Rapid microsatellite identification from Illumina paired-end genomic sequencing in two birds and a snake.

Abstract

Identification of microsatellites, or simple sequence repeats (SSRs), can be a time-consuming and costly investment requiring enrichment, cloning, and sequencing of candidate loci. Recently, however, high throughput sequencing (with or without prior enrichment for specific SSR loci) has been utilized to identify SSR loci. The direct "Seq-to-SSR" approach has an advantage over enrichment-based strategies in that it does not require a priori selection of particular motifs, or prior knowledge of genomic SSR content. It has been more expensive per SSR locus recovered, however, particularly for genomes with few SSR loci, such as bird genomes. The longer but relatively more expensive 454 reads have been preferred over less expensive Illumina reads. Here, we use Illumina paired-end sequence data to identify potentially amplifiable SSR loci (PALs) from a snake (the Burmese python, Python molurus bivittatus), and directly compare these results to those from 454 data. We also compare the python results to results from Illumina sequencing of two bird genomes (Gunnison Sage-grouse, Centrocercus minimus, and Clark's Nutcracker, Nucifraga columbiana), which have considerably fewer SSRs than the python. We show that direct Illumina Seq-to-SSR can identify and characterize thousands of potentially amplifiable SSR loci for as little as $10 per sample--a fraction of the cost of 454 sequencing. Given that Illumina Seq-to-SSR is effective, inexpensive, and reliable even for species such as birds that have few SSR loci, it seems that there are now few situations for which prior hybridization is justifiable.

Full text

Page 1

Rapid Microsatellite Identification from Illumina Paired-
End Genomic Sequencing in Two Birds and a Snake
Todd A. Castoe1, Alexander W. Poole1, A. P. Jason de Koning1, Kenneth L. Jones1, Diana F. Tomback2,
Sara J. Oyler-McCance3, Jennifer A. Fike3, Stacey L. Lance4, Jeffrey W. Streicher5, Eric N. Smith5, David D.
Pollock1*
1Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America, 2Department of Integrative
Biology, University of Colorado Denver, Denver, Colorado, United States of America, 3United States Geological Survey – Fort Collins Science Center, Fort Collins, Colorado,
United States of America, 4University of Georgia, Savannah River Ecology Laboratory, Aiken, South Carolina, United States of America, 5Department of Biology and
Amphibian and Reptile Diversity Research Center, The University of Texas at Arlington, Arlington, Texas, United States of America
Abstract
Identification of microsatellites, or simple sequence repeats (SSRs), can be a time-consuming and costly investment
requiring enrichment, cloning, and sequencing of candidate loci. Recently, however, high throughput sequencing (with or
without prior enrichment for specific SSR loci) has been utilized to identify SSR loci. The direct ‘‘Seq-to-SSR’’ approach has an
advantage over enrichment-based strategies in that it does not require a priori selection of particular motifs, or prior
knowledge of genomic SSR content. It has been more expensive per SSR locus recovered, however, particularly for genomes
with few SSR loci, such as bird genomes. The longer but relatively more expensive 454 reads have been preferred over less
expensive Illumina reads. Here, we use Illumina paired-end sequence data to identify potentially amplifiable SSR loci (PALs)
from a snake (the Burmese python, Python molurus bivittatus), and directly compare these results to those from 454 data.
We also compare the python results to results from Illumina sequencing of two bird genomes (Gunnison Sage-grouse,
Centrocercus minimus, and Clark’s Nutcracker, Nucifraga columbiana), which have considerably fewer SSRs than the python.
We show that direct Illumina Seq-to-SSR can identify and characterize thousands of potentially amplifiable SSR loci for as
little as $10 per sample – a fraction of the cost of 454 sequencing. Given that Illumina Seq-to-SSR is effective, inexpensive,
and reliable even for species such as birds that have few SSR loci, it seems that there are now few situations for which prior
hybridization is justifiable.
Citation: Castoe TA, Poole AW, de Koning APJ, Jones KL, Tomback DF, et al. (2012) Rapid Microsatellite Identification from Illumina Paired-End Genomic
Sequencing in Two Birds and a Snake. PLoS ONE 7(2): e30953. doi:10.1371/journal.pone.0030953
Editor: Bengt Hansson, Lund University, Sweden
Received August 31, 2011; Accepted December 27, 2011; Published February 14, 2012
Copyright: ? 2012 Castoe et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors acknowledge the support of the National Institutes of Health (R01, GM083127) and University of Colorado setup funds to DDP, and a CRISP
Award (University of Colorado Denver, Denver campus) to DFT. SLL was partially supported by the Department of Energy under Award Number DE-FC09-
07SR22506 to the University of Georgia Research Foundation. The Gunnison Sage-grouse sample was obtained as the result of previous research funded by the
Colorado Division of Wildlife. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: David.Pollock@ucdenver.edu
Introduction
Constant advances in DNA sequencing technology and lower
costs are driving innovation in the life sciences, and are having an
especially large impact on the study of ecology, evolution, and
population genetics. With these advances, traditional approaches
to data generation and marker development require continual re-
evaluation. For example, simple sequence repeats (SSRs; also
known as microsatellite loci) have long been important in
population genetic studies, but the identification of SSRs from
non-model species previously required substantial and costly
technical effort, and often returned far fewer loci than were
required to address most population genetics questions adequate-
ly. This effort included creating libraries enriched for SSR loci,
cloning, hybridization to detect positive clones, plasmid isolation,
and Sanger sequencing. The application of next-generation
sequencing approaches has recently made the cost of obtaining
SSR loci less expensive and more efficient, allowing researchers
to focus technical efforts on obtaining larger sample sizes
appropriate to answer the population genetics questions being
asked.
Several research groups [1,2,3,4], including our own [5], have
developed approaches and software to identify SSR loci from raw
454 sequence reads. These approaches first identify reads
containing SSR loci and then identify flanking sequences
appropriate for PCR primer sites, avoiding sequences that form
secondary structures or are of low complexity. This produces what
we call a ‘‘potentially amplifiable locus’’ (PAL). Some of these
published approaches included an SSR enrichment step, while
others obtained sequence data from an un-selected shotgun
genomic library (defined here as the direct ‘‘Seq-to-SSR’’
approach). The long read lengths afforded by 454 sequencing
were considered central to this approach because they could
identify SSRs and enough flanking sequence on either side for the
design of PCR primers. For many species and studies, the number
of SSR loci obtained from a small amount of sequencing without
enrichment is sufficient [5]. SSRs are rare in the genomes of some
species, however, and the prohibitive cost of sufficient 454
PLoS ONE | www.plosone.org 1 February 2012 | Volume 7 | Issue 2 | e30953

