- Research article
- Open Access
In silicocomparative analysis of SSR markers in plants
© Victoria et al; licensee BioMed Central Ltd. 2011
Received: 10 July 2010
Accepted: 19 January 2011
Published: 19 January 2011
The adverse environmental conditions impose extreme limitation to growth and plant development, restricting the genetic potential and reflecting on plant yield losses. The progress obtained by classic plant breeding methods aiming at increasing abiotic stress tolerances have not been enough to cope with increasing food demands. New target genes need to be identified to reach this goal, which requires extensive studies of the related biological mechanisms. Comparative analyses in ancestral plant groups can help to elucidate yet unclear biological processes.
In this study, we surveyed the occurrence patterns of expressed sequence tag-derived microsatellite markers for model plants. A total of 13,133 SSR markers were discovered using the SSRLocator software in non-redundant EST databases made for all eleven species chosen for this study. The dimer motifs are more frequent in lower plant species, such as green algae and mosses, and the trimer motifs are more frequent for the majority of higher plant groups, such as monocots and dicots. With this in silico study we confirm several microsatellite plant survey results made with available bioinformatics tools.
The comparative studies of EST-SSR markers among all plant lineages is well suited for plant evolution studies as well as for future studies of transferability of molecular markers.
In agriculture, productivity is affected by environmental conditions such as drought, salinity, high radiation and extreme temperatures faced by plants during their life cycle, that impose severe limitations to the growth and propagation, restricting their genetic potential and, ultimately, reflecting yield losses of agricultural crops. Although, advances have been achieved through classical breeding, further progress is needed to increase abiotic stress tolerance in cultivated plants. New gene targets need to be identified in order to reach these goals, requiring extensive studies concerning the biological processes related to abiotic stresses. Comparative analysis between primitive and related groups of cultivated species may shed some light on the understanding of these processes.
Microsatellites or SSRs (Simple Sequence Repeats) are sequences in which one or few bases are tandemly repeated, ranging from 1-6 base pair (bp) long units. They are ubiquitous in prokaryotes and eukaryotes, present even in the smallest bacterial genomes [1–3]. Variations in SSR regions originate mostly from errors during the replication process, frequently DNA Polymerase slippage. These errors generate base pair insertions or deletions, resulting, respectively, in larger or smaller regions . SSR assessments in the human genome have shown that many diseases are caused by mutation in these sequences . The genomic abundance of microsatellites, and their ability to associate with many phenotypes, make this class of molecular markers a powerful tool for diverse application in plant genetics. The identification of microsatellite markers derived from EST (or cDNAs), and described as functional markers, represents an even more useful possibility for these markers when compared to those based on assessing anonymous regions [6–8]. EST-SSRs offer some advantages over other genomic DNA-based markers, such as detecting the variation in the expressed portion of the genome, giving a ''perfect''marker-trait association; they can be developed from EST databases at no cost and unlike genomic SSRs, they may be used across a number of related species .
Many studies indicate UTRs as being more abundant in microsatellites than CDS regions . In a study of micro- and minisatellite distribution in UTR and CDS regions using the Unigene database for several higher plants groups, higher occurrence of these elements in coding regions were found for all the studied species . Disagreements between earlier reports and the later, reflect a deficiency in annotation when translated and non-translated fractions are separated in the Unigene transcript database. Dimer repeats were also frequent in CDS regions, which could be due to the fact that the Unigene database contains predominantly EST clusters. Therefore, there is a tendency for under-representing the UTR regions in the annotated sequences .
The characterization of tandem repeats and their variation within and between different plant families, could facilitate their use as genetic markers and consequently allow plant-breeding strategies that focus on the transfer of markers from model to orphan species to be applied. EST-SSR also have a higher probability of being in linkage disequilibrium with genes/QTLs controlling economic traits, making them more useful in studies involving marker-trait association, QTL mapping and genetic diversity analysis .
On model organisms, microsatellites have been reported to correspond to 0.85% of Arabidopsis thaliana (L.) Heynh, 0.37% of maize (Zea mays L.), 3.21% of tiger puffer (Takifugu rubripes Temminck & Schlegel), 0.21% of the nematode Caenorhabditis elegans Maupas and 0.30% of yeast (Saccharomyces cerevisiae Meyer ex. E.C. Hansen) genomes . Moreover, they constitute 3.00% of the human genome . All kinds of repeated element motifs, excluding trimers and hexamers, are significantly less frequent in the coding sequences when compared to intergenic DNA streches of A. thaliana, Z. mays, Oryza sativa subsp japonica S. Kato (rice), Glycine max (L.) Merr. (soybean) and Triticum aestivum L. (wheat) .
