chapter 5: development of ssr primers from...

CHAPTER 5: Development of SSR primers from EST database of Jatropha curcas

Marker assisted selection for high oil yielding varieties in Jatropha curcas 62

5.1 INTRODUCTION

The development of DNA-based genetic markers has been the driving force behind

current revolution in animal and plant genetics (Dodgson et al., 1997). The abundance

and hyper-variability associated with SSRs make them ideal candidates for

development of markers for genetic mapping, fingerprinting, gene tagging, marker-

assisted selection and evolutionary studies (Kantety et al., 2002; Powell et al., 1996;

Rafalski et al., 1993; Tautz, 1989). The standard molecular biology method for

developing SSR markers is the construction of small insert libraries followed by

nucleic acid hybridization-based identification of candidate clones and sequencing

(Liu et al., 1996, Akkaya et. al., 1992; Morgante et al., 1993). While improved SSR

enrichment methods reduce marker development costs, they still require some time-

consuming steps for the development (Kumpatla et al., 2004).Computational

approaches provide an attractive alternative to conventional laboratory methods for

rapid and economical development of SSR markers by utilizing freely available

sequences in public databases (Varshney et al., 2002).

Expressed Sequence Tag (EST) databases received much attention as potentially

valuable resources for the development of molecular markers for population genetics

studies and gene discovery due to increasing amounts of ESTs being deposited in

databases for various plants. Publicly-available EST collections are a largely

unexplored source of expression data (Ewing et al., 2000). Currently there are more

than 2 million ESTs available for major monocotyledonous species and more than 1.5

million ESTs for dicots.The usefulness of EST-SSR markers arises from their close

linkage to potentially important genes, helping to identify candidate genes for

quantitative trait loci (QTL).

Expressed Sequence Tags (ESTs) are short sequence reads, typically within the range

of 100–700 bp, obtained from randomly selected cDNA clones. ESTs are often

generated by single pass sequencing of cDNA clones from one or both ends, usually

covering only a part of the transcript sequence, and are relatively prone to error

(~3%). Despite this limitation, EST sequencing represents a main stream

methodology for gene surveying. Even in these days, when whole genome sequences

are available for many organisms, ESTs continue to play an important role in gene



identification, gene expression studies, transcript mapping, description of the

transcriptional activity of a tissue/cell type, evidence for gene prediction and an

abundant resource of molecular markers for physical mapping (Gruber et al.,2006). In

particular, ESTs provide valuable resources to develop gene-associated SSR markers.

Since EST-SSR markers are derived from expressed genes, they are more conserved

and have a better potential for applications such as identifying conserved genomic

regions among species and genera, comparative genomics, and evolutionary studies

(Thiel et al., 2003; Eujayl et al., 2004; Ukoskit et al., 2012). Also, due to their

existence in transcribed regions of genomic DNA, they can lead to the development of

gene-based maps which may help to identify candidate function genes and increase

the efficiency of marker-assisted selection (Liang et al., 2009). Moreover, the

development of SSR (microsatellite) markers from genomic libraries is expensive and

inefficient, while development of SSR markers through data mining has become a

fast, efficient, and low-cost option for many plants (Eujayl et al., 2004).

Bioinformatics approaches are increasingly being used for molecular marker

development since the sequences from many genomes are made freely available in the

public databases (Gu et al., 1998; Kantety et al., 2002; Varshney et al., 2002).

Additionally, bioinformatics tools also supplement existing approaches by automating

the task of SSR identification from available DNA sequences. Moreover, recent

studies have observed that the frequency of microsatellites was significantly higher in

ESTs than in genomic DNA in several plant species investigated (Morgante et al.,

2002; Toth et al., 2000). Because of the above advantages, SSR markers have been

developed from ESTs in various crops including A. thaliana (Delseny et al., 1999),

Sugarcane (Ukoskit et. al., 2012), Medicago truncatula (Eujayl et al., 2004), Barley

(Thiel et al., 2003), Rubber (Li et al., 2012), Cotton (Han et al., 2006), Capsicum

(Ince et al., 2010), Oil palm (Billotte et al., 2001), Grasses (Kantety et al., 2002) and

J. curcas (Kumar et al., 2011; Wen et al., 2010). Around 1% to 5% of the ESTs

contain SSRs, hence these SSRs have become the marker class of choice for

molecular mapping and plant breeding studies (Eujayl et al., 2004).