Page 2

sequencing in such cases often necessitates a pre-sequencing SSR
enrichment step.
While the per-base cost of 454 sequencing has stayed relatively
constant, the cost of obtaining Illumina sequence data has dropped
substantially. Illumina sequences now can produce moderately
long reads (up to 150 bp with the GAIIx, and 100 bp with the
HiSeq) and accommodate paired-end sequencing from both ends
of ,200–600 bp fragments. There have also been massive
increases in the number of reads obtained per Illumina sequencing
run. To take advantage of these advances, we implemented and
tested a new approach, analogous to the previous 454-based
method, utilizing Illumina paired-end sequencing to identify PALs
(SSR loci and flanking PCR primer sites) without library
enrichment or post-sequencing assembly of reads.
We first applied this approach to detect SSR loci from the
Burmese python (Python molurus bivittatus), which has been shown to
have a relatively high genomic frequency of SSR loci [6,7]. Using
libraries prepared from the same python individual, we compared
the Illumina results to results using 454 sequencing of an
analogous shotgun genomic library. To further demonstrate the
utility of using Illumina sequencing for SSR identification, we
tested the approach on two bird species (Gunnison Sage-grouse,
Centrocercus minimus, and Clark’s Nutcracker, Nucifraga columbiana).
We specifically chose to test the approach on birds because, among
vertebrates, they have particularly low genomic SSR content [8,9]
and thus can be challenging for shotgun sampling methods. We
identified thousands of SSR loci from all samples, but with orders
of magnitude better economy using the Illumina-based Seq-to-
SSR method.
Materials and Methods
Preparation of shotgun libraries
All tissues used in this study were obtained from collaborators,
and not collected directly by the authors. Liver tissue (snap-frozen
in liquid nitrogen and stored at 280uC) from a captive bred
Burmese python was used as a source for genomic DNA (IACUC
A08.025, University of Texas Arlington). Total DNA was
extracted using standard phenol-chloroform-isoamyl alcohol
organic separation, precipitated with ethanol/sodium acetate,
washed with 70% ethanol, and resuspended in TE buffer. A total
of 10 ug of this DNA was used to make two 454 shotgun libraries,
one with FLX shotgun adapters and a second with FLX-Titanium
adapters, both prepared using the standard shotgun library
preparation protocol and quality control steps (Roche). Data from
these two libraries have been previously published [7], and are
available on NCBIs Sequence Read Archive (SRA029568), and at
www.snakegenomics.org.
An Illumina paired-end (IPE) shotgun library was also prepared
from 5 ug of DNA extracted from the same python individual,
using a previously published protocol [10] involving fragmentation
via nebulization, ‘‘Y’’-adapter ligation, and agarose gel-based size
selection. The resulting paired-end library, including the ligated
adapter sequences, had a mean size of approximately 325 bp. This
library preparation method used only ‘off-the-shelf’ reagents
rather than library preparation kits to reduce the cost to
approximately $20 per library.
In addition to the python, total genomic DNA was obtained
from two bird species. For the Gunnison Sage-grouse, DNA was
extracted from blood obtained from an individual trapped in
Gunnison, Colorado (animal protocols approved and conducted
by the Colorado Division of Wildlife). DNA was isolated using
standard phenol-chloroform-isoamyl alcohol organic separation,
precipitated with ethanol/sodium acetate, washed with 70%
ethanol, and resuspended in TE buffer. For the Clark’s
Nutcracker, DNA was extracted from muscle tissue of an
individual using the Wizard Genomic DNA Purification Kit
(Promega). That individual Nutcracker had been trapped near
Logan, Utah and used for behavioural experiments (IACUC
number 00-006, Northern Arizona University). Illumina paired-
end libraries for both birds were prepared following the same
protocol as for the python, and the resulting paired-end libraries
had a similar mean size of approximately 325 bp.
Sequencing of the python 454 libraries is described elsewhere
[7], but in brief included sequencing on the 454GS platform using
either FLX-LR or FLX-XLR Titanium sequencing reagents.
About half of the ,30 million base-pairs (Mbp) obtained came
from each of these two sequencing kits, and thus the python 454
data are nearly an equal mixture of FLX-LR and FLX-XLR
Titanium sequence reads. Illumina sequencing for the python
library was conducted on a GAIIx platform, and sequenced for
114 bp for each of the two paired-end reads. The two bird
libraries were sequenced on the GAIIx platform with 120 bp
paired-end reads, although the first four nucleotides were
multiplex identifiers that were computationally removed, making
the effective lengths used for analyses 116 bp per read for both
birds.
Identification of SSR loci
A Perl script was written, which we named PAL_FINDER_
v0.02.03, to extract reads that contained perfect dinucleotide
(2mer), trinucleotide (3mer), tetranucleotide (4mer), pentanucleo-
tide (5mer), and hexanucleotide (6mer) tandem SSRs. Reads were
identified as SSRs if they contained simple repeats of at least 12 bp
in length for 2–4mers (e.g., 6 tandem repeats for dinucleotides), and
at least 3 repeats for 5mers or 6mers. The reads were then sorted
by the monomer sequence of the repeat (e.g., TAC or TA repeats)
and by the number of tandemly repeated units observed. Non-
unique repeat motifs (reverse-complement repeat motifs (e.g., TG
and CA) and translated or shifted motifs (e.g., TGG, GTG, and
GGT)) were grouped together, so that there were a total of four
unique 2mer repeats, 10 unique 3mer repeats, and so on. If
multiple SSR loci were discovered in a single read, the locus was
considered a compound repeat if the SSR had different motifs;
they were considered a broken repeat if the SSR had the same
motif. In relatively rare cases in which the same repeated motif
occurred at the internal termini of both paired-end sequences, the
microsatellite was considered to be a spanning read, and
annotated as such.
The program PAL_FINDER_v0.02.03 is operated using a
control file that determines parameter settings. The control file can
be readily modified by the user to alter criteria for SSR
identification. For example, the user can specify which type of
reads (454 vs. Illumina) are to be analyzed, and if the program
should attempt to design primers or simply count SSR loci. The
user can also specify the minimum number of tandem repeats (for
each n-mer size class) to be considered, and which n-mer size
classes to search for (from 2mers to 6mers).
Automated design and characterization of PCR
amplification primers flanking identified SSR loci
A common motivation for identifying new SSRloci is to use them
for scoring allelic length variation. Thus, newly identified SSR loci
are typically useful only if primers in the non-SSR flanking regions
can be designed and used successfully for PCR amplification. We
therefore screened reads with SSR loci for flanking regions with
high-quality PCR priming sites. The primer-pair design process was
automated to submit large batches of sequences to a local
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org2 February 2012 | Volume 7 | Issue 2 | e30953

