# SSR Locator: Tool for Simple Sequence Repeat Discovery Integrated with Primer Design and PCR Simulation

Authors: Luciano Carlos da Maia; Dario Abel Palmieri; Velci Queiroz de Souza; Mauricio Marini Kopp; Fernando Irajá Félix de Carvalho; Antonio Costa de Oliveira
Journal: International Journal of Plant Genomics
 (2008)

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## Abstract

Microsatellites or SSRs (simple sequence repeats) are ubiquitous short tandem duplications occurring in eukaryotic organisms. These sequences are among the best marker technologies applied in plant genetics and breeding. The abundant genomic, BAC, and EST sequences available in databases allow the survey regarding presence and location of SSR loci. Additional information concerning primer sequences is also the target of plant geneticists and breeders. In this paper, we describe a utility that integrates SSR searches, frequency of occurrence of motifs and arrangements, primer design, and PCR simulation against other databases. This simulation allows the performance of global alignments and identity and homology searches between different amplified sequences, that is, amplicons. In order to validate the tool functions, SSR discovery searches were performed in a database containing 28 469 nonredundant rice cDNA sequences.


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## Body

## 1. Introduction

Microsatellites or
SSRs (simple sequence repeats) are
sequences in which one or few bases are tandemly repeated for varying numbers of
times [1]. Variations in SSR regions originate mostly from errors during the replication process, frequently DNA
polymerase slippage, generating insertion or deletion of base pairs, resulting,
respectively, in larger or smaller regions [2, 3]. SSR assessments in the human
genome have shown that many diseases are caused by mutation in these sequences [4].SSRs can be found in different regions of genes, that is, coding sequences, untranslated
sequences (5′-UTR and 3′-UTR), and introns, where the expansions and/or
contractions can lead to gene gain or loss of function [5]. Also, there are
evidences that genomic distribution of SSRs is related to chromatin
organization, recombination, and DNA repair. SSRs are found throughout the
genome, in both protein-coding and noncoding regions. Genome fractions as low
as 0.85% (Arabidopsis thaliana), 0.37%
(Zea mays), 0.21% (Caenorhabtis elegans), 0.30% (Sacharomyces cerevisae) and as high as 3.0%
(Homo sapiens) and 3.21% (Fugu rubripes) have been found. Some
bias for defined genomic locations has also been reported [6, 7]. This class of
markers is broadly applied in genetics and plant breeding, due to its
reproducibility, multiallelic, codominant nature, and genomic abundance. Its use
for integrating genetic maps, physical mapping, and anchoring gives geneticists
and plant breeders a pathway to link genotype and phenotype variations [8].The protocols for
isolating SSR loci for a new species were always very labor-intensive. Currently,
with the accumulation of biological data originating from whole genome sequence
initiatives, the use of bioinformatics tools helps to maximize the
identification of these sequences and consequently, the efficiency in the
number of generated markers [9].The first in silico studies of SSRs were developed
using FASTA [10] and BLAST [11] packages. Later, more specific algorithms, such as SPUTINICK [12],
REPEATMASKER [13], TRF-Tandem Repeat Find [14], TROLL [15], MISA [16] and
SSRIT (Simple Sequence Repeat Tool) [17],
were obtained [9].SSR detection is generally followed by the use of another program for primer design, to be
anchored on flanking sequences. Also, in some applications, a third step using e-PCR
[18] is added, with the goal of
verifying primer redundancy. The sequential use of a number of software is
often called a pipeline. Building such a pipeline can be a very difficult task
for research groups not familiar with programming tools.In the present work, a computing tool with an interface for Windowsusers was developed, called SSR Locator. The
application integrates the following functions: (i) detection and
characterization of SSRs and minisatellite motifs between 1 and 10 base pairs; (ii)
primer design for each locus found;  (iii)
simulation of PCR (polymerase chain reaction), amplifying fragments
with different primer pairs from a given set of fasta files; (iv) global alignment
between amplicons generated by the same primer pair; and (v) estimation of global
alignment scores and identities between amplicons, generating information on
primer specificity and redundancy. The described tool is publicly available at
the sitehttp://www.ufpel.edu.br/~lmaia.faem.


## 2. Material and Methods

### 2.1. Algorithms

The algorithms used for the searches, alignment, and homology estimates are described separately.


### 2.2. SSR Search

The algorithm used
for perfect and imperfect micro-/minisatellite searches was written in Perl and
consists of the generation of a matrix that mixes A(adenine), T(thymine),
C(cytosine), and G(guanine) in all possible composite arrangements between 1
and 10 nucleotides. The script instructions perform readings on fasta files,
searching all possible arrangements in each database sequence.Several instructions in the algorithm used in SSRLocator resemble those from MISA [16]
and SSRIT [17]. However, additional instructions have been inserted in SSRLocator's code. Instead of allowing the overlap of a few nucleotides
when two SSRs are adjacent to each other and one of them is shorter than the
minimum size for a given class as found in MISA and SSRIT, a module written in Delphi language records the data and eliminates such
overlaps.The SSR Locator
software contains windows focused on the selection and configuration of SSR and
minisatellite types (mono- to 10-mers) and a
minimum number of repeats for each one of the selected types. The algorithm
calls a perfect repeat when one locus is present with
adjacent loci at an up or downstream distance higher than 100 bp.The algorithm calls
an imperfect repeat when the same motif is present on both sides of a fragment containing
up to 5 base pairs.The algorithm
identifies a composite locus when two or more adjacent
loci were found at distances between 6 and 100 bp [16].In this study, only “Class I” (≥20 bp) repeats are shown. These repeats
have been described as the most efficient loci for use as molecular markers
[17]. The software SSRLocator was configured to locate a minimum of 20 bp SSRs:
monomers(x20), 2-mers(x10), 3-mers(x7), 4-mers(x5), 5-mers(x4), 6-mers(x4), and
minissatellites: 7-mers(x3), 8-mers(x3), 9-mers(x3), and 10-mers(x3).In order to
validate the efficiency of SSRLocator in finding SSRs and minisatellites, the
same database was analyzed withMISA
and SSRIT, using the same parameters for minimum number of repeats.


### 2.3. Primer Design

An algorithm written in Delphi language performs calls to Primer3 [19], which execute primer designs. These
results are fed to a module that performs Virtual-PCRs and allocates individual
identification, forward and reverse primer sequences, and a sequence fragment
corresponding to the region flanked by the primers (original amplicon) to each
SSR locus. A window allows the selection of Primer3 parameters, such as range
of primer and amplicon sizes, as well as optimum primer size, ranges of melting temperature
(TM) (minimum, maximum, and optimum) and GC content (minimum and optimum). For
primer searches, the software automatically looks for five base pair distances from
both SSR (5′ and 3′)
flanking sites.  In this study, the
following parameters were used: amplicon size
between 100 and 280 bp; minimum, optimum, and maximum annealing temperature
(TM) of 45, 50, and 55, respectively, minimum, optimum, and maximum primer size
of 15, 20, and 25 bp, respectively.