Close to 48.67% of repeat elements found in many species are formed by dimer motifs. In Picea abies (L.) H. Karst. (Norway spruce), for example, the dimer occurrence is 20 times more frequent in clones originating from intergenic regions vs. transcript regions . Approximately 14% of protein translated sequences (CDS - coding sequences) contain repetitive DNA regions, and this phenomenon is 3 folds more frequent in eukaryotes than prokaryotes . Clustering studies showing microsatellite occurrence in distinct protein families (non-homologous) from either prokaryotic or eukaryotic genomes, indicate that the origins of these loci occurred after eukaryotic evolution [14–16]. The highest and lowest repeat counts were found in rodents and C. elegans, respectively .
In plant species, some reports have described the levels of occurrence of microsatellites associated to transcribed regions [7, 8, 10, 11, 17–22]. However, some comparative and/or descriptive approaches, still can offer new perspectives on the features of these markers. Furthermore, frequently new groups of plant species have their genome sequenced, enabling the reassessment of databases using new sequences, representing divergent evolutionary groups and/or with different genetic models.
The online platforms for nucleotide, protein and transcript (ESTs) databases available for the majority of species are relatively small when compared with model species, eg Physcomitrella patens (Hedw.) Bruch & Schimp., O. sativa and A. thaliana. Since the protocols for the isolation of repetitive element loci, such as microsatellites, require intensive labour and can be expensive, the exploitation of these elements in silico on databases of model plants and their respective transfer to orphan species, is a potentially fruitful strategy.
In this study we present our results on the SSR survey for the development of plant SSR markers. The survey was based on clustered non-redundant EST data, their classification, characterization and comparative analysis in eleven phylogenetically distant plant species including two green algae, a hepatic, two mosses, two fern, two gymnosperms, a monocot and a dicot.
Results and Discussion
EST database size and Overall occurrence of SSR, percentages and average length motifs per specie
EST database count
Average pg count per EST
GC Content %
EST database size and Overall occurrences of SSRs, percentages and average length motifs per species
Number of SSR loci
SSR/EST database (%)
Average motif length (bp)
EST sequences with SSRs (%)
N. of seq. containing more than one SSR (%)
The average motif length, excluding compound SSRs, was 27.03 bp. Mesostigma EST database shows the longest SSR average size with 34.13 bp, and the shortest size was found for Marchantia polymorpha with 22.56 bp mean size. The SSR size for model plants was similar. For P. patens, O. sativa and A. thaliana, average sizes of 24.2, 23.4 and 26.5 bp were found, respectively. A total 1,106 EST sequences contained more than one SSR. Among the species, O. sativa and P. patens are on the extremes of the distribution with 37.34% and 3.46% of virtual transcripts containing one or more microsatellites. However, Adiantum capillus-veneris EST database contained the highest percentage of transcripts displaying more than one SSR (20.86%) based on the database size. Similar results were found in our group , using the Unigene database for grasses and other allies. In the same study, rice was shown to have the highest frequency of ESTs containing more than one SSR (11.28%). In the present study, a similar value was found for rice (10.20%). These small differences could be due to different redundancy reduction parameters used in Unigene species database and CAP3 default settings. Other reports for higher plants [19, 20, 24–26], showed different ranges, but never higher than 2-3 fold. The variations encountered in different reports are related to the strategy employed by investigators (software, repeat number and motif type) . The results for each species, regarding the percentage of SSRs found per EST database size are shown on Table 2.
The microsatellite survey using SSRLocator showed that 13,133 SSRs were available as potential marker loci. From those, 12,585 loci were found in single formation and only 590 were found in compound formation. The fern A. capillus-veneris showed the highest percentage (20%) of compound SSR loci. When compared with other available SSR marker search tools, similar results were found. Using MISA software, a total of 13,861 SSRs were available as potential marker loci, being 13,172 SSRs single and 689 compound SSRs for all studied species. Adiantum EST database showed the highest percentage of SSR in compound formation (15.55%). This trend does not hold for the majority of lower plants. P. patens, for example, presented few EST-SSRs in compound formation (3.57%) and possibly the fern lower database size is masking the results. When it is compared with the majority of plant groups, P. taeda is the only species showing a high percentage of compound SSRs (5.81%), corroborating other studies which report that compound and imperfect tandem repeats are most common in pines [27–29].
A total of 3,723 EST-SSRs were found in P. patens database using the MISA software . The SSRLocator analysis resulted in 2,839 SSR for this species. When the same non-redundant databases were run in other bioformatics tools, the results were similar to MISA. Using the SciKoco package  combined with MISA, Sputinik and Modified scripts, it was possible to narrow SSR results to a 2-fold range variation.
The average GC-content in the 11 datasets was 48.55%. Significantly increased GC-contents were detected for the green algae Chlamydomonas (57.22%) and Mesostigma (51.36%), for the moss Syntrichia ruralis (54.75%) and the fern moss Sellaginella spp. (51.38%). These results are in agreement with other genomic comparative analyses of a wide range of plant groups, where the lower groups presented the higher contents [23, 31, 32]. The remaining species showed similar results (Table 1).