Page 3

installation of the program Primer3 (version 2.0.0) [11], and was
implemented inthe Perl programPAL_FINDER_v0.02.03, which
is freely available (see below).
For the purpose of selecting primer sites, low complexity and
simple repeat sequences were masked from sequences flanking
SSR loci using the RepBase v14.01 database (the ‘‘simple.txt’’
library) [12]. We used the following criteria for primer design: 1)
GC content greater than 30%; 2) melting temperatures of 58–
65uC with a maximum 2uC difference between paired primers; 3)
the last two 39 nucleotides were G or C (a GC ‘‘clamp’’); 4)
maximum poly-N of four nucleotides. All other parameters were
set to Primer3 default values. If all criteria were met, a single
primer-pair was chosen based on the highest score assigned by
Primer3 [11], and based on finding primers that will amplify the
maximum number of repeats in each read or read pair. The
control file for the PAL_FINDER_v0.02.03 program contains a
large number of parameter settings that direct the primer design
criteria of Primer 3. These include ranges and optimal values for
primer length, melting temperature, and secondary structure. The
user can readily modify these parameter settings in the
PAL_FINDER control file.
A concern in identifying PALs is the copy number of the
primer sequence in the genome. We addressed this by estimating
the number of observed occurrences of identified primer sequen-
ces in the sequence set analyzed. Specifically, PAL_FINDER
uses the raw set of reads as a reference and counts the copy
number of forward and reverse primers in this library. A further
consideration is that while the forward and reverse primer
sequences may have multiple copies in the genome, they can still
produce a single distinct band for scoring SSRs if they occur close
to one another only once or a few times. In other words, even if
primer sequences are somewhat frequent in the genome sample,
they may only rarely occur in close proximity (and thus produce a
PCR product). To evaluate this, we counted how often each PAL
primer pair co-occurred in a set of paired reads in our library of
reads for each species. Thus, PALs can be further screened based
on the copy number of primers and primer pairs, with the lowest
frequencies indicating primers and pairs most likely to amplify a
single locus. All these attributes of PAL primers are annotated for
each locus in the output of PAL_FINDER. These attributes of
primer copy number, together with their sequences, and the
detailed detection of SSRs per locus are output in a combined
tab-delimited. This allows the output of SSR loci with flanking
primers to be sorted and filtered by a number of criteria that
might interest researchers.
Results
Raw data and subsets used for demonstration
The genome size of the Burmese python is unknown, but a
related python species (P. reticulatus) has been estimated to be
1.44 Gbp [13]. The genome sizes for the two birds used in this
study are also unknown, although bird genomes that have been
surveyed average 1.38 Gbp [14]. Thus, for rough comparisons of
SSR loci identification and sequence sampling, the python and the
two birds in this study can be considered to have approximately
similar genome sizes. For the purposes of comparing the success
rates of SSR identification across species using the Illumina-based
Seq-to-SSR method, we chose to use datasets including 5 million
paired-end reads (equivalent to 5 million62 reads; 5 M hereafter)
per species. This number was chosen because it was slightly under
16nucleotide-level coverage (,1.15 Gbp) of the genomes of these
species.
For detailed comparisons of performance of 454 versus
Illumina-based sequencing, we focused analyses on the python,
for which we had both types of data from the same individual. In a
previous study [7], we had collected 28.5 Mbp from 118,973 reads
from shotgun genomic libraries using the 454 platform (available
at www.snakegenomics.org, and NCBI’s Sequence Read Archive,
accession SRA029568), and we use these data to evaluate the
performance of the 454 reads in the Seq-to-SSR approach. For
direct comparisons with the 454 data, we subsampled the python
Illumina data to include the same number of reads as the 454 data:
118,973 paired-end reads. Hereafter, we refer to these 454 and
Illumina paired-end datasets as ‘‘454’’ and ‘‘IPE-119K’’, as in
Table 1.
Comparison of 454 and Illumina-based Seq-to-SSR with
the python
The 454 and IPE-119K python data contained the same
number of reads and similar amounts of sequence (28.5 Mbp and
27.1 Mbp, respectively), and were comparable in their ability to
identify SSR loci and flanking primers (Table 1). Just over 11,000
sequences containing SSR loci were identified in each data set
(454: 11,027, IPE-119k: 11,073), and the total number of SSR loci
identified in each were similar (454: 13,142, IPE-119k: 12,833),
being slightly higher than the number of SSR-containing reads
because some reads contained multiple SSRs. Thus, SSR loci were
identified from between 9.23% (454) and 9.37% (IPE) of all
python reads from both platforms. We identified 5,474 PALs from
the 454 data and 4,129 PALs from the IPE-119k data (Table 1),
Table 1. Summary of microsatellite identification from various python and bird genome sequence sets.
Burmese Python Gunnison Sage-grouseClark’s Nutcracker
Sample set454 IPE-119K IPE-5M IPE-5MIPE-5M
Millions of reads0.1190.1195.0005.0005.000
Megabases of sequence28.5 27.11,140.01,160.01,160.0
Reads containing one or more microsatellites 11,02711,073 470,333228,243179,663
Total individual microsatellite loci13,142 12,833 546,956 247,714195,176
Compound loci1,314 973 41,726 8,756 4,528
Mirosatellite reads per megabase of sequence 386.9 408.6412.9196.8 154.9
Discrete PALs5,4744,129174,370 74,60672,125
Discrete PAL rate0.496 0.3730.3710.327 0.401
doi:10.1371/journal.pone.0030953.t001
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org3February 2012 | Volume 7 | Issue 2 | e30953