### 2.4. Virtual-PCR

The module used to
simulate a PCR reaction was written in Delphi.
The algorithm consists in reading the file generated by the previous module
(SSR locus, forward and reverse primers, and original amplicon), followed by a
search of sequences containing primer annealing sites. When annealing sites are found for the two primers, the flanked
region and the primer sequences are copied to a new variable called “paralog amplicon.”


### 2.5. Global Alignment

For the global alignment between paralog and original amplicon sequences
and score calculations (match, mismatch, gaps),
a routine was written in Delphi language using
the algorithms of Needleman and Wunsch (1970) [20] and Smith and Waterman
(1981) [21]. Also, in the same module, amplicon identities were calculated
according to Waterman (1994) [22] and Vingron and Waterman (1994) [23].


### 2.6. Implementation

The strategy of creating a two-language
hybrid program was established as a function of: (i) the higher speed achieved
by handling large text files with Perl as compared to Delphi, and (ii) the better
fitness of Perl for generating combinatory strings to be located. The Perl
module was transformed into an executable file, making unnecessary to install Perl libraries during program installing.
The graphic interface built, integrating input and output windows to the
Windows operational system, was obtained using the Suite Turbo Delphi, where a
menu system executes calls for each of the previously described modules.


### 2.7. Sequences for Analysis

A total of 28 469 rice (Oryza sativa ssp. japonica- cv. Nipponbare) nonredundant full length nonredundant
cDNA
sequences, sequenced by The Rice Full-Length cDNA Consortium, mapped on the databases derived from the sequencing of japonica (japonica draft
genome, BAC/PAC clones—IRGSP) and indica (indica draft genome) subspecies [24] were used for the
analyses. These sequences are deposited in NCBI as two groups, the
first comprising accesses from AK058203 to AK074028, and
the second comprising accesses from AK98843 to AK111488. All these sequences
can be also found in KOME (Knowledge-based Oryza Molecular Biological
Encyclopedia).A flow chart representing the different steps performed by the software is shown in Figure1.Figure 1
Flow-chart
showing the functional structure of SSR Locator. (A) Perl script to search SSRs;
(B) text file where information from detected SSRs is stored; (C) module for the
statistical calculations for SSR motif occurrence; (D) module that formats text
files into standard Primer3 input files; (E) running of Primer3; (F) module for
running Virtual-PCR (using a second sequence file as a template); (G) module
performing global alignment between homologous amplicons; (H) identity and
alignment score calculations between homologous amplicons; and (I) file
containing SSR, primer, homologous amplicons, identity, and score information.


## 2.1. Algorithms

The algorithms used for the searches, alignment, and homology estimates are described separately.


## 2.2. SSR Search

The algorithm used
for perfect and imperfect micro-/minisatellite searches was written in Perl and
consists of the generation of a matrix that mixes A(adenine), T(thymine),
C(cytosine), and G(guanine) in all possible composite arrangements between 1
and 10 nucleotides. The script instructions perform readings on fasta files,
searching all possible arrangements in each database sequence.Several instructions in the algorithm used in SSRLocator resemble those from MISA [16]
and SSRIT [17]. However, additional instructions have been inserted in SSRLocator's code. Instead of allowing the overlap of a few nucleotides
when two SSRs are adjacent to each other and one of them is shorter than the
minimum size for a given class as found in MISA and SSRIT, a module written in Delphi language records the data and eliminates such
overlaps.The SSR Locator
software contains windows focused on the selection and configuration of SSR and
minisatellite types (mono- to 10-mers) and a
minimum number of repeats for each one of the selected types. The algorithm
calls a perfect repeat when one locus is present with
adjacent loci at an up or downstream distance higher than 100 bp.The algorithm calls
an imperfect repeat when the same motif is present on both sides of a fragment containing
up to 5 base pairs.The algorithm
identifies a composite locus when two or more adjacent
loci were found at distances between 6 and 100 bp [16].In this study, only “Class I” (≥20 bp) repeats are shown. These repeats
have been described as the most efficient loci for use as molecular markers
[17]. The software SSRLocator was configured to locate a minimum of 20 bp SSRs:
monomers(x20), 2-mers(x10), 3-mers(x7), 4-mers(x5), 5-mers(x4), 6-mers(x4), and
minissatellites: 7-mers(x3), 8-mers(x3), 9-mers(x3), and 10-mers(x3).In order to
validate the efficiency of SSRLocator in finding SSRs and minisatellites, the
same database was analyzed withMISA
and SSRIT, using the same parameters for minimum number of repeats.


## 2.3. Primer Design

An algorithm written in Delphi language performs calls to Primer3 [19], which execute primer designs. These
results are fed to a module that performs Virtual-PCRs and allocates individual
identification, forward and reverse primer sequences, and a sequence fragment
corresponding to the region flanked by the primers (original amplicon) to each
SSR locus. A window allows the selection of Primer3 parameters, such as range
of primer and amplicon sizes, as well as optimum primer size, ranges of melting temperature
(TM) (minimum, maximum, and optimum) and GC content (minimum and optimum). For
primer searches, the software automatically looks for five base pair distances from
both SSR (5′ and 3′)
flanking sites.  In this study, the
following parameters were used: amplicon size
between 100 and 280 bp; minimum, optimum, and maximum annealing temperature
(TM) of 45, 50, and 55, respectively, minimum, optimum, and maximum primer size
of 15, 20, and 25 bp, respectively.


## 2.4. Virtual-PCR

The module used to
simulate a PCR reaction was written in Delphi.
The algorithm consists in reading the file generated by the previous module
(SSR locus, forward and reverse primers, and original amplicon), followed by a
search of sequences containing primer annealing sites. When annealing sites are found for the two primers, the flanked
region and the primer sequences are copied to a new variable called “paralog amplicon.”


## 2.5. Global Alignment

For the global alignment between paralog and original amplicon sequences
and score calculations (match, mismatch, gaps),
a routine was written in Delphi language using
the algorithms of Needleman and Wunsch (1970) [20] and Smith and Waterman
(1981) [21]. Also, in the same module, amplicon identities were calculated
according to Waterman (1994) [22] and Vingron and Waterman (1994) [23].


