Optimizing selection of microsatellite loci from 454 pyrosequencing via post-sequencing bioinformatic analyses

The comparatively low cost of massive parallel sequencing technology, also known as next-generation sequencing (NGS), has transformed the isolation of microsatellite loci. The most common NGS approach consists of obtaining large amounts of sequence data from genomic DNA or enriched microsatellite libraries, which is then mined for the discovery of microsatellite repeats using bioinformatics analyses. Here, we describe a bioinformatics approach to isolate microsatellite loci, starting from the raw sequence data through a subset of microsatellite primer pairs. The primary difference to previously published approaches includes analyses to select the most accurate sequence data and to eliminate repetitive elements prior to the design of primers. These analyses aim to minimize the testing of primer pairs by identifying the most promising microsatellite loci.