Page 4

with about one quarter of each of these sets containing multiple
(compound) SSRs. Thus, fairly similar proportions of PALs from
among the distinct SSR-containing loci were identified in both
data sets (49.6% from the 454 data and 37.3% from the IPE-119K
data), with about 12% greater PAL identification success from the
454 reads that contained SSRs.
To determine whether there were differences between the
techniques based on SSR structure, we further examined SSR
locus and PAL identification by repeat motif monomer size (Fig. 1).
More SSRs and PALs are identified from analysis of IPE reads
(versus 454) for the shortest and longest repeat motifs (2mers and
6mers), whereas the opposite is true of the middle-sized repeats,
such as the 4mers (Fig. 1, Table 2). These differences in
identification success between 454 and IPE reads are highly
significant for each of the SSR n-mer classes (P,0.001, based on
G-tests). Their basis is uncertain, but may be due to differences in
sequencing performance in highly repetitive regions.
Comparison of PAL recovery for the python versus bird
genomes with Illumina Seq-to-SSR
We compared the effectiveness of Illumina Seq-to-SSR in two
birds and the python using five million IPE reads each (henceforth,
we refer to these as IPE-5M data sets). As expected based on
previous information on the relative abundance of SSR loci in bird
and snake genomes [6,7,8,9,15,16,17], we identified about twice as
many SSR loci (Table 1) in the python (546,956) as in the
Gunnison Sage-grouse (247,714) or Clark’s Nutcracker (195,176)
IPE-5M samples. There was some difference among the three
species in the rate of PAL identification, with the highest rate in
the nutcracker (40.1%), an intermediate rate in the python
(37.1%), and the lowest rate in the grouse (32.7%; Table 1).
The three genomes have notably different frequencies of SSRs
of different repeat motif lengths (e.g., 3mers, 4mers). The 4mers are
most frequent in all three genomes, and particularly abundant in
the python (Fig. 2). The python genome also had particularly high
counts of 6mer repeats compared to the two bird genomes (Fig. 2).
As an example of differences in motif composition, we compared
motifs for 4mers, which are desirable for scoring amplified loci
Figure 1. Comparison of identification of microsatellite loci and ‘potentially amplifiable microsatellite loci’ (PAL) using Illumina
long (114 bp) paired-end reads versus 454 reads. Comparison based on the same number of reads for each platform (118,973 reads) sampled
from Burmese python shotgun genomic libraries.
doi:10.1371/journal.pone.0030953.g001
Table 2. Comparison of microsatellite and PAL identification
from Illumia paired-end reads versus 454 reads for the Burmese
Python, broken down by microsatellite repeat motif length.
2mers3mers 4mers 5mers6mers
IPE-119K
Loci2,7782,0205,2701,088 1,677
PAL 1,317 1,0232,256415 830
Percent PAL 47.41%50.64%42.81%38.14%49.49%
454
Loci 2,554 2,476 6,226 1,336550
PAL 835 1,2733,012 633245
Percent PAL32.69% 51.41%48.38%47.38% 44.55%
doi:10.1371/journal.pone.0030953.t002
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org4 February 2012 | Volume 7 | Issue 2 | e30953

Page 5

Figure 2. Comparison of identification of microsatellite loci and ‘potentially amplifiable microsatellite loci’ (PAL) among bird and
python samples. Analysis based on using five million Illumina long (114–116 bp) paired-end reads from a shotgun genomic library for each species.
doi:10.1371/journal.pone.0030953.g002
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org5 February 2012 | Volume 7 | Issue 2 | e30953