Dimer and Trimer most frequent motifs
For algae species, the most frequent dimer motifs were AC/GT and CA/TG (Figure 2). For example, in C. reinhardtii, from 548 dimer occurrences, 199 AC/GT and 233 CA/TG motifs were found. The predominant trimer motifs found were GCA/TGC, CAG/CTG and GCC/GGC (Additional file 3) with 55, 46 and 39 occurrences in 263 trimers found for algae species. For nonvascular plants, the predominant dimer motifs were AG/CT (239/1,049), AT/AT (226/1,049) and GA/TC (340/1,049), as found for P. patens. For mosses, the most frequent trimers found within the studied species were GCA/TGC, AAG/CTT and AGC/GCT. For vascular plants, the most frequent motifs were AG/CT and GA/TC. In O. sativa, 246 (43%) and 191(33%) occurrences for these motifs were found, respectively, in a total of 578 dimer occurrences. The GC/GC was only detected in C. reinhardtii. There has been a report on the abundance of GC elements in Chlamydomonas genome libraries .
Among trimer motifs, there was a predominance of AAG/CTT, AGA/TCT, GGA/TCC and GAA/TTC in higher plants. In lower plants, the motifs GCA/TGC and CAG/CTG were predominant. The trimer motif CCG/CGG is predominant in the algae C. reinhardtii and the model moss P. patens, and could reflect the high GC content in these two species. However, this relationship does not hold for the other cryptogams analysed. The increased CCG/CGG frequency has been described earlier for grasses and has been related to a high GC-content . In this context, the CCG/CGG increase in Chlamydomonas and P. patens was consistent, but, a previous study reported that it can not be taken as a rule, since higher GC values were found for other lower groups with low CCG/CGG contents . For rice CCG/CGG is the predominant motif and its content appears to be high in the members of the grass family [11, 21].
Comparing all plant groups selected for this in silico study, the most frequent dimer motifs found were AG/CT and GA/TC, occurring for all plant species. The most frequent trimers were AAG/CTT and GCA/TGC occurring in the 11 studied species.
Tetramers, Pentamers and Hexamers
Tetramer and pentamer motifs were rare for all studied species except for M. viride. This algae showed the higher frequencies in loci formed by motifs longer than three nucleotides with 36.95% of tetramer and 19.56% of pentamer motifs. Although these results are in agreement with other study , it is difficult to state that this is a rule for this species, since the EST database size for Mesostigma is the smallest one available among the studied databases. In general, tetramer and pentamer motifs predominantly found for Oryza, Physcomitrella and Selaginela where CATC/GATG, CTCC/GGAG, GATC/GATC, TGCT/AGCA (Additional file 4) and CTTCT/AGAAG, GGAGA/TCTCC, GGCAG/CTGCC, TCTCG/CGAGA and TGCTG/CAGCA (Additional file 5) and these were the most frequent motifs, at least for two out of three of these species.
Hexamer motifs were predominant in novel taxa such as gymnosperms and flowering plants [3, 21, 35]. P. taeda and G. gnemom showed the highest frequency (26.95%) of these motifs, but none of the hexamer motifs found in Gnetum and Pinus were found in common with other plant EST databases. However, one can not state the absence of hexamer motif patterns in plant groups, since in Bryophytes there is a possibility of patterns occurring within closely related groups. For P. patens and M. polymorpha the AGCAGG/AGCAGG, AGCTGG/CCAGGT, CAGCAA/TTGCTG and TGGTGC/GCACCA motifs occur in both species (Additional file 6). Based on plastid molecular data, Marchantiophyta and Bryophyta originated about 450 Mya  and its possible that some repeats are conserved for recently formed groups, but it would be necessary to include others species in further analyses to confirm this hypothesis. For the other SSR types (7, 8, 9 and 10 repeats) frequencies were very low (less than 2 occurrences per motif) and were not further characterized.
Physcomitrella patens SSR loci versusGene Ontology assignments
Distribution of Blast hits for Physcomitrella patens SSR loci sequences against several taxa with GO assignment
Best Hits (%)
Predicted coding for SSR loci
The small EST databases available for some species did not seem to have hampered the results, since the predicted loci distribution found were consistent within the taxonomic groups. The absence of a relationship between genome size and tandem repeat loci content were reported based in grass genome studies , where large genomes such as sugarcane (Saccharum officinarum L.), maize and wheat did not present higher frequencies of SSR loci.
Relationship of Codon-bias with EST-SSR motif occurrences
The width of the GC3 distribution in flowering plants was found to be a result of variation in the levels of directional mutation pressure or selection against mutational biases. Likewise, the low frequency of GC2 occurrences is a result of a strong selective pressure against peptide substitution. The balance between these forces could be shaping the distribution of EST-SSR by means of codon usage preference .
