Introduction

Microsatellites, or simple sequence repeats (SSRs), are the genetic marker of choice for numerous applications in forensics, ecology, and evolution [1]. In particular their high variability and abundance across genomes make them ideal for studies of kinship, parentage, individual identification, population genetics, and linkage mapping (reviewed in [2]). In recent years, technological advances have brought other genetic markers into favor. For example, single nucleotide polymorphisms (SNPs) have gained favor for linkage studies [3], are increasingly being used in wildlife forensics [4], and with the development and improvement [5] of restriction-site associated DNA (RAD) tag sequencing approaches for SNP assays are likely to be increasingly used in population genetics studies (e.g., [6,7]). However, SSRs remain integral as is evidenced by examining a recent issue (vol 22 issue 4) of Molecular Ecology in which over 50% of the original articles relied on microsatellite analysis. In addition, new SSR loci are still being continually developed (e.g., 58 papers describing new SSR loci in Conservation Genetics Resources vol 4 no 4 December 2012).

Although SSR loci remain the genetic marker of choice, their development is still considered to be expensive and labor intensive. For many years, SSR development involved creating libraries enriched for repeat motifs, cloning the library, and using traditional Sanger sequencing to identify clones with inserts positive for SSRs. With the advent of next-generation sequencing technologies, methods for development and characterization of SSRs have improved dramatically. Most notably, researchers began using the Roche 454 sequencing platform to sequence SSR-enriched libraries [8]. Since then, our lab has used the enrichment and 454 sequencing methods in combination across a broad range of taxa including vertebrates [9-12], invertebrates [13-15], and plants [16,17]. While the two methods in tandem have worked well, the enrichment process is nonetheless time consuming, limits the search to selected motifs, can require high concentrations of DNA as starting material. In some species can result in inadvertent enrichment for transposable elements, which have similar motifs to SSRs [18]. It is possible to avoid inadvertent enrichment by employing shotgun sequencing on the 454 platform [19,20]; however, for species with large genomes or infrequent SSRs the cost can be prohibitive. Recently, a more cost effective and efficient method for SSR development using Illumina sequencing has been described [21]. Still, even with the technological advances of next-generation sequencing, the most common method for SSR detection still involves cloning and Sanger sequencing. In the SSR development papers in the issue of Conservation Genetics Resources mentioned above, the authors used Sanger sequencing in 52%, 454 sequencing (1/3 with enriched libraries) in 36%, and Illumina sequencing in only one article.

In recent years, advances in Illumina sequencing have substantially increased the number of reads obtained. In addition, the cost of Illumina sequencing has decreased while the cost of 454 sequencing has remained stable. As a result, it is now cost efficient to use a shotgun sequencing approach with Illumina paired-end sequencing (IPE) 100 bp (HiSeq) or 150 bp (GAIIx) to identify SSRs [21]. Castoe et al. [21] demonstrate that for one species, the Burmese python, shotgun sequencing via IPE and 454 yielded similar results and that IPE reads worked well for two species of birds, even though birds have relatively low frequency of SSR loci [22]. Though Castoe et al. thoroughly describe the SSR data from the IPE reads, they did not validate the primers designed for the three species. The method described by Castoe et al. is highly promising; however, there are two major concerns for the IPE method. First, that the short reads may not allow for sufficient flanking sequence to design primers. Second, that when primers are designed there is no estimate of amplicon length because the two sequences from the paired-end read may not overlap, and thus numerous loci may be either too short or long for classical fragment analysis. Given the apparent hesitancy of researchers to switch to next-generation sequencing for SSR development, we sought to assess and validate the IPE method for a variety of taxa. Our objectives include 1) comparing two different IPE shotgun library preparation protocols (one that requires 1 µg of DNA and one that only requires 10 ng), 2) using the IPE approach across a broad range of taxa to assess the number of reads returned positive for SSRs, the number of positive reads suitable for primer design, and the types of SSRs identified, and 3) to validate that primers designed via IPE will produce quality SSR loci for genotyping purposes.