Page 6

based on size because they are abundant, generally variable, and
more easily scored than shorter motifs (Fig. 3). Some 4mer motifs
(e.g., AAAC, AAAG, AAAT) have similar frequencies among the
birds and the python, but many others differ substantially in
frequency between the python and birds, and even between the
two bird species (e.g., TTCC, ATCT, ATGG; Fig. 3). These
differences highlight the strength of the Seq-to-SSR approach in
discovery of SSR loci without requiring a priori targeting of
particular motifs, as in enrichment-based approaches, because it
would be difficult predict these within-class differences in motif
frequencies ahead of time.
Copy number of primer sites flanking SSRs
To utilize the identified SSR loci in PCR-based size scoring of
alleles, it is important to determine which loci will successfully
produce PCR amplification products. A major concern is that
primers designed in the flanking regions around these loci only
amplify a single locus. Therefore, as part of our annotation scheme
we applied three overlapping degrees of stringency (criteria) for
filtering PALs, based on how their primer sequences (or their
reverse complements) were repeated in the entire set of reads. The
most stringent criterion was to take the product of the counts of the
forward and reverse primers in all reads, where a product of ‘‘1’’
would mean that both the forward and the reverse primers were
each only observed once in the entire read dataset. (Note that the
designation of ‘‘forward’’ and ‘‘reverse’’ primers here is arbitrary,
depending on the direction in which they happened to be read).
The next stringency level was to take the minimum number of
times either primer in a PAL occurred in the entire set of reads.
With this filter set to ‘‘1’’, for example, at least one of the two PCR
primers chosen should be unique to the SSR locus targeted, and
therefore lead to successful specific amplification. The least
stringent criterion depended on the number of times that a pair
of PAL primers was observed together, in the correct orientation,
in paired reads. This is a direct estimate of how often they might
occur in close enough proximity in the genome to produce
amplifiable PCR products, but is the least stringent criterion
because the amount of sequencing may be insufficient to detect
repeated pairs. It is also possible that primer pairs might be
amplifiable but are further apart than the lengths of the paired-end
library, and are thus not detected.
To decide what the cutoff numbers for each of these stringency
criteria should be, we divided the numbers in each stringency
criterion into classes based on 1, 2, or .2 observations. The
proportions in each category for each stringency criterion were
strikingly similar across the three species (Fig. 4), with the
proportion of filtered PALs for any given stringency/cutoff
combination ranging from about 20% (only one copy of both
primers observed) to about 80% (only one or two copies of the
primer pair observed in the same orientation in paired reads). By
even the most stringent criteria, and for the bird with the fewest
SSRs, there were still 15,269 stringently filtered PALs (i.e., for the
nutcracker with both PAL primers occurring only once). Thus,
filtering potential target PALs based on stringent primer copy
number requirements still results in tens of thousands of high-
quality loci.
Yield of ultra high-quality amplifiable loci (Best PALs)
To further convey the practical return of extremely high quality
SSR loci that might be expected from sampling five million
Illumina reads, we considered data that were selected for having
both long repeat units (4-, 5-, and 6mers, which are more easily
scored) and longer repeat stretches (more than 7 observed repeat
units, which are more likely to be highly variable in population
samples). We refer to this highly selective set of loci as ‘‘Best
PALs’’. We note that while these particular criteria are somewhat
arbitrary, these criteria are readily selectable using the control file
of the program PAL_FINDER_v0.02.03, and thus readily tuned.
We then considered the same three stringency criteria as before
(Figure 4), each with a cutoff of 1. In the python, the numbers of
such PALs returned ranged from ,2,100 for the most stringent
criterion to ,5,800 for the least stringent (Fig. 5). In birds, the
numbers for the same criteria were, respectively, ,100–200 and
,300–450 (Fig. 5). Thus, even though there are far fewer usable
SSRs in birds compared to other vertebrates, the massive read
numbers offered by Illumina Seq-to-SSR still provide sufficient
numbers of loci, even with extremely stringent criteria, for robust
population genetic analyses.
Availability of software and SSR loci identified
Supplementary data are available from the journal’s website,
and at the lead and corresponding authors’ web sites (www.
EvolutionaryGenomics.com and www.snakegenomics.org). The
Perl script (PAL_FINDER_v0.02.03) used to identify and
analyze SSR loci is also freely available at http://sourceforge.
net/projects/palfinder/and at the authors’ web sites. An accessory
script for preparing multiplexed IPE data for input into
PAL_FINDER is available at the authors’ websites. Identified sets
of SSR loci, together with statistics for each locus, and primer
sequences for PALs, are provided online as tab-delimited files for
each of the three species analyzed, based on the full 5 million read
datasets (Datasets S1, S2, S3). For the python, PAL_FINDER
output files based on analysis of the 454 and matched IPE-119
data are provided as supplementary files (Datasets S4, S5).
Discussion
Our results suggest that Illumina paired-end sequencing is
capable of identifying massive numbers of potentially PCR-
amplifiable SSR loci with tremendous economy. We find that on a
read-by-read basis, Illumina paired-end sequences are nearly as
effective as 454 sequence reads for identification of PALs. The
levels of PAL recovery from Illumina sequencing are high enough
that a fraction of a flow cell (lane) is sufficient to identify tens of
thousands of PALs, even in taxa such as birds that have low
genomic SSR densities. Thus, with Illumina sequencing, there
seems to be little justification for performing an intermediate step
of hybridization (targeting specific SSR motifs) prior to sequencing
[18], rather than the direct Seq-to-SSR described here.
Currently, the GAIIx is capable of producing ,30 million reads
per flow cell lane (1/8 of a flow cell), and the HiSeq is capable of
producing ,180 million reads per lane, with read lengths of up to
150 bp (GAIIx) and 100 bp (HiSeq) per read, for approximately
$2500. In contrast, for a similar price the 454 platform would be
expected to deliver approximately 300,000 reads (from a 1/4
70675 mm picotiter plate), or 1006 fewer than the GAIIx.
Although the HiSeq platform offers 6-fold greater economy per
paired read, the GAIIx platform offers longer read capability (to
150, versus 100 bp), and substantially more accurate base calling
at lengths .50 bp. Given that ,$500 of GAIIx sequencing of the
python yielded over a half million identified SSR loci, and
,175,000 PALs, it seems that the cost of the GAIIx is already
sufficiently low that there is no great benefit to using HiSeq and its
somewhat shorter and less accurate reads.
As an example of the extreme economy of the method, the cost
to sequence the IPE-119K dataset would have been ,$10 on the
GAIIx. Thus, for the python, 1/250 of a GAIIx lane yielded 4,129
PALs. Also, our shotgun library preparations utilized all
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org6February 2012 | Volume 7 | Issue 2 | e30953

Page 7

Figure 3. Comparison of microsatellite loci and ‘potentially amplifiable microsatellite loci’ (PAL) identification for 4mer repeat
motifs in birds and the python. Results based on five million Illumina long (114–116 bp) paired-end reads per species.
doi:10.1371/journal.pone.0030953.g003
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org7 February 2012 | Volume 7 | Issue 2 | e30953

Page 8

Figure 4. Empirically estimated copy numbers of identified flanking primer sequences for potentially amplifiable microsatellite loci
(PALS) for each species. The number of times a primer sequence or primer pair was observed in all data (from five million reads per species) was
counted per species to approximate their genomic frequencies. Here the ‘‘Min (Fwd, Rev)’’ represents the minimum copy number of the forward and
reverse primers observed in the data. The product of the independent frequencies of the forward and reverse primer sequences (per locus) is also
shown (‘‘Fwd6Rev Primer’’), as is the frequency that each primer pair was observed together in a set of paired reads (‘‘Primer Pair’’).
doi:10.1371/journal.pone.0030953.g004
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org8 February 2012 | Volume 7 | Issue 2 | e30953