Positive and negative selection sites in EST-SSR across species
SSRs represent hyper mutable loci subject to reversible changes in their length . Significant differences in SSR representations exist even among closely related species, suggesting that SSR abundance may change relatively rapidly during evolution . To infer about the selection pressures (dN/dS ratio) on EST-SSR found for the 11 species chosen for this work, we used the common most frequent motif in all species (AAG/CTT and GCA/TGC). The dN-dS test revealed few negatively selected sites in the triplets for each EST-SSR (Additional file 7). The positive selection in SSR based sequence was reported in other studies [8, 49–51]. More than 50% of sites for both motifs analyzed across species were under a positive selection (dN/dS > 1), suggesting a weak selection pressure on these EST-SSR motifs, as was reported for other species [52, 53]. The occurrence of selective sweeps or background selection in ancestral lineages  cannot be discarded, however it could not be tested with the present data.
In silico transferability of EST-SSR across species
Across-species transferability of EST-SSRs is greater than genomic SSRs, as they originate from expressed regions and therefore they are more conserved across a number of related species .
The virtual PCR shows a lower transferability of Chlamydomonas reinhardtii EST-SSR for most of the plant species tested. The best results were found for Adiantum and Arabidopsis, where successful rates of positive EST-SSR amplicons derived from algae were 26% and 9%, respectively. When EST-SSR primers designed from Arabidopsis were used against other species, again low transferability rates were found, being the best positive cases found in Physcomitrella, Pinus and rice with amplification rates of 1.04%, 1.20% and 1.90%. The summary of in silico PCR results can be accessed in the Additional files section of this article. Some reports suggest that SSR markers have higher transferability rates when used between closely related species [6, 22, 55]. In this work virtual PCR amplification did follow the same trend.
These results make it possible to create strategies for transferring molecular markers based on microsatellites from model to orphan species.
Microsatellites were found in all species studied and variable transfer rates were found as a function of genetic distance among taxa. The motifs found are influenced by species codon usage preference. The two most common motifs among the eleven species are under a positive selection pressure. Primers generating one amplicon in the genome of origin may generate multiple amplicons in other taxa and only a few retain their original targeting sequence. The similarities between the results here presented and other initiatives using similar bioinformatics Perl scripts, such as MISA , support SSRLocator as a useful tool for SSR survey analyses.
An exploratory in silico analysis of SSRs was made in ESTs databases of 11 taxa, as follows: two unicellular green algae (Chlamydomonas reinhardtii Dang, Mesostigma viride Lauterborn.), three bryophytes s. l. [Marchantia polymorpha L., Physcomitrella patens and Syntricha ruralis (Hedw.) Weber & Mohr], two ferns (Selaginella spp. and Adiantum capillus-veneris L.), two gymnosperms (Gnetum gnemon L. and Pinus taeda L.) and two flowering plants, a monocot (Oryza sativa) and a dicot (Arabidopsis thaliana). These species were chosen because the amount of available ESTs data in Genbank (NCBI). As these databases may have redundancy, we used the program CAP3  for MacOX, to construct contigs with the sequences and get non-redundant sequences for each database following the default settings.
Taxa data were loaded into the software SSRLocator , to investigate the presence of tandem repetitive elements (SSRs). The analysis was performed following the search parameters for repetitive elements in class I (≥ 20 bp) described as more efficient molecular markers . Data resulting from in silico analyses were assessed for occurrence patterns in chosen taxa databases. The same analysis was performed using MISA script http://pgrc.ipk-gatersleben.de/misa/ software to search for SSR occurrences per contig. Several instructions in the algorithm used in SSRLocator resemble those from MISA  and SSRIT . 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. For GC content, Perl scripts were used and the results were stored in text files (.txt) for later comparative analyses.
For the predicted amino acid contents in the SSR loci, an additional routine script was written in the SSRLocator software. This script determined which amino acids were coded by trimer, hexamer and nonamer motifs found in the EST database analysed .
To validate the frequencies obtained using the SSRLocator software, the Physcomitrella patens EST database was chosen.
This database was run with other SSR search scripts and softwares, such as MISA  and SPUTINIK , running in SCIROKO package , MINE SSR http://www.genome.clemson.edu/resources/online_tools/ssr, SSRIT following the SSR categories defined above . The results were exported into Microsoft Excel spreadsheets (MacOSX-Oficce 2008) and respectively grouped by taxon.
A codon-bias for the model plants included in this research (Chlamydomonas reinhardtii, Physcomitrella patens, Oryza sativa and Arabidopsis thaliana) was made comparing with the preferencial codon table for each species available at http://www.kazusa.or.jp/codon/. The sequences containing EST-SSR for Physcomitrella patens was submitted to CodonO server  to confirm the preferencial codon usage compared with the know codon table for this species. To investigate the selective pressure on the triplets on the EST-SSR which occurs in all studied species a dN-dS statistics  was used to verify the synonymous and noun-synonymous substitutions in the preferential codons nearby the repeats chosen using the molecular phylogenetics package MEGA4 .