## 2.6. Implementation

The strategy of creating a two-language
hybrid program was established as a function of: (i) the higher speed achieved
by handling large text files with Perl as compared to Delphi, and (ii) the better
fitness of Perl for generating combinatory strings to be located. The Perl
module was transformed into an executable file, making unnecessary to install Perl libraries during program installing.
The graphic interface built, integrating input and output windows to the
Windows operational system, was obtained using the Suite Turbo Delphi, where a
menu system executes calls for each of the previously described modules.


## 2.7. Sequences for Analysis

A total of 28 469 rice (Oryza sativa ssp. japonica- cv. Nipponbare) nonredundant full length nonredundant
cDNA
sequences, sequenced by The Rice Full-Length cDNA Consortium, mapped on the databases derived from the sequencing of japonica (japonica draft
genome, BAC/PAC clones—IRGSP) and indica (indica draft genome) subspecies [24] were used for the
analyses. These sequences are deposited in NCBI as two groups, the
first comprising accesses from AK058203 to AK074028, and
the second comprising accesses from AK98843 to AK111488. All these sequences
can be also found in KOME (Knowledge-based Oryza Molecular Biological
Encyclopedia).A flow chart representing the different steps performed by the software is shown in Figure1.Figure 1
Flow-chart
showing the functional structure of SSR Locator. (A) Perl script to search SSRs;
(B) text file where information from detected SSRs is stored; (C) module for the
statistical calculations for SSR motif occurrence; (D) module that formats text
files into standard Primer3 input files; (E) running of Primer3; (F) module for
running Virtual-PCR (using a second sequence file as a template); (G) module
performing global alignment between homologous amplicons; (H) identity and
alignment score calculations between homologous amplicons; and (I) file
containing SSR, primer, homologous amplicons, identity, and score information.


## 3. Results

### 3.1. Program Validation

A total of 3907 micro- and minisatellites were detected by SSRLocator in the 28 469
analyzed cDNA sequences. The same database searched with MISA and SSRIT presented
3913 and 3917 loci, respectively. The mono-, 4-mer, 6-mer, 7-mer, 8-mer, 9-mer,
and 10-mer repeats were identical for the three programs. In the case of 2-mer repeats,
594 elements were detected by SSRLocator and 596 elements were detected by MISA
and SSRIT. 3-mer repeats were differently scored by SSRLocator (1990) and the
other two (1994) algorithms. For 5-mer repeats, SSRLocator and MISA found the
same number of repeats (426), while SSRIT (430) found a different value.


### 3.2. Overall Distribution of SSR Types

The results obtained with SSRLocator
indicate that out of 28 469 cDNA sequences, 3765 (13.22%) presented one or
more micro-/minisatellite loci. In other studies, microsatellites were found in
the following proportions in ESTs: 3% in arabidopsis  [25], 4% in rosaceae [26], 8.11% in barley [16], 2.9% in sugarcane [27], and values ranging between 6–11% [28] and 1.5–4.7%
[29]  for cereals in general (maize,
barley, rye, sorghum, rice, and wheat).Considering the 3765fl-cDNA
sequences, in 3632 (92.96%) only a single micro-/minisatellitelocus was detected. In 125 sequences, two loci were detected, in seven
sequences three lociandonly one sequence had four loci, adding
up to 3907 occurrences. Among the types analyzed, SSRs (mono to 6-mer repeats)
and minisatellites (7- to 10-mer repeats) comprised 96.98% and 4.12% of
detected loci, respectively.The distribution of occurrences detected by SSRLocator was consisted of 138 monomers,
594 2-mers, 1990 3-mers, 251 4-mers, 426 5-mers, 390 6-mers, 82 7-mers,  6 8-mers, 25 9-mers, and 5 10-mers, corresponding to rates of 3.53%, 15.20%, 50.93%,
6.42%, 10.90%, 9.98%, 2.10%, 0.15%, 0.64%, and 0.13%, respectively (see Table1).Table 1
Distribution of SSR/minisatellite motifs according to the number of repeats.
RepeatsMono-(%)2-mer(%)3-mer(%)4-mer(%)5-mer(%)6-mer(%)7-mer(%)8-mer(%)9-mer(%)10-mer(%)Total(%)30—0—0—0—0—0—7895.126100249651001132.8940—0—0—0—34881.6932382.8244.8800140067617.3050—0—0—18172.116916.204511.54000000002957.5560—0—0—4116.3371.64133.3300000000611.5670—0—122061.3193.590051.2800000000123431.5880—0—44122.1693.5910.2310.260000000045211.5790—0—1738.6941.590010.26000000001784.56100—12521.04683.4210.400020.51000000001965.02110—8213.80321.6131.200000000000001172.99120—7612.79180.9010.40000000000000952.43130—7111.9550.2510.40000000000000771.97140—396.5720.1000000000000000411.05150—447.4150.250010.230000000000501.28160—305.0520.1000000000000000320.82170—335.5610.0500000000000000340.87180—152.5330.1500000000000000180.46190—172.8610.0510000000000000190.49202115.22142.3620.1000000000000000370.95211913.7781.3520.1000000000000000290.74221510.8761.0130.1500000000000000240.612385.8071.1830.1500000000000000180.462432.1750.84000000000000000080.202596.5250.8410.0500000000000000150.382653.6240.6720.1000000000000000110.282732.1710.1710.050000000000000050.132810.7230.5130.150000000000000070.182942.900010.050000000000000050.133021.4500000000000000000020.053196.5220.3410.0500000000000000120.313232.1730.51000000000000000060.153332.1700000000000000000030.083410.7210.17000000000000000020.053564.3510.17000000000000000070.183610.7210.17000000000000000020.053710.7200000000000000000010.033842.9000000000000000000040.10390010.17000000000000000010.034010.7200000000000000000010.034110.7200000000000000000010.034221.4500000000000000000020.054321.4500000000000000000020.05440000000000000000000000.00≥451410.14000000000000000000140.36Total13859419902514263908262553907(%)3.5315.2050.936.4210.909.982.100.150.640.13100.00For the
remaining SSRs, average percentage values have been reported as between 17 and 40% for 2-mer,
54–78% for 3-mer, 2.6–6.6% for 4-mer, 0.4–1.3% for 5-mer, and less than 1% for 6-mer
repeats [28] and 26.5%
for 2-mer, 65.4% 3-mer, 6.8% 4-mer, 0.77% 5-mer, and 0.45% for 6-mer repeats [30] for barley, maize, wheat, sorghum, rye, and rice, respectively.
In nonredundant transcripts from the TIGR database, 15.6% 2-mer, 61.6% 3-mer,
8.5% 4-mer, and 14.4% 5-mer repeats were found in rice [31].The frequency of micro/minisatellite
locus occurrence for each million nucleotides (loci/Mb) [6] in this study was 2.94,
12.64, 42.34, 5.34, 9.06, 8.30, 1.74, 0.13, 0.53, and 0.11 for mono to 10-mer
repeats/Mb, respectively. Overall occurrences of 83.13 loci/Mb were found (see Table 1). In other studies, different taxa were described in analyses of EST databases,
such as 133 loci/Mb (barley), 161 loci/Mb (wheat, sorghum and rye), and 256 loci/Mb
for rice [28]. Also, for nonredundant ESTs in rice, sorghum, barley, wheat, and
Arabidopsis, frequencies of 277, 169, 112, 94 and 133 loci/Mb were found,
respectively [30]. Frequencies closer to those found in this study were
described for CDS regions of Rosaceaespecies, with an average of 40.9–78 loci/Mb for
Rose, Almond and Peach, while 39 loci/Mb were found for Arabidopsis [26].