Page 9

independently purchased (‘‘off the shelf’’) reagents, rather than
kits, and cost approximately $20 per sample. Considering that our
results demonstrate similar performance for SSR locus and PAL
identification on a read-by-read basis for 454 and IPE data, and a
two to three order of magnitude difference in cost favouring IPE
sequencing, IPE-based SSR identification by Seq-to-SSR is likely
the preferred approach.
The major benefit of the IPE Seq-to-SSR approach for
evolution, population genetics, and linkage mapping studies is
that it quickly, reliably, and inexpensively delivers an unbiased
genome-wide characterization of SSR loci along with PALs and
their primers. It also produces a rich dataset of randomly sampled
sequences that can be used for other purposes, such as studying
transposable element content, mitochondrial genomes, or other
highly repeated DNA segments. Furthermore, unlike other
methods, the Seq-to-SSR approach provides information on the
possible repetitive nature of potential primers that no other
approach provides. While other groups have used IPE sequencing
to identify SSR loci from genomic libraries after enriching for
SSRs [18], the economy of IPE sequencing argues strongly against
the need for such enrichment. One slight disadvantage of IPE
versus 454 sequencing is, however, that 454 reads will often count
the exact number of SSRs, whereas the exact number of repeats
may be unknown for many IPE loci [18]. This is because the IPE
library insert size is typically larger than the combination of the
two paired read lengths (as in our case), and therefore, SSR loci
may extend into the intervening portion of the insert sequence that
is not covered by either read. It is also notable that the total read
length of both 454 and IPE data limits the measurement of the
total length of SSRs, such that the length of SSRs exceeding the
read length or extending outside the boundaries of reads will be
underestimated. Despite this, loci can still be sorted and targeted
for further work based on the observed number of repeats in the
IPE paired reads, which represents a lower bound on the absolute
number of repeats.
Given the large number of loci identified, the Seq-to-SSR
approach allows great flexibility to preferentially target loci with
favourable characteristics. For example, longer SSRs [19] are
generally known to exhibit greater allelic variability, as are perfect
(versus imperfect or compound) SSRs [20]. Other characteristics
of a locus, such as the copy number of designed flanking primers
and length of targeted amplicon, determined by the Seq-to-SSR
approach, provide further information for choosing loci most likely
to amplify successfully.
Figure 5. Highly stringent selection of choice microsatellite targets. Microsatellite loci were selected from the 5 million read datasets for
each species that fit specific criteria for repeat monomer length (4–6), number of repeats observed (.7), and primer copy number in the observed
data (variations shown above).
doi:10.1371/journal.pone.0030953.g005
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org9 February 2012 | Volume 7 | Issue 2 | e30953