The Physcomitrella patens SSR results were run through a Gene Ontology (GO) assignment database in order to assess associations between SSR loci and biological processes, cellular components and molecular function of known genes. A fasta file with all EST-SSRs found in P. patens was subjected to Blast2GO software and ran against the GO annotated sequences, and the obtained hits were compiled.
To verify the potential transferability of this molecular markers we have tested in silico all EST-SSR found for the plant ancestral lineage, and for the derivative plant group, represented here by the green algae Chlamydomonas reinhardtii and Arabidopsis thaliana, across the others species EST database used for the present SSR survey. Electronic PCR  was used to verify the transferability of EST-SSRs across studied species. The positive results found were used to simulate a gel electrophoresis with aid of SIMGEL.exe included in the SPCR package  using the Physcomitrella patens EST-SSR sequences to design primers and Chlamydomonas, rice and Arabidopsis as templates. The virtual amplicons resulted for each primer set tested across species were aligned to verify the homology between the amplicons.
We would like to thank the Developmental Center of Technology (CDTec/UFPEL) for the support to the first author. This work was supported by the National Council for Scientific and Technological Development CNPq (process # 480938/2009-1 and 475122/2007-0).
- Morgante M, Olivieri AM: PCR-amplified microsatellites as markers in plant genetics. The Plant Journal. 1993, 3 (1): 175-182. 10.1111/j.1365-313X.1993.tb00020.x.PubMedView ArticleGoogle Scholar
- Jurka J, Pethiyagoda C: Simple repetitive DNA sequences from Primates: Compilation and analysis. Journal of Molecular Evolution. 1994, 40: 120-126. 10.1007/BF00167107.View ArticleGoogle Scholar
- Tóth G, Gáspári Z, Jurka J: Microsatellites in different eukaryotic genomes: survey and analysis. Genome Research. 2000, 10: 967-981.PubMedPubMed CentralView ArticleGoogle Scholar
- Iyer RR, Pluciennik A, Rosche WA, Sinder RR, Wells RD: DNA polymerase III proofreading mutants enhance the expansion and deletion of triplet repeat sequence in Escherichia coli. Journal of Biological Chemistry. 2000, 275 (3): 2174-2184. 10.1074/jbc.275.3.2174.PubMedView ArticleGoogle Scholar
- Mirkin SM: DNA structures, repeat expansions and human hereditary disorders. Current Opinion in Structural Biology. 2006, 16 (3): 351-358. 10.1016/j.sbi.2006.05.004.PubMedView ArticleGoogle Scholar
- Varshney RK, Graner A, Sorrells ME: Genic microsatellite markers in plants: features and applications. Trends in Biotechnology. 2005, 23 (1): 48-55. 10.1016/j.tibtech.2004.11.005.PubMedView ArticleGoogle Scholar
- Varshney RK, Hoisington DA, Tyagy AK: Advances in cereal genomics and applications in crop breeding. Trends in Biotechnology. 2006, 24 (11): 490-499. 10.1016/j.tibtech.2006.08.006.PubMedView ArticleGoogle Scholar
- Kashi Y, King DG: Simple sequence repeats as advantageous mutators in evolution. Trends Genet. 2006, 22: 253-259. 10.1016/j.tig.2006.03.005.PubMedView ArticleGoogle Scholar
- Gupta PK, Rustgi S, Sharma S, Singh R, Kumar N, Balyan HS: Transferable EST-SSR markers for the study of polymorphism and diversity in bread wheat. Molecular Genetics and Genomics. 2003, 270: 315-323. 10.1007/s00438-003-0921-4.PubMedView ArticleGoogle Scholar
- Morgante M, Hanafey M, Powell W: Microsatellites are preferentially associated with nonrepetitive DNA in plant genomes. Nature Genetics. 2002, 3 (2): 194-200. 10.1038/ng822.View ArticleGoogle Scholar
- Maia LC, Souza VQ, Kopp MM, Carvalho FIF, Oliveira AC: Tandem repeat distribution of gene transcripts in three plant families. Genetics and Molecular Biology. 2009, 32 (4): 1-12. 10.1590/S1415-47572009005000091.View ArticleGoogle Scholar
- Subramanian S, Mishra RK, Singh L: Genome-wide analysis of microsatellite repeats in humans: their abundance and density in specific genomic regions. Genome Biology. 2003, 4 (2): R13-10.1186/gb-2003-4-2-r13.PubMedPubMed CentralView ArticleGoogle Scholar