### 3.3. Occurrence Patterns for Different SSR and Minisatellite Types and Motifs Monomers,  2-Mers, 3-Mers, and 4-Mers

On Table2, the contents and percentage values for different micro-/minisatellite motifs
are shown. For monomer, 2-mer and 3-mer repeats, all possible arrangements are
shown, while for 4-mer to 10-mer repeats, only the ten most frequent motifs are
shown.Table 2
Distribution of SSR/minisatellite repeats in the rice cDNA collection.
MotifOcur(1)(%)(1)Ocur(2)(%)(2)Total(%) Group(%) OverallMono-A/T11188.801411.2012590.583.20C/G1076.92323.08139.420.332-merAG/CT9736.0617263.9426945.296.89GA/TC14361.379038.6323339.235.96CA/TG1035.711864.29284.710.72AT24100.00——244.040.61AC/GT631.581368.42193.200.49TA19100.00——193.200.49CG2100.00——20.340.053-merCCG/CGG19753.6817046.3236718.449.39CGC/GCG21861.2413838.7635617.899.11GCC/GGC11253.089946.9221110.605.40CTC/GAG7342.699857.311718.594.38AGG/CCT3430.917669.091105.532.82GGA/TCC6062.503637.50964.822.46CAG/CTG5876.321823.68763.821.95AAG/CTT3450.753349.25673.371.71CGA/TCG3354.102845.90613.071.56AGC/GCT3662.072237.93582.911.48GCA/TGC4783.93916.07562.811.43AGA/TCT3362.262037.74532.661.36CCA/TGG3975.001325.00522.611.33ACC/GGT2248.892351.11452.261.15GAA/TTC2863.641636.36442.211.13CAC/GTG2865.121534.88432.161.10GAC/GTC1854.551545.45331.660.84ACG/CGT1142.311557.69261.310.67ATC/GAT545.45654.55110.550.28TCA/TGA550.00550.00100.500.26CAA/TTG450.00450.0080.400.20ACT/AGT342.86457.1470.350.18TAA/TTA114.29685.7170.350.18CTA/TAG466.67233.3360.300.15AAT/ATT120.00480.0050.250.13CAT/ATG4100.000040.200.10AAC/GTT375.00125.0040.200.10ATA/TAT150.00150.0020.100.05GTA/TAC1100.000010.050.034-merGATC18100.0000187.170.46ATTA/TAAT952.94847.06176.770.44ATCG/CGAT320.001280.00155.980.38CATC/GATG440.00660.00103.980.26AGAA/TTCT225.00675.0083.190.20GCTA/TAGC675.00225.0083.190.20GATA/TATC114.29685.7172.790.18GCGA/TCGC342.86457.1472.790.18GCAC/GTGC233.33466.6762.390.15AGGG/CCCT233.33466.6762.390.155-merAGGAG/CTCCT315.001785.00204.690.51CTCTC/GAGAG1789.47210.53194.460.49GAGGA/TCCTC956.25743.75163.760.41CCTCC/GGAGG1280.00320.00153.520.38AGAGG/CCTCT426.671173.33153.520.38GGAGA/TCTCC218.18981.82112.580.28CTCGC/GCGAG777.78222.2292.110.23AGCTA/TAGCT444.44555.5692.110.23GAAAA/TTTTC225.00675.0081.880.20AGGCG/CGCCT225.00675.0081.880.206-merCGCCTC/GAGGCG1285.71214.29143.590.36CGGCGA/TCGCCG428.571071.43143.590.36CCTCCG/CGGAGG981.82218.18112.820.28AGGCGG/CCGCCT110.00990.00102.560.26CCGTCG/CGACGG444.44555.5692.310.23CGTCGC/GCGACG777.78222.2292.310.23ACCGCC/GGCGGT112.50787.5082.050.20CCACCG/CGGTGG685.71114.2971.790.18GGCGGA/TCCGCC571.43228.5771.790.18CTCCAT/ATGGAG6100.000061.540.157-merCCGCCGC/GCGGCGG466.67233.3367.320.15CTCTCTC/GAGAGAG480.00120.0056.100.13CCTCTCT/AGAGAGG4100.000044.880.10CTCTCTT/AAGAGAG4100.000044.880.10CCCAAAT/ATTTGGG3100.000033.660.08GCCGCCG/CGGCGGC3100.000033.660.08GCGGCGC/GCGCCGC2100.000022.440.05AATAAAA/TTTTATT2100.000022.440.05GTGTGCG/CGCACAC2100.000022.440.05CGCCGTC/GACGGCG2100.000022.440.058-merTTGGTTTC/GAAACCAA2100.0000233.330.05TGGGCTTG/CAAGCCCA1100.0000116.670.03GCTTCTTG/CAAGAAGC1100.0000116.670.03ACGGGCGA/TCGCCCGT1100.0000116.670.03ATGATGTA/TACATCAT1100.0000116.670.039-merTCGGCGGCG/CGCCGCCGA2100.000028.000.05AGGTGGTGG/CCACCACCT2100.000028.000.05CCGGTGCGA/TCGCACCGG1100.000014.000.03ACGAGGAGG/CCTCCTCGT1100.000014.000.03TCCCTTTTC/GAAAAGGGA1100.000014.000.03CGGCATGAA/TTCATGCCG1100.000014.000.03CGGCAGCGA/TCGCTGCCG1100.000014.000.03ACCATCCCG/CGGGATGGT1100.000014.000.03ATGGGCGGC/GCCGCCCAT1100.000014.000.03ATGCAGGGT/ACCCTGCAT1100.000014.000.0310-merAGCCCCAACG/CGTTGGGGCT150.00150.00240.000.05TTTTTTTCTT/AAGAAAAAAA1100.0000120.000.03CCTGCTTTGC/GCAAAGCAGG1100001200.03ATCTCCGCCG/CGGCGGAGAT1100001200.03The A/T monomer
repeats were found in 125 loci, with 111 (88.80%) and 14 (11.20%) loci formed
by A and T nucleotides, respectively. The C/G motifs were found in 13 loci,
with ten (76.92%) and three (23.08%) loci formed by C and G, respectively. A/T
containing SSRs were predominant and comprised 90.58% of monomer loci. In the
overall distribution, the monomers represent 3.53% of 3907 detected loci. Motifs
AG/CT and GA/TC were the most frequent and added up to 8.52% of 2-mer SSRs, and
6.89% and 5.96% of all 3907 detected occurrences. The motifs CT, GA, and TC were
the most abundant adding up to 172, 143, and 90 loci, respectively. In maize,
barley, rice, sorghum, and wheat ESTs, the motif AG was described as the most
frequent [6, 16, 28, 29, 31, 32]. However, in some studies, the most frequent
motif was GA [30, 33]. Repeats composed by guanine and cytosine were the most
abundant among trimers, with occurrences of 18.44%, 17.89%, and 10.60%,
respectively, for the motifs CCG/CGG, CGC/GCG, and GCC/GGC, adding up to 23.9% of
the overall frequencies of micro-/minisatellites in the analysis. The motifs
CGC, CCG, and CGG were the most frequent comprising 218, 197, and 170 loci,
respectively. Many reports indicate the 3-mer CCG as the most frequent in
maize, barley, wheat, sorghum and rye [6, 16, 28, 32], sugarcane [27] and rice [29, 31].Among 4-mers, 100 different
arrangements were found, where the motifs GATC (7.17%), ATTA/AAT (6.77%), and
ATCG/CGAT (5.98%) were the most frequent. These motifs add up to 19.92% of 4-mer
repeats found and represent 1.28% of the overall content of micro-/minisatellites.
In barley ESTs, ACGT was reported as the most abundant motif [16, 28]. For
other species, AAAG/CTTT and AAGG/CCTT in Lolium
perene [34], AAAG/CTTT and AAAC/GTTT in arabidopsis UTRs [6, 35], and AAAT and
AAAG in citrus [36, 37] were described as most abundant.


