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32 species validation of a new Illumina paired-end approach for the development of microsatellites

  • Stacey L. Lance ,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • Cara N. Love,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • Schyler O. Nunziata,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • Jason R. O’Bryhim,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • David E. Scott,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • R. Wesley Flynn,

    Affiliation Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina, United States of America

  • Kenneth L. Jones

    Affiliation Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America

32 species validation of a new Illumina paired-end approach for the development of microsatellites

  • Stacey L. Lance, 
  • Cara N. Love, 
  • Schyler O. Nunziata, 
  • Jason R. O’Bryhim, 
  • David E. Scott, 
  • R. Wesley Flynn, 
  • Kenneth L. Jones


Development and optimization of novel species-specific microsatellites, or simple sequence repeats (SSRs) remains an important step for studies in ecology, evolution, and behavior. Numerous approaches exist for identifying new SSRs that vary widely in terms of both time and cost investments. A recent approach of using paired-end Illumina sequence data in conjunction with the bioinformatics pipeline, PAL_FINDER, has the potential to substantially reduce the cost and labor investment while also improving efficiency. However, it does not appear that the approach has been widely adopted, perhaps due to concerns over its broad applicability across taxa. Therefore, to validate the utility of the approach we developed SSRs for 32 species representing 30 families, 25 orders, 11 classes, and six phyla and optimized SSRs for 13 of the species. Overall the IPE method worked extremely well and we identified 1000s of SSRs for all species (mean = 128,485), with 17% of loci being potentially amplifiable loci, and 25% of these met our most stringent criteria designed to that avoid SSRs associated with repetitive elements. Approximately 61% of screened primers yielded strong amplification of a single locus.


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.


Library preparation and sequencing

Within a total of 32 species that comprise a wide taxonomic range (table 1), we used two different methods (16 species each) for creating Illumina paired-end shotgun libraries. The first entailed shearing 1 µg of genomic DNA using a Covaris S220, following the standard protocol of the Illumina TruSeq DNA Library Kit, and using the multiplex identifier adaptor indices. The second method followed the standard protocol of the Nextera™ DNA Sample Prep Kit from Epicentre® that uses only 10 ng of genomic DNA and incorporates Illumina-compatible bar codes. With both methods we pooled 4 - 8 libraries and conducted Illumina sequencing on the HiSeq with 100 bp paired-end reads. We demultiplexed the raw data using Illumina's standard GERALD pipeline. Following demultiplexing, we quality controlled reads for each species to remove bad reads. We wrote a Python QC script (available at to: remove "B-tail" bases (strings of bases with qualities less than Q15 at the end of a read, denoted by the B quality score in Phred-64 data), remove trimmed reads less than 50 bp, and reduce the files to 5M QC-passed paired reads. The resulting reads were analyzed with the program PAL_FINDER_v0.02.03 [21] to extract those reads that contained perfect di-, tri-, tetra-, penta-, and hexanucleotide microsatellites and batch positive reads to a local installation of the program Primer3 (version 2.0.0) for primer design.

Sample NumberKingdomPhylumClassOrderFamilyGenusSpecies
6AnimaliaArthropodaMalacostracaDecapodaOcypodidaeUca mimax
7AnimaliaArthropodaMalacostracaDecapodaOcypodidaeUca spinicarpa

Table 1. Taxonomic information for the 32 species sequenced.

Sample number in bold indicates a Nextera library preparation method was used instead of the standard Illumina preparation.
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Primer Screening

For 12 of the 32 species, we tested forty-eight primer pairs for clean amplification and polymorphism across DNA obtained from eight individuals per species. We performed all PCR amplifications in a 12.5-μL volume (10 mM Tris pH 8.4, 50 mM KCl, 25.0 μg/ml BSA, 0.4 μM unlabeled primer, 0.04 μM tag-labeled primer, 0.36 μM universal dye-labeled primer, 3.0 mM MgCl2, 0.8 mM dNTPs, 0.5 units AmpliTaq Gold® Polymerase (Applied Biosystems), and 20 ng DNA template) using an Applied Biosystems GeneAmp 9700. For all loci, we used a touchdown thermal cycling program [23] encompassing a 10°C span of annealing temperatures ranging between 65-55°C. Touchdown cycling parameters consisted of an initial denaturation step of 5 min at 95°C followed by 20 cycles of 95°C for 30 s, 65°C (decreased 0.5°C per cycle) for 30 s, and 72 °C for 30 s; and 20 cycles of 95 °C for 30 s, 55°C for 30 s, and 72 °C for 30 s; and a final extension at 72°C for 5 m. We ran all PCR products on an ABI-3130xl sequencer and sized with Naurox size standard prepared as described in DeWoody et al. [24], except that unlabeled primers started with GTTT. We used GeneMapper version 3.7 (Applied Biosystems) to analyze alleles.

Data Analysis

We performed all statistical tests using general linear models (GLM; SAS version 9.2, SAS 2009). We first tested the effect of library prep METHOD on the numbers of SSRs and PALs identified; with no difference in prep method detected, we removed METHOD from subsequent models. We tested for taxonomic effects on numbers of SSRs, PALs, and Premium PALs (see below) identified at the kingdom, phylum, and class levels. We calculated the proportions of repeat types (hexa-, penta-, tetra-, tri-, and dinucleotides) out of all SSRs, the proportions out of all PALs, and the proportion of Premium PALs to PALs—proportion data were arcsin-squareroot transformed prior to analyses for taxonomic effects.

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.

Sample NumberGenusNumber of sequences with microsatellites Number of PALs6mers5mers4mers3mers2mers
6Uca 644,886144,5027013,01042,400199,907389,499
7Uca 545,30194,80511413,36040,44988,638402,740

Table 2. The number of paired end reads out of 5 million that contain microsatellites, and within those the number that contain suitable sequence for primers and are considered potentially amplifiable loci (PALs).

Also included are the number of those SSRs that contained hexanucleotide, pentanucleotide, tetranucleotide, trinucleotide, or dinucleotide repeats. Sample number in bold indicates a Nextera library preparation method was used instead of the standard Illumina preparation.
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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.

Kingdom (2)Phylum (7)Class (11)
No. msatsNSNS<0.0001
No. PALsNSNS<0.0001
Premium PALSNSNS0.0003

Table 3. Results of General Linear Model analysis examining role of taxonomy on the number of sequences that had microsatellites (No. msats), the number of PALs, the number of PALs that were different repeat types, the number of premium PALs (pPALs), the number of pPALs that were different repeat types, and the proportion of PALs that were pPALs.

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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.

Sample NumberpPALs6mers5mers4mers3mers2mers

Table 4. Sample number and for each the number of pPALs found and the number that contained hexanucleotide, pentanucleotide, tetranucleotide, trinucleotide, or dinucleotide repeats.

Download CSV
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).

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.

Amplification ResultSpecies Sample Number
Number of loci with good amplification 11242625192329112229401130
Number of loci with good amplification, but were too small (e.g., <100bp)03200156341241
Number of loci that would require further optimization 14121091115316135598
Number of loci that yielded zero amplification 239101418911151010248

Table 5. Forty-eight primers were tested for amplification across 13 species.

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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.


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.

Author Contributions

Conceived and designed the experiments: SLL KLJ. Performed the experiments: SLL KLJ CNL SON JRO RWF. Analyzed the data: SLL KLJ DES CNL SON JRO RWF. Contributed reagents/materials/analysis tools: SLL KLJ. Wrote the manuscript: SLL CNL KLJ DES SON JRO RWF.


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