- Li YC, Korol AB, Fahima T, Beiles A, Nevo E: Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Molecular Ecology. 2002, 11: 2453-2465. 10.1046/j.1365-294X.2002.01643.x.PubMedView ArticleGoogle Scholar
- Marcotte EM, Pellegrini M, Yeates TO, Eisenberg D: A census of protein repeats. Journal of Molecular Biology. 1999, 293: 151-10.1006/jmbi.1999.3136.PubMedView ArticleGoogle Scholar
- Kashi Y, King D, Soller M: Simple sequence repeats as a source of quantitative genetic variation. Trends in genetics. 1997, 13: 74-78. 10.1016/S0168-9525(97)01008-1.PubMedView ArticleGoogle Scholar
- Wren JD, Forgacs E, Fondon JW, Pertsemlidis A, Cheng SY, Gallardo T, Williams RS, Shohet RV, Minna JD, Garner HR: Repeat polymorphisms within gene regions: phenotypic and evolutionary implications. American Journal of Human Genetics. 2000, 67: 345-356. 10.1086/303013.PubMedPubMed CentralView ArticleGoogle Scholar
- Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S: Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Research. 2001, 11 (8): 1441-52. 10.1101/gr.184001.PubMedPubMed CentralView ArticleGoogle Scholar
- McCouch SR, Teytelman L, Xu Y, et al: Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA research. 2002, 9 (6): 199-207. 10.1093/dnares/9.6.199.PubMedView ArticleGoogle Scholar
- Thiel T, Michalek W, Varshney RK, Graner A: Exploiting EST databases for the development of cDNA derived microsatellite markers in barley (Hordeum vulgare L.). Theoretical and Applied Genetics. 2003, 1-6: 411-422.Google Scholar
- Nicot N, Chiquet V, Gandon B, Amilhat L, Legeai F, Leroy P, Bernard M, Sourdille P: Study of simple sequence repeat (SSR) markers from wheat expressed sequence tags (ESTs). Theoretical and Applied Genetics. 2004, 1-9 (4): 8008-5.Google Scholar
- Lawson MJ, Zhang L: Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomes. Genome Biology. 2006, 7: R14-10.1186/gb-2006-7-2-r14. 3PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang L, Yuan D, Yu S, Li Z, Cao Y, Miao Z, Qian H, Tang K: Preference of simple sequence repeats in coding and non coding regions of Arabidopsis thaliana. Bioinformatics. 2004, 20: 1081-1086. 10.1093/bioinformatics/bth043.PubMedView ArticleGoogle Scholar
- von Stackelberg MV, Rensing SA, Reski R: Identification of genic moss SSR markers and a comparative analysis of twnty-four algal and plant gene indices reveal species-specific rather than group-specific characteristics of microsatellites. BMC Plant Biology. 2006, 6: 9-10.1186/1471-2229-6-9.PubMedPubMed CentralView ArticleGoogle Scholar
- Cordeiro GM, Casu R, McIntyre CL, Manners JM, Henry RJ: Microsatellite markers from sugarcane (Saccharum spp.) ESTs cross transferable to erianthus and sorghum. Plant science. 2001, 16 (6): 1115-1123. 10.1016/S0168-9452(01)00365-X.View ArticleGoogle Scholar
- Kantety RV, La Rota M, Matthews DE, Sorrells ME: Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant molecular biology. 2002, 48 (5-6): 5-1-11.Google Scholar
- Asp T, Frei UK, Didion T, Nielsen KK, Lübberstedt T: Frequency, type, and distribution of EST-SSRs from three genotypes of Lolium perenne, and their conservation across orthologous sequences of Festuca arundinacea, Brachypodium distachyon, and Oryza sativa. BMC plant biology. 2007, 12 (7): 36-10.1186/1471-2229-7-36.View ArticleGoogle Scholar
- Echt CS, May-Marquardt P, Hseih M, Zahorchak R: Characterization of microsatellire markers in eastern white pine. Genome. 1996, 39: 1102-1108. 10.1139/g96-138.PubMedView ArticleGoogle Scholar
- Echt CS, May-Marquardt P: Survey of microsatellite DNA in pine. Genome. 1997, 40: 9-17. 10.1139/g97-002.PubMedView ArticleGoogle Scholar
- Fisher PJ, Gardner RC, Richardson TE: Single locus microsatellites isolated using 5'anchored PCR. Nucleic Acids Research. 1996, 24: 4369-4372. 10.1093/nar/24.21.4369.PubMedPubMed CentralView ArticleGoogle Scholar
- Kofler R, Schlotterer C, Lelley T: SciRoKo: A new tool for whole genome microsatellite search and investigation. Bioinformatics. 2007, 23: 1683-1685. 10.1093/bioinformatics/btm157.PubMedView ArticleGoogle Scholar
