘one-stop shop’ of information to help each new user carefully select tools for identifying and utilizing SSRs in genetic research and breeding programs.