### 3.4. Remaining Repeats

Among 5-mers, 188
different arrangements were detected and the most frequent were CTCCT, CTCTC, and
CCTCC with 17, 17, and 12 occurrences, respectively. In the analysis of CDS
regions, the ACCCG motif was the most frequent in Arabidopsis, AAAAG inS. cerevisae, C. elegans,
and AAAAC in different primates [38]. Also, the motifs
AAAAT, AAAAC, and AAAAG were described as the most frequent in eukaryotes [39].
In rice, the motifs AGAGG and AGGGG were the most abundant [31]. Repeats of
type 6-mer were detected in 230 different arrangements, where CGCCTC and TCGCCG
were the most frequent, occurring in 12 and 10 loci, respectively. Other
studies have shown higher frequencies for the motifs AAGATG, AAAAAT in arabidopsis
[35], AAAAAG in citrus [36], AACACG in S. cerevisae, ACCAGG in C. elegans and CCCCGG in primates [38]. For
all remaining repeats (minisatellites), the occurrences are widely distributed
with low-percentage values for each arrangement. For 7-mer, 8-mer, 9-mer, and 10-mer
repeats, the totals of occurrences were 57, 5, 23, and 5, respectively.


### 3.5. Primer Design and PCR Simulation

The design of
primers for the 3907 detected micro-/minisatellites resulted in 3329 primer
pairs, covering 85.20% of loci. The running of “Virtual PCR” generated a total of
4610 amplicons. A module in SSRLocator checks for primer redundancy. A total
of 2397 primer pairs amplified only the fragment from its original locus (specific
amplicons) and 932 pairs amplified one or more regions besides the original
locus. From these, 692 pairs amplified two fragments, one from the original
site and a second from another region (paralogous). In this case, 692 specific amplicons
plus 692 redundant amplicons, were detected. A total of 143, 90, 2, and 5 primer
pairs generated three (two redundancies), four (three redundancies), five (four
redundancies), and six (five redundancies) fragments, respectively. The final
product of 932 primers with more than one anchoring region resulted in 932 specific
amplicons and 1281 redundant amplicons, adding up to 2213 fragments.To investigate the ability of these
primers in amplifying genomic sequences, an extra experiment was performed
against the whole rice genomic sequence available at NCBI. The different groups
of redundant and nonredundant primer sets, that is, amplifying one, two, three,
or more times in the cDNA database, were tested against the genomic sequence.
From the 2397 nonredundant primers, only 924 amplified a locus in the genomic
sequence. This difference was already expected because of difficulties in
amplifying genomic regions, that is, if some primers anneal to a boundary
region between two exons in the cDNA, the presence of introns would make this
annealing site no more available. It is interesting to note that from the 924
amplicons detected, 914 (99%) did amplify only one locus in the genomic region,
agreeing with the cDNA results. When the primer sets that amplified two
different cDNAs were run against the genomic sequence, only 294/692 (42.5%) did
amplify, having 14.5%
been able to amplify two different loci. Only
one primer set did amplify more than two loci. These results indicate that SSR
locator performance was consistent between the two databases regarding the
nonredundant loci, that is, from those loci that were able to be amplified in
both databases, their status of nonredundant was maintained. The changes
observed for the redundant loci can be attributable to many causes, including
redundancy in the cDNA database, but also to biological reasons due to primer
positioning.


### 3.6. Identity between Specific and Redundant Amplicons

Results of a global
alignment between amplicons from original and redundant sites are shown in Table3. Among the 1281 redundant amplifications, 787 (61.44%) resulted in a perfect
alignment between both loci (identity equal to 100). For redundant amplicons
with identity levels of 96–99%, and 90–95%, 452
(35.28%) and 8 (0.62%) loci were found, respectively. Alignments with identity
levels bellow 90% were found in only 2.65% of cases. The fact that such a high
percentage of redundant loci show high identity is probably a consequence of
the genome fraction chosen, that is, expressed sequences. This fraction is
under tight selection pressure and should not accumulate variations such as
substitutions or indels at a high rate. As expected, comparisons to whole
genome, generated a great deal of polymorphism, due to the inclusion of
intronic regions in the alignments (data not shown).Table 3
Distribution of amplicon alignments for specific and redundant amplicons with varying identity levels.
Identity1009998979695–9089–8079–7069–60≤59TotalAmplicons78726115129118865151281%61.4420.3711.792.260.860.620.620.470.391.17—


## 3.1. Program Validation

A total of 3907 micro- and minisatellites were detected by SSRLocator in the 28 469
analyzed cDNA sequences. The same database searched with MISA and SSRIT presented
3913 and 3917 loci, respectively. The mono-, 4-mer, 6-mer, 7-mer, 8-mer, 9-mer,
and 10-mer repeats were identical for the three programs. In the case of 2-mer repeats,
594 elements were detected by SSRLocator and 596 elements were detected by MISA
and SSRIT. 3-mer repeats were differently scored by SSRLocator (1990) and the
other two (1994) algorithms. For 5-mer repeats, SSRLocator and MISA found the
same number of repeats (426), while SSRIT (430) found a different value.