Methods

Results and Discussion

To determine the overall efficiency of the method, we sequenced IPE libraries for 32 species across a wide taxonomic range (table 1; NCBI BioProject PRJNA209850). Overall the IPE method worked extremely well and we identified 1000s of SSRs for all species (mean = 128,485) with the fewest (2,541) found in a bird species and the highest (644,886) in a crab (table 2). Due to the relatively short read length of the IPE method as compared with Sanger sequencing or 454, the ability to identify suitable primer sites was a concern. However, enough suitable flanking sequence was available for primer design in 17% of the reads with SSRs yielding on average 19,072 potentially amplifiable loci (PALs, sensu [21]). Though 17% is not a large value, given the vast amount of data produced, the process results in ample PALs. The library preparation method did not impact either the number of microsatellites (F=0.07, p = 0.79) or the number of PALs identified (F= 0.05, p = 0.8176). Though the Nextera method is more expensive it allows for using the IPE method even when only 10 ng of DNA is available. The ability to use very small quantities of DNA can be very important for species in which only non-invasive samples can be used or DNA is difficult to extract.

We further filtered the PALs to identify those for which both the forward and reverse primer sequences were found only one time throughout the 5 million reads. These loci are deemed the loci with the best potential for clean amplification and are considered the Premium PALs (hereafter referred to as pPALs). One problem with older enrichment methods is the inadvertent selection of SSRs associated with transposable elements [18]. It is well described that for some taxa SSRs often occur in repetitive elements. When primers are designed for these SSRs, they often amplify multiple loci and accurately scoring such loci can be challenging or impossible. With PAL_FINDER_v0.02.03, it is possible to partially avoid these loci. By only working with loci that qualify as pPALs, it is less likely the primers will amplify multiple loci. Even using the stringent criteria for pPALs, we found over 100 loci for each species, over 500 for 27 species, and over 1000 for 19 species. Overall, ~25% of all PALs qualify as pPALs.

Given the range of species included, we examined for effects of taxonomy on SSR development. There was no effect of kingdom or phylum on the number of SSRs, PALs, or pPALs found; however, class significantly affected all three categories (table 3). Across classes, the number of SSRs was lowest in the Amphibia and highest in Malacostraca. The number of PALs found was lowest in Aves and again highest in Malacostraca. However, for both measures there is ample variation across species within a class, as can be seen by the standard deviations (Figure 1a,b). The frequency of pPALs also ranged widely across taxa (mean = 5,607; range 136 - 52,682; table 4; Figure 1c). In working with PALs, the most important information is the proportion of PALs that are pPALs. Both phylum and class significantly affected this proportion (table 3), where the lowest proportion occurs in insects and the highest in mammals (Figure 1d). To further illustrate this point, we chose just one of the primer sequences (forward) and examined its copy number in the entire dataset. In some cases, the copy numbers of sequences is greater than 100,000 and frequently greater than 10,000 (Figure 2). In Eurycea, numerous primer sequences had copy numbers in excess of 900,000. Across taxa, the distribution of copy numbers is quite different. In 3 of 4 mammalian taxa tested, the copy number of most PALs is one and rarely exceeded 10 (Figure 2a). Contrast this with insects and plants within the class Magnoliopsida that have relatively high PAL copy numbers (Figure 2b and 2c). The benefit of using the IPE method in conjunction with PAL_FINDER v0.02.03 is the ability to identify and avoid these loci when desired.

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Figure 1. The mean and 95% upper confidence limit (values in parentheses are high values that go off the scale) for the number of SSR’s (a), PALs (b), pPALs (c), and percent of PALs that were pPALs that were observed across classes.

https://doi.org/10.1371/journal.pone.0081853.g001

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Figure 2. Frequency histograms of forward primer sequence copy number within 5 million paired end reads.