Page 10

Here, we provided one example of extremely stringent filtering
of PALs to identify a set of ‘‘Best PALs’’ that have many of the
above mentioned characteristics, as well as having longer repeat
motifs (4–6mers). These strict criteria yielded hundreds of Best
PALs in the birds and thousands in the python. These and many
other features of loci are either adjustable parameters in our
program PAL_FINDER, or are part of the output annotation for
each locus and can thus be used to sort and filter sets of SSRs. To
empirically demonstrate the effectiveness of the approach, we
applied these same ‘‘Best PALs’’ filters to similar IPE data from a
plant (Mimulus ringens), and empirically tested 48 highly stringent
primer sets on four individuals. In the first attempt, 22 of these loci
produced clearly distinguishable amplification products, and 21
were polymorphic. Another 9 were probably good polymorphic
loci but require further PCR optimization. Thus, by sampling
microsatellite loci on essentially a genome-scale, the IPE Seq-to-
SSR approach provides excellent flexibility for researchers to
choose microsatellite loci with a suite of favourable characteristics
for their needs.
Our approach and software for SSR loci primer identification
should also be useful to rapidly characterize genomic SSR
landscapes for comparative purposes. We used the earlier 454-
specific version of this software to identify differences in the
genomic SSR content among species of snakes. This led to the
discovery that these differences were due to SSR-seeding by a
particular family of transposable elements [7]. In the current
study, our analysis showcases the major differences between bird
and snake SSR content, and newly demonstrates substantial
differences in SSR content between the two bird genomes (e.g.,
Fig. 3).
Supporting Information
Dataset S1
mese python, Python molurus bivittatus (5 million read
IPE dataset). This is a tab-delimited text file that can be readily
imported into a spreadsheet.
(TXT)
Potentially amplifiable loci output - Bur-
Dataset S2
son Sage-grouse, Centrocercus minimus (5 million read
IPE dataset). This is a tab-delimited text file that can be readily
imported into a spreadsheet.
(TXT)
Potentially amplifiable loci output - Gunni-
Dataset S3
Nutcracker, Nucifraga columbiana (5 million read IPE
dataset). This is a tab-delimited text file that can be readily
imported into a spreadsheet.
(TXT)
Potentially amplifiable loci output - Clark’s
Dataset S4
mese python, Python molurus bivittatus (119k IPE
dataset). This is a tab-delimited text file that can be readily
imported into a spreadsheet.
(TXT)
Potentially amplifiable loci output - Bur-
Dataset S5
mese python, Python molurus bivittatus (454 dataset).
This is a tab-delimited text file that can be readily imported into a
spreadsheet.
(TXT)
Potentially amplifiable loci output - Bur-
Acknowledgments
We thank C. Franklin (UTA) for the python specimen used for this study,
and Alan Kamil (University of Nebraska), Russel Balda, and Russell
Benford (Northern Arizona University) for the Clark’s Nutcracker
specimen. The use of trade, product, or firm names in this publication is
for descriptive purposes only and does not imply endorsement by the U.S.
Government.
Author Contributions
Conceived and designed the experiments: TAC AWP DDP. Performed the
experiments: TAC AWP APJdK KLJ DFT SJOM JAF SLL JWS ENS
DDP. Analyzed the data: TAC AWP KLJ. Contributed reagents/
materials/analysis tools: TAC AWP SLL SJOM DDP. Wrote the paper:
TAC DDP.
References
1. Abdelkrim J, Robertson B, Stanton JA, Gemmell N (2009) Fast, cost-effective
development of species-specific microsatellite markers by genomic sequencing.