## 3.2. Overall Distribution of SSR Types

The results obtained with SSRLocator
indicate that out of 28 469 cDNA sequences, 3765 (13.22%) presented one or
more micro-/minisatellite loci. In other studies, microsatellites were found in
the following proportions in ESTs: 3% in arabidopsis  [25], 4% in rosaceae [26], 8.11% in barley [16], 2.9% in sugarcane [27], and values ranging between 6–11% [28] and 1.5–4.7%
[29]  for cereals in general (maize,
barley, rye, sorghum, rice, and wheat).Considering the 3765fl-cDNA
sequences, in 3632 (92.96%) only a single micro-/minisatellitelocus was detected. In 125 sequences, two loci were detected, in seven
sequences three lociandonly one sequence had four loci, adding
up to 3907 occurrences. Among the types analyzed, SSRs (mono to 6-mer repeats)
and minisatellites (7- to 10-mer repeats) comprised 96.98% and 4.12% of
detected loci, respectively.The distribution of occurrences detected by SSRLocator was consisted of 138 monomers,
594 2-mers, 1990 3-mers, 251 4-mers, 426 5-mers, 390 6-mers, 82 7-mers,  6 8-mers, 25 9-mers, and 5 10-mers, corresponding to rates of 3.53%, 15.20%, 50.93%,
6.42%, 10.90%, 9.98%, 2.10%, 0.15%, 0.64%, and 0.13%, respectively (see Table1).Table 1
Distribution of SSR/minisatellite motifs according to the number of repeats.
RepeatsMono-(%)2-mer(%)3-mer(%)4-mer(%)5-mer(%)6-mer(%)7-mer(%)8-mer(%)9-mer(%)10-mer(%)Total(%)30—0—0—0—0—0—7895.126100249651001132.8940—0—0—0—34881.6932382.8244.8800140067617.3050—0—0—18172.116916.204511.54000000002957.5560—0—0—4116.3371.64133.3300000000611.5670—0—122061.3193.590051.2800000000123431.5880—0—44122.1693.5910.2310.260000000045211.5790—0—1738.6941.590010.26000000001784.56100—12521.04683.4210.400020.51000000001965.02110—8213.80321.6131.200000000000001172.99120—7612.79180.9010.40000000000000952.43130—7111.9550.2510.40000000000000771.97140—396.5720.1000000000000000411.05150—447.4150.250010.230000000000501.28160—305.0520.1000000000000000320.82170—335.5610.0500000000000000340.87180—152.5330.1500000000000000180.46190—172.8610.0510000000000000190.49202115.22142.3620.1000000000000000370.95211913.7781.3520.1000000000000000290.74221510.8761.0130.1500000000000000240.612385.8071.1830.1500000000000000180.462432.1750.84000000000000000080.202596.5250.8410.0500000000000000150.382653.6240.6720.1000000000000000110.282732.1710.1710.050000000000000050.132810.7230.5130.150000000000000070.182942.900010.050000000000000050.133021.4500000000000000000020.053196.5220.3410.0500000000000000120.313232.1730.51000000000000000060.153332.1700000000000000000030.083410.7210.17000000000000000020.053564.3510.17000000000000000070.183610.7210.17000000000000000020.053710.7200000000000000000010.033842.9000000000000000000040.10390010.17000000000000000010.034010.7200000000000000000010.034110.7200000000000000000010.034221.4500000000000000000020.054321.4500000000000000000020.05440000000000000000000000.00≥451410.14000000000000000000140.36Total13859419902514263908262553907(%)3.5315.2050.936.4210.909.982.100.150.640.13100.00For the
remaining SSRs, average percentage values have been reported as between 17 and 40% for 2-mer,
54–78% for 3-mer, 2.6–6.6% for 4-mer, 0.4–1.3% for 5-mer, and less than 1% for 6-mer
repeats [28] and 26.5%
for 2-mer, 65.4% 3-mer, 6.8% 4-mer, 0.77% 5-mer, and 0.45% for 6-mer repeats [30] for barley, maize, wheat, sorghum, rye, and rice, respectively.
In nonredundant transcripts from the TIGR database, 15.6% 2-mer, 61.6% 3-mer,
8.5% 4-mer, and 14.4% 5-mer repeats were found in rice [31].The frequency of micro/minisatellite
locus occurrence for each million nucleotides (loci/Mb) [6] in this study was 2.94,
12.64, 42.34, 5.34, 9.06, 8.30, 1.74, 0.13, 0.53, and 0.11 for mono to 10-mer
repeats/Mb, respectively. Overall occurrences of 83.13 loci/Mb were found (see Table 1). In other studies, different taxa were described in analyses of EST databases,
such as 133 loci/Mb (barley), 161 loci/Mb (wheat, sorghum and rye), and 256 loci/Mb
for rice [28]. Also, for nonredundant ESTs in rice, sorghum, barley, wheat, and
Arabidopsis, frequencies of 277, 169, 112, 94 and 133 loci/Mb were found,
respectively [30]. Frequencies closer to those found in this study were
described for CDS regions of Rosaceaespecies, with an average of 40.9–78 loci/Mb for
Rose, Almond and Peach, while 39 loci/Mb were found for Arabidopsis [26].