- Qiu Y-L, Lee J, Bernasconi-Quadroni B, Soltis DE, et al: The earliest Angiosperms: Evidence from mitochondrial, palstid and nuclear genomes. Nature. 1999, 402: 404-407. 10.1038/46536.PubMedView ArticleGoogle Scholar
- Rensing SA, Lang D, Zimmer AD, et al: The Physcomitrella genome reveals insights into the conquest of land by plants. Science. 2008, 319: 64-69. 10.1126/science.1150646.PubMedView ArticleGoogle Scholar
- Wakarchuk WW, Müller FW, Beck C: F. Two GC-rich elements of Chlamydomonas reinhardtii with complex arrangements of directly repeated sequences motifs. Plant Molecular Biology. 1992, 18: 143-146. 10.1007/BF00018468.PubMedView ArticleGoogle Scholar
- Yashoda R, Sumathi R, Chezhian P, Kavitha S, Ghosh M: Eucalyptus microsatellites mined in silico: survey and evaluation. Journal of Genetics. 2008, 87 (1): 21-25. 10.1007/s12041-008-0003-9.View ArticleGoogle Scholar
- Jiang D, Zhong GY, Hong QB: Analysis of microsatellites in citrus unigenes. Acta genetica Sinica. 2006, 33 (4): 345-53. 10.1016/S0379-4172(06)60060-7.PubMedView ArticleGoogle Scholar
- Magallón S, Hilu KW: Land plants (Embryophyta). The Timetree of Life. Edited by: S. B. Hedges, S. Kumar. Oxford, University Press; 2009, 133-137.Google Scholar
- Nishiyama T, Fujita T, Shin-I T, Seki M, Nishide H, Uchiyama I, Kamiya A, Carninci P, Hayashizaki Y, Shinozaki K, Kohara Y, Hasebe M: Comparative genomics of Physcomitrella patens gametophytic transcriptome and Arabidopsis thaliana: Implication for land plant evolution. PNAS. 2003, 100 (13): 8007-8012. 10.1073/pnas.0932694100.PubMedPubMed CentralView ArticleGoogle Scholar
- Oliver MJ, Dowd SE, Zaragoza J, Mauget SA, Payton PR: The rehydration transcriptome of the desiccation-tolerant bryophyte Tortula ruralis: Transcript classification and analysis. BMC Genomics. 2004, 5: 89-10.1186/1471-2164-5-89.PubMedPubMed CentralView ArticleGoogle Scholar
- Lang D, Eisinger J, Reski R, Resing SA: Representation and High-Quality Annotation of the Physcomitrella patens Transcriptome Demonstrates a High Proportion of Proteins Involved in Metabolism in Mosses. Plant Biology. 2005, 7: 238-250. 10.1055/s-2005-837578.PubMedView ArticleGoogle Scholar
- Ware D, Jaiswal P, Ni J, Pan X, Chang K, Clark K, Teytelman L, Schmidt S, Zhao W, Cartinhour S, McCouch S, Stein L: Gramene: a resource for comparative grass genomics. Nucleic Acids Research. 2002, 30: 103-105. 10.1093/nar/30.1.103.PubMedPubMed CentralView ArticleGoogle Scholar
- Rhee SY, Beavis W, Berardini TZ, et al: The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Research. 2003, 31: 224-228. 10.1093/nar/gkg076.PubMedView ArticleGoogle Scholar
- Jung S, Abbott A, Jesudurai C, Tomkins J, Main D: Frequency, type, distribution and annotation of simple sequence repeats in Rosaceae ESTs. Functional & integrative genomics. 2005, 5 (3): 136-43.View ArticleGoogle Scholar
- La Rota M, Kantety RV, Yu JK, Sorrells ME: Nonrandom distribution and frequencies of genomic and EST-derived microsatellite markers in rice, wheat, and barley. BMC Genomics. 2007, 18 (1): 23-6Google Scholar
- Varshney RK, Thiel T, Stein N, Langridge P, Graner A: In silico analysis on frequency and distribution of microsatellites in ESTs of some cereal species. Cell Mol Biol Lett. 2002, 7: 537-546.PubMedGoogle Scholar
- Parida SK, Anand Raj Kumar K, Dalal V, Singh NK, Mohapatra T: Unigene derived microsatellite markers for the cereal genomes. Theor Appl Genet. 2006, 112: 808-817. 10.1007/s00122-005-0182-1.PubMedView ArticleGoogle Scholar
- Rensing SA, Fritzomsky D, Lang D, Reski R: Protein encoding genes in an ancient plant: analysis of codon usage, retained genes and splice sites in a moss, Physcomitrella patens. BMC genomics. 2005, 6: 43-10.1186/1471-2164-6-43.PubMedPubMed CentralView ArticleGoogle Scholar
- Kawabe A, Miyashita NT: Patterns of codon usage bias in three dicot an four monocot plant species. Genes and Genetic System. 2003, 78: 343-352. 10.1266/ggs.78.343.View ArticleGoogle Scholar
- Mrázek J: Analysis of distribuition indicates diverse functions of simple sequence repeats in Mycoplasma genomes. Molecular Biology and Evolution. 2006, 23: 1370-1385.PubMedView ArticleGoogle Scholar