Biotechniques 46: 185–192.
2. Allentoft M, Schuster SC, Holdaway R, Hale M, McLay E, et al. (2009)
Identification of microsatellites from an extinct moa species using high-
throughput (454) sequence data. Biotechniques 46: 195–200.
3. Meglecz E, Costedoat C, Dubut V, Gilles A, Malausa T, et al. (2010) QDD: a
user-friendly program to select microsatellite markers and design primers from
large sequencing projects. Bioinformatics 26: 403–404.
4. Faircloth BC (2008) msatcommander: detection of microsatellite repeat arrays
and automated, locus-specific primer design. Mol Ecol Resour 8: 92–94.
5. Castoe TA, Poole AW, Gu W, de Koning APJ, Daza JM, et al. (2010) Rapid
identification of thousands of copperhead snake (Agkistrodon contortrix) microsat-
ellite loci from modest amounts of 454 shotgun genome sequence. Mol Ecol
Resour 10: 341–347.
6. Castoe TA, Bronikowski AM, Brodie ED, III, Edwards SV, Pfrender ME, et al.
(2011) A proposal to sequence the genome of a garter snake (Thamnophis sirtalis).
Stand Genomic Sci 4: 257–270.
7. Castoe TA, Hall KT, Guibotsy Mboulas ML, Gu W, de Koning APJ, et al.
(2011) Discovery of highly divergent repeat landscapes in snake genomes using
high throughput sequencing. Genome Biol Evol 3: 641–653.
8. International Chicken Genome Sequencing Consortium (2004) Sequence and
comparative analysis of the chicken genome provide unique perspectives on
vertebrate evolution. Nature 432: 695–716.
9. Warren WC, Clayton DF, Ellegren H, Arnold AP, Hillier LW, et al. (2010) The
genome of a songbird. Nature 464: 757–762.
10. Gibbons JG, Janson EM, Hittinger CT, Johnston M, Abbot P, et al. (2009)
Benchmarking next-generation transcriptome sequencing for functional and
evolutionary genomics. Mol Biol Evol 26: 2731–2744.
11. Rozen S, Skaletsky H (2000) Primer3 on the World Wide Web for general users
and for biologist programmers. In: Krawetz S, Misener S, eds. Bioinformatics
Methods and Protocols: Methods in Molecular Biology. Totowa, New Jersey:
Humana Press. pp 365–386.
12. Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, et al. (2005)
Repbase Update, a database of eukaryotic repetitive elements. Cytogenet
Genome Res 110: 462–467.
13. De Smet WHO (1981) The nuclear Feulgen-DNA content of the vertebrates
(especially reptiles), as measured by fluorescence cytophotometry, with notes on
the cell and chromosome size. Acta Zoologica et Pathologica Antverpiensia 76:
119–167.
14. Gregory TR, Nicol JA, Tamm H, Kullman B, Kullman K, et al. (2007)
Eukaryotic genome size databases. Nucleic Acids Res 35: D332–338.
15. Castoe TA, de Koning APJ, Hall KT, Yokoyama KD, Gu W, et al. (2011)
Sequencing the genome of the Burmese python (Python molurus bivittatus) as a
model for studying extreme adaptations in snakes. Genome Biol 12: 406.
16. Hughes AL, Piontkivska H (2005) DNA repeat arrays in chicken and human
genomes and the adaptive evolution of avian genome size. BMC Evol Biol 5: 12.
17. Shedlock AM, Botka CW, Zhao S, Shetty J, Zhang T, et al. (2007)
Phylogenomics of nonavian reptiles and the structure of the ancestral amniote
genome. Proc Natl Acad Sci U S A 104: 2767–2772.
18. Jennings TN, Knaus BJ, Mullins TD, Haig SM, Cronn RC (2011) Multiplexed
microsatellite recovery using massively parallel sequencing. Mol Ecol Resour.
19. Kelkar YD, Tyekucheva S, Chiaromonte F, Makova KD (2008) The genome-
wide determinants of human and chimpanzee microsatellite evolution. Genome
Res 18: 30–38.
20. Buschiazzo E, Gemmell NJ (2006) The rise, fall and renaissance of
microsatellites in eukaryotic genomes. Bioessays 28: 1040–1050.
Microsatellite Identification from Illumina Reads
PLoS ONE | www.plosone.org10 February 2012 | Volume 7 | Issue 2 | e30953