## 3.3. Occurrence Patterns for Different SSR and Minisatellite Types and Motifs Monomers,  2-Mers, 3-Mers, and 4-Mers

On Table2, the contents and percentage values for different micro-/minisatellite motifs
are shown. For monomer, 2-mer and 3-mer repeats, all possible arrangements are
shown, while for 4-mer to 10-mer repeats, only the ten most frequent motifs are
shown.Table 2
Distribution of SSR/minisatellite repeats in the rice cDNA collection.
MotifOcur(1)(%)(1)Ocur(2)(%)(2)Total(%) Group(%) OverallMono-A/T11188.801411.2012590.583.20C/G1076.92323.08139.420.332-merAG/CT9736.0617263.9426945.296.89GA/TC14361.379038.6323339.235.96CA/TG1035.711864.29284.710.72AT24100.00——244.040.61AC/GT631.581368.42193.200.49TA19100.00——193.200.49CG2100.00——20.340.053-merCCG/CGG19753.6817046.3236718.449.39CGC/GCG21861.2413838.7635617.899.11GCC/GGC11253.089946.9221110.605.40CTC/GAG7342.699857.311718.594.38AGG/CCT3430.917669.091105.532.82GGA/TCC6062.503637.50964.822.46CAG/CTG5876.321823.68763.821.95AAG/CTT3450.753349.25673.371.71CGA/TCG3354.102845.90613.071.56AGC/GCT3662.072237.93582.911.48GCA/TGC4783.93916.07562.811.43AGA/TCT3362.262037.74532.661.36CCA/TGG3975.001325.00522.611.33ACC/GGT2248.892351.11452.261.15GAA/TTC2863.641636.36442.211.13CAC/GTG2865.121534.88432.161.10GAC/GTC1854.551545.45331.660.84ACG/CGT1142.311557.69261.310.67ATC/GAT545.45654.55110.550.28TCA/TGA550.00550.00100.500.26CAA/TTG450.00450.0080.400.20ACT/AGT342.86457.1470.350.18TAA/TTA114.29685.7170.350.18CTA/TAG466.67233.3360.300.15AAT/ATT120.00480.0050.250.13CAT/ATG4100.000040.200.10AAC/GTT375.00125.0040.200.10ATA/TAT150.00150.0020.100.05GTA/TAC1100.000010.050.034-merGATC18100.0000187.170.46ATTA/TAAT952.94847.06176.770.44ATCG/CGAT320.001280.00155.980.38CATC/GATG440.00660.00103.980.26AGAA/TTCT225.00675.0083.190.20GCTA/TAGC675.00225.0083.190.20GATA/TATC114.29685.7172.790.18GCGA/TCGC342.86457.1472.790.18GCAC/GTGC233.33466.6762.390.15AGGG/CCCT233.33466.6762.390.155-merAGGAG/CTCCT315.001785.00204.690.51CTCTC/GAGAG1789.47210.53194.460.49GAGGA/TCCTC956.25743.75163.760.41CCTCC/GGAGG1280.00320.00153.520.38AGAGG/CCTCT426.671173.33153.520.38GGAGA/TCTCC218.18981.82112.580.28CTCGC/GCGAG777.78222.2292.110.23AGCTA/TAGCT444.44555.5692.110.23GAAAA/TTTTC225.00675.0081.880.20AGGCG/CGCCT225.00675.0081.880.206-merCGCCTC/GAGGCG1285.71214.29143.590.36CGGCGA/TCGCCG428.571071.43143.590.36CCTCCG/CGGAGG981.82218.18112.820.28AGGCGG/CCGCCT110.00990.00102.560.26CCGTCG/CGACGG444.44555.5692.310.23CGTCGC/GCGACG777.78222.2292.310.23ACCGCC/GGCGGT112.50787.5082.050.20CCACCG/CGGTGG685.71114.2971.790.18GGCGGA/TCCGCC571.43228.5771.790.18CTCCAT/ATGGAG6100.000061.540.157-merCCGCCGC/GCGGCGG466.67233.3367.320.15CTCTCTC/GAGAGAG480.00120.0056.100.13CCTCTCT/AGAGAGG4100.000044.880.10CTCTCTT/AAGAGAG4100.000044.880.10CCCAAAT/ATTTGGG3100.000033.660.08GCCGCCG/CGGCGGC3100.000033.660.08GCGGCGC/GCGCCGC2100.000022.440.05AATAAAA/TTTTATT2100.000022.440.05GTGTGCG/CGCACAC2100.000022.440.05CGCCGTC/GACGGCG2100.000022.440.058-merTTGGTTTC/GAAACCAA2100.0000233.330.05TGGGCTTG/CAAGCCCA1100.0000116.670.03GCTTCTTG/CAAGAAGC1100.0000116.670.03ACGGGCGA/TCGCCCGT1100.0000116.670.03ATGATGTA/TACATCAT1100.0000116.670.039-merTCGGCGGCG/CGCCGCCGA2100.000028.000.05AGGTGGTGG/CCACCACCT2100.000028.000.05CCGGTGCGA/TCGCACCGG1100.000014.000.03ACGAGGAGG/CCTCCTCGT1100.000014.000.03TCCCTTTTC/GAAAAGGGA1100.000014.000.03CGGCATGAA/TTCATGCCG1100.000014.000.03CGGCAGCGA/TCGCTGCCG1100.000014.000.03ACCATCCCG/CGGGATGGT1100.000014.000.03ATGGGCGGC/GCCGCCCAT1100.000014.000.03ATGCAGGGT/ACCCTGCAT1100.000014.000.0310-merAGCCCCAACG/CGTTGGGGCT150.00150.00240.000.05TTTTTTTCTT/AAGAAAAAAA1100.0000120.000.03CCTGCTTTGC/GCAAAGCAGG1100001200.03ATCTCCGCCG/CGGCGGAGAT1100001200.03The A/T monomer
repeats were found in 125 loci, with 111 (88.80%) and 14 (11.20%) loci formed
by A and T nucleotides, respectively. The C/G motifs were found in 13 loci,
with ten (76.92%) and three (23.08%) loci formed by C and G, respectively. A/T
containing SSRs were predominant and comprised 90.58% of monomer loci. In the
overall distribution, the monomers represent 3.53% of 3907 detected loci. Motifs
AG/CT and GA/TC were the most frequent and added up to 8.52% of 2-mer SSRs, and
6.89% and 5.96% of all 3907 detected occurrences. The motifs CT, GA, and TC were
the most abundant adding up to 172, 143, and 90 loci, respectively. In maize,
barley, rice, sorghum, and wheat ESTs, the motif AG was described as the most
frequent [6, 16, 28, 29, 31, 32]. However, in some studies, the most frequent
motif was GA [30, 33]. Repeats composed by guanine and cytosine were the most
abundant among trimers, with occurrences of 18.44%, 17.89%, and 10.60%,
respectively, for the motifs CCG/CGG, CGC/GCG, and GCC/GGC, adding up to 23.9% of
the overall frequencies of micro-/minisatellites in the analysis. The motifs
CGC, CCG, and CGG were the most frequent comprising 218, 197, and 170 loci,
respectively. Many reports indicate the 3-mer CCG as the most frequent in
maize, barley, wheat, sorghum and rye [6, 16, 28, 32], sugarcane [27] and rice [29, 31].Among 4-mers, 100 different
arrangements were found, where the motifs GATC (7.17%), ATTA/AAT (6.77%), and
ATCG/CGAT (5.98%) were the most frequent. These motifs add up to 19.92% of 4-mer
repeats found and represent 1.28% of the overall content of micro-/minisatellites.
In barley ESTs, ACGT was reported as the most abundant motif [16, 28]. For
other species, AAAG/CTTT and AAGG/CCTT in Lolium
perene [34], AAAG/CTTT and AAAC/GTTT in arabidopsis UTRs [6, 35], and AAAT and
AAAG in citrus [36, 37] were described as most abundant.