- King DG, Kashi Y: Indirect selection for mutuability. Heredity. 2007, 99: 123-124. 10.1038/sj.hdy.6800998.PubMedView ArticleGoogle Scholar
- King DG, Soller M: Variation and fidelity: The evolution of simple sequence repeats as functional elements in adjustable genes. Evolutionary Theory and Processes: Modern Perspectives. Edited by: Wasser SP. 1999, Kluwer Academic Publisher, the Netherlands, 65-82.View ArticleGoogle Scholar
- Vigouroux Y, Matsuoka Y, Doebley J: Directional evolution for microstellites size in maize. Molecular Biology and Evolution. 2003, 20: 1480-1483. 10.1093/molbev/msg156.PubMedView ArticleGoogle Scholar
- Ellis JR, Burke JM: EST-SSRs as a resource for population genetic analyses. Heredity. 2007, 99: 125-132. 10.1038/sj.hdy.6801001.PubMedView ArticleGoogle Scholar
- Yatabe Y, Kane NC, Scotti-Saintagne C, Rieseberg LH: Rampant gene exchange across a strong reproductive barrier between the annual sunflowers, Helianthus annuus and H petiolaris. Genetics. 2007, 175: 1883-1893. 10.1534/genetics.106.064469.PubMedPubMed CentralView ArticleGoogle Scholar
- Wrigth SI, Gaut BS: Molecular population genetics and the search for adaptative evolution in plants. Molecular Biology and Evolution. 2005, 22 (3): 506-519.Google Scholar
- Chapman MA, Hvala J, Strever J, et al: Development, polymorphism, and cross-taxon utility of EST-SSR markers from safflower (Carthamus tinctorius L.). Theoretical and Applied Genetics. 2009, 120: 85-91. 10.1007/s00122-009-1161-8.PubMedView ArticleGoogle Scholar
- Cao Y, Wang L, XU K, Kou C, Zhang Y, Wei G, He J, Wang Y, Zhao L: Information theory-based algorithm for in silico prediction of PCR products with whoke genomic sequences as templates. BMC bioinformatics. 2005, 6: 190-10.1186/1471-2105-6-190.PubMedPubMed CentralView ArticleGoogle Scholar
- Brondani C, Rangel PHN, Borba TCO, Brondani RPV: Transferability of microsatellite and sequence tagged site markers in Oryza species. Hereditas. 2003, 138: 187-192. 10.1034/j.1601-5223.2003.01656.x.PubMedView ArticleGoogle Scholar
- Castillo A, Budak H, Varshney RK, Dorado G, Graner A, Hernandez P: Tranferability and polimorphism of barley EST-SSR markersused for phylogenetic analysus in Hordeum chilense. BMC plant biology. 2008, 8: 97-10.1186/1471-2229-8-97.PubMedPubMed CentralView ArticleGoogle Scholar
- Yodav OP, Mitchell SE, Fulton TM, Kresovich S: Tranferring molecular markers from sorghum, rice and other cereals to pearl millet and identifying polumorphic markers. Journal of SAT Agricultural Research. 2008, 6: 1-4.Google Scholar
- Zeid M, Yu JK, Goldowitz I, Denton ME, et al: Cross-amplification of EST-derived markers among 16 grass species. Field Crops Research. 2010, 118: 28-35. 10.1016/j.fcr.2010.03.014.View ArticleGoogle Scholar
- Barbará T, Palma-Silva C, Paggi GM, Bered F, Fay MF, Lexer C: Cross-species transfer of nuclear microsatellites markers: potential and limitations. Molecular Ecology. 2007, 16: 3759-3767.PubMedView ArticleGoogle Scholar
- Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Research. 1999, 9: 868-877. 10.1101/gr.9.9.868.PubMedPubMed CentralView ArticleGoogle Scholar
- Maia LC, Palmieri DA, Souza VQ, Kopp MM, Carvalho FIF, Oliveira AC: SSR Locator: Tool for Simple Sequence Repeat Discovery Integrated with Primer Design and PCR Simulation. International Journal of Plant Genomics. 2008, Article ID 412696, 9 pagesGoogle Scholar
- Abajan C, SPUTINIK: 1994, [http://espressosoftware.com/sputnik/index.html]
- Angellotti MC, Bhuiyan SB, Chen G, Wan X-F: CodonO: codon usage bias analysis within and across genomes. Nucleic Acids Research. 2007, 35: W132-W136. 10.1093/nar/gkm392.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Z, Bielawski JP: Statistical methods for detecting molecular adaptation. Trends in Ecology and Evolution. 2000, 12: 496-503. 10.1016/S0169-5347(00)01994-7.View ArticleGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Molecular Biology and Evolution. 2007, 24: 1596-1599. 10.1093/molbev/msm092.PubMedView ArticleGoogle Scholar
- Schuler GD: Sequence mapping by eletronic PCR. Genome Research. 1997, 7 (5): 541-550.PubMedPubMed CentralGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.