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Genomic DNA sequence databases are a potential and growing resource for simple sequence repeat (SSR) marker development in loblolly pine (Pinus taeda L.). Loblolly pine also has many expressed sequence tags (ESTs) available for microsatellite (SSR) marker development. We compared loblolly pine SSR densities in genome survey sequences (GSSs) to those in non-redundant EST and cDNA sequences (UniGenes) and designed SSR primer pairs from both sequence types. Overall SSR densities were 96 SSR/Mb in GSSs and 38 SSR/Mb in UniGenes. Loblolly pine had the lowest transcriptome SSR density when compared to 49 other species in the NCBI UniGene database. Among the five different GSS genome fractions of loblolly pine analyzed, methylation-filtered DNA had the highest SSR density at 145 SSR/Mb. The most abundant loblolly pine SSR motif was AT in both GSSs (34 SSR/Mb) and UniGenes (7.6 SSR/Mb). Among the trinucleotide SSR motifs, the most abundant was AAT (9.5 SSR/Mb) in GSSs and AGC (4.9 SSR/Mb) in UniGenes. We designed PCR primer pairs for 120 genomic SSRs and 315 EST-SSRs and evaluated PCR amplification for 108 (25%). We identified 21 primer pairs that reliably amplified polymorphic loci from 31 loblolly pine individuals and estimated that at least 60 additional polymorphic marker loci could be developed from available P. taeda GSS and UniGene resources.

Microsatellite markers were developed for conservation genetic studies of Lindera melissifolia (pondberry), a federally endangered shrub of southern bottomland ecosystems. Microsatellite sequences were obtained from DNA libraries that were enriched for the (AC) n simple sequence repeat motif. From 35 clone sequences, 20 primer pairs were designed and evaluated. Eleven primer pairs amplified polymorphic marker loci in pondberry while two did so in Lindera benzoin (spicebush). In 46 samples from a single pondberry site the number of microsatellite alleles ranged from two to 11 per locus with observed heterozygosity values of 0.07–0.91.

Simple sequence repeats (SSRs) have proven to be extremely valuable DNA markers for genetic mapping and population genetic analyses. However, data collected across laboratories or even within laboratories are difficult to combine due to challenges in standardizing allele names, especially for nonmodel systems. Here we provide a new approach for standardizing SSR allele names that combines several previously recognized components for standardization, including reference samples/alleles, cumulative binsets, static between-allele spacing, and interval allele naming.