## 3.4. Remaining Repeats

Among 5-mers, 188
different arrangements were detected and the most frequent were CTCCT, CTCTC, and
CCTCC with 17, 17, and 12 occurrences, respectively. In the analysis of CDS
regions, the ACCCG motif was the most frequent in Arabidopsis, AAAAG inS. cerevisae, C. elegans,
and AAAAC in different primates [38]. Also, the motifs
AAAAT, AAAAC, and AAAAG were described as the most frequent in eukaryotes [39].
In rice, the motifs AGAGG and AGGGG were the most abundant [31]. Repeats of
type 6-mer were detected in 230 different arrangements, where CGCCTC and TCGCCG
were the most frequent, occurring in 12 and 10 loci, respectively. Other
studies have shown higher frequencies for the motifs AAGATG, AAAAAT in arabidopsis
[35], AAAAAG in citrus [36], AACACG in S. cerevisae, ACCAGG in C. elegans and CCCCGG in primates [38]. For
all remaining repeats (minisatellites), the occurrences are widely distributed
with low-percentage values for each arrangement. For 7-mer, 8-mer, 9-mer, and 10-mer
repeats, the totals of occurrences were 57, 5, 23, and 5, respectively.


## 3.5. Primer Design and PCR Simulation

The design of
primers for the 3907 detected micro-/minisatellites resulted in 3329 primer
pairs, covering 85.20% of loci. The running of “Virtual PCR” generated a total of
4610 amplicons. A module in SSRLocator checks for primer redundancy. A total
of 2397 primer pairs amplified only the fragment from its original locus (specific
amplicons) and 932 pairs amplified one or more regions besides the original
locus. From these, 692 pairs amplified two fragments, one from the original
site and a second from another region (paralogous). In this case, 692 specific amplicons
plus 692 redundant amplicons, were detected. A total of 143, 90, 2, and 5 primer
pairs generated three (two redundancies), four (three redundancies), five (four
redundancies), and six (five redundancies) fragments, respectively. The final
product of 932 primers with more than one anchoring region resulted in 932 specific
amplicons and 1281 redundant amplicons, adding up to 2213 fragments.To investigate the ability of these
primers in amplifying genomic sequences, an extra experiment was performed
against the whole rice genomic sequence available at NCBI. The different groups
of redundant and nonredundant primer sets, that is, amplifying one, two, three,
or more times in the cDNA database, were tested against the genomic sequence.
From the 2397 nonredundant primers, only 924 amplified a locus in the genomic
sequence. This difference was already expected because of difficulties in
amplifying genomic regions, that is, if some primers anneal to a boundary
region between two exons in the cDNA, the presence of introns would make this
annealing site no more available. It is interesting to note that from the 924
amplicons detected, 914 (99%) did amplify only one locus in the genomic region,
agreeing with the cDNA results. When the primer sets that amplified two
different cDNAs were run against the genomic sequence, only 294/692 (42.5%) did
amplify, having 14.5%
been able to amplify two different loci. Only
one primer set did amplify more than two loci. These results indicate that SSR
locator performance was consistent between the two databases regarding the
nonredundant loci, that is, from those loci that were able to be amplified in
both databases, their status of nonredundant was maintained. The changes
observed for the redundant loci can be attributable to many causes, including
redundancy in the cDNA database, but also to biological reasons due to primer
positioning.


## 3.6. Identity between Specific and Redundant Amplicons

Results of a global
alignment between amplicons from original and redundant sites are shown in Table3. Among the 1281 redundant amplifications, 787 (61.44%) resulted in a perfect
alignment between both loci (identity equal to 100). For redundant amplicons
with identity levels of 96–99%, and 90–95%, 452
(35.28%) and 8 (0.62%) loci were found, respectively. Alignments with identity
levels bellow 90% were found in only 2.65% of cases. The fact that such a high
percentage of redundant loci show high identity is probably a consequence of
the genome fraction chosen, that is, expressed sequences. This fraction is
under tight selection pressure and should not accumulate variations such as
substitutions or indels at a high rate. As expected, comparisons to whole
genome, generated a great deal of polymorphism, due to the inclusion of
intronic regions in the alignments (data not shown).Table 3
Distribution of amplicon alignments for specific and redundant amplicons with varying identity levels.
Identity1009998979695–9089–8079–7069–60≤59TotalAmplicons78726115129118865151281%61.4420.3711.792.260.860.620.620.470.391.17—


## 4. Conclusions

The software SSRLocator was successfully implemented, adding steps for (1) SSR
discovery, (2) primer design, and (3) PCR simulation between the primers obtained
from original sequences and other fasta files. Also, the software produces
reports for frequency of occurrence, nucleotide arrangement, primer lists with
all standard information needed for PCR and global alignments. From the PCR
simulation, it was possible to point out which primer pairs were nonredundant,
suggesting that these primers are more appropriate for mapping purposes. In
this case, however, wet lab experiments should be performed to confirm the
advantage of nonredundant over redundant primers for mapping.It is possible that the results for micro-/minisatellite frequencies (loci/Mb) obtained
in this study diverge from the results found in the literature. This can be
explained by the different databases used (redundant ESTs, nonredundant ESTs and/or
fl-cDNA), different algorithm configurations and minimum requirements set for
counting motifs. Another explanation for some contrasting results is the fact
that only “Class I” repeats were analyzed in our study.The results showed that 932 (27.99%) primers presented
amplifications in more than one gene sequence. This could be mostly due to the
fact that primer pairs derived from a specific gene (cDNA) anchored in similar
sites in other duplicated genes, since 5,607/28,469 (19.70%) genes were described as paralogs in the
annotation of the database used [24]. Gene duplication along with polyploidy
and transposon amplification are the major driving forces in genome evolution [40].
It is therefore not surprising that so many loci have redundancy. Also, a
second possibility is that some primers were generated from protein domain
regions within the analyzed cDNAs. These domains could be found in protein
families with many genome copies, resulting in the observed redundancies. A
validation of the redundancies of cDNA results was obtained through a virtual-PCR against the whole
rice genome sequence. From the nonredundant primers that generated an amplicon,
ca. 99% were nonredundant.Finally,
this tool can be used successfully for data mining strategies to find SSR
primers in genomic or expressed sequences (ESTs/cDNAs). Also, this software can
be a tool for microsatellite discovery in databanks of related species,
anchoring primers in ortholog or paralog regions contained between databases
from two different species.



---

*Source: 412696-2008-07-27.xml*