The proportion of all primers observed 1, 2-10, 11-100, 101-1000, 1001-10,000, 10,001 – 100,000 or > 100,000 times is shown for Mammallia (a), Insecta (b), and Magnoliopsida (c).

https://doi.org/10.1371/journal.pone.0081853.g002

Interestingly, the types of SSRs found also varied across taxa. There was a significant effect of kingdom and phylum on the proportion of PALs and pPALs that were tetranucleotides, with fewer found in plants than animals (table 3). Class affected the proportion of most repeat types seen (table 3). As expected, dinucleotide repeats were overall the most common and accounted for > 50% of the SSRs for most species and classes (table 2). However when considering pPALs, Aves had relatively fewer dinucleotides and more hexa-, penta-, and tri-nucleotides than any other class. In amphibians, tetra-, tri-, and di-nucleotide repeats occurred at similar frequencies and had relatively more tetranucleotides than other classes. A vast majority of pPALs were dinucleotides in both fish species (83%) and the conifer (84%) species. However, due to the large number of SSRs identified, there are still numerous non-dinucleotide pPALs to work with (651 in Rhinichthys, 1379 in Prosopium, and 469 in Juniperus).

For the 13 species for which we optimized primers, we had clean amplification of a single locus for 61% of the loci when using a single set of pcr conditions and cycling parameters (table 5). Success varied across major groups with ~49%, 60%, and 67% amplifying in invertebrates, vertebrates, and plants respectively, with many other loci showing promise with additional optimization. One perceived problem with the IPE method is that once primers are designed the resulting amplicon size cannot be predicted. As we always designed primers in separate reads of the pair (i.e., forward primer in the forward read, and the reverse primer in the reverse read), and it was rarely the case that the paired ends overlapped, there was always uncertainty in how much sequence exists between the primers. Our methods only allowed us to visualize products under 550bp, thus it is possible that some primer pairs amplified larger fragments for which we could not detect. In some cases, the resulting product was too small for accurate sizing using our methods. This was a particular problem with the bivalve. However, we have ascertained that when the repetitive sequence was found in both of the paired reads the resulting amplicon is often very small, likely due to an overly short insert. After working with the bivalve, we began only ordering primers for loci in which the SSR was found in one direction only. This approach has eliminated short inserts, and subsequently short amplicons, as a serious problem. Alternatively, doing a strict size selection before sequencing could also remove these shorter loci. In general, for those species for which additional data on polymorphism and allelic diversity have been collected, a good spread of size ranges between 100 and 500bp have been observed [25-29]. The species that had the lowest success in yielding amplifiable loci was Stictotarsus. Interestingly, it also yielded a low proportion of pPALs, as well as very few tetranucleotide repeats, which in our experience amplify more cleanly. Developing robust SSR loci for Lepidopterans in general has been difficult, primarily due to the flanking sequences across loci being too similar ([30] and references therein). Often only a few loci are generated per species (e.g., [31-34]). In our own experience with earlier methods, we screened 96 primer pairs to obtain five loci [35]. In the current study, we screened 48 primer pairs for Junonia coenia using only a single set of amplification conditions and identified 26 loci that produced strong peaks and did not appear to amplify multiple loci.

Overall, our results demonstrate that Illumina paired-end sequencing identifies large numbers of SSR loci across a wide range of taxa. Additionally, using PAL_FINDER_v0.02.03 to analyze and refine the SSRs selection process, results in a high amplification success rate. In the current study we analyzed 5M reads per species, however, with sufficient resources much more data can be processed and we have now successfully analyzed up to 40M reads allowing for further refinement of PAL selection.

Lastly, as both of our library preparation techniques yielded similar results, this IPE method is ideal even when only a very small amount of genomic DNA is available.

Acknowledgments

We would like to thank all of the following scientists that allowed us to include the data resulting from developing microsatellites for their species of interest: D. Trapnell, J. Karron, R. Mitchell, I. Phillipsen, T. Jenkins, J. Stoutamore, D. Tallmon, P. Berendzen, S. Nerkowski, A. Metcalf, E. Taylor (and his funding from an NSERC Discovery grant and a research award grant), S. Thomas, T. Birtt, P. Rosel, J. Ortega, K. Flores, P. Stapp, B. Horne, J. Chong, K. Roe, J. Castells, M. Angel, S. Fehlberg, J. Beck. Thanks to A. Poole and T. Castoe for modifying their scripts to make PAL_FINDER_v0.02.03 compatible for HiSeq data.