Jatropha curcas L. is promoted as non edible biodiesel crop worldwide. Though SSRs

are markers of choice in many plant species, only a very limited number of SSR



markers are publicly available for Jatropha curcas. The two major limiting factors in

the use of molecular markers for quantitative trait locus (QTL) analysis and marker-

assisted selection programs in Jatropha are: 1) the limited number of suitable markers

available in the public sector, and 2) the lack of knowledge of how these markers are

associated with economically important QTLs (traits). The use of EST database for

marker development will provide a promising tool to enhance molecular and genomic

research in Jatropha.

In this study, we have characterized informative SSR markers from a large collection

of EST (42,483 ESTs) using EST database of Jatropha curcas, which should provide

a clear picture of repeat types, number of repeats, frequency and distribution of the

EST- SSRs in Jatropha curcas. These EST-SSR markers will enrich the current

resource of molecular markers for Jatropha community and would be useful for

qualitative and quantitative trait mapping, marker-assisted selection, and genetic

diversity study in Jatropha as well as related plant species.

5.2 METHODS AND MATERIALS

5.2 Methodology

5.2.1 Dataset

J. curcas sequences used in this study were obtained from NCBI’s EST Database

(http://www.ncbi.nlm.nih.gov/nucestterm=jatropha%20curcas[organism]). These EST

sequences were collected in FASTA format for the identification of SSRs.

5.2.2 SSR analysis:

Perfect mono-, di-, tri-, tetra-, penta-, and hexa-nucleotide motifs with arepeat of ≥6

times were identified using the software WebSat, The Web Static Analyzer Tool

(http://wsmartins.net/websat/), an SSR repeat finder, along with Primer3, PCR primer

design program, into one pipeline tool (Martins et al., 2009). The EST sequences from

Jatropha curcas database were downloaded and entered in the WebSat software. As

the program can process 150,000 characters, multiple FASTA formatted sequences

were processed for SSR analysis at a time. The output generated by the program

highlights the SSR sequences in yellow color (Fig 5.1).



Fig 5.1: Identification of SSR markers using Websat. SSR markers are highlighted with

yellow color. Arrow shows type of SSR

The analysis of occurrence and frequency of SSRs among EST sequences was carried

out by exporting the WebSat results to Microsoft Excel spreadsheets.

5.2.3 Marker Development

The 3286 SSR containing sequences were subsequently analyzed for primer designing

with the WEBSAT (http://wsmartins.net/websat) which uses primer3 software.

Flanking DNA sequences was analyzed for the presence of suitable specific forward

and reverse primers to assay the SSR loci (Robinson, 2004).

The parameters set for primer design were; Primer Size Min: 18 bp, Optimum Primer

Size: 22 bp , Primer Size Max: 27 bp , Primer Tm Min: 57.0°C, Optimum Primer Tm:

60.0°C, Primer Tm Max: 68.0°C, Primer GC% Min: 40.0, Primer GC% Max: 80.0,

Max Tm Difference: 1.00 and Product Size: 100 – 400 bp.

If primer design is successful and a pair of primers is designed, they are colored green

along with the SSR in blue. If not, a message reporting the failure of primer design

appears (Fig 5.2).



Fig 5.2: Output of WebSat software. SSR in highlighted in blue and primer specific

for that SSR shown in green

The file can be easily visualized in a spread sheet program, by using the option to

import external data in CVS (MS excel) file, with following fields for each SSR: the

sequence identification, SSR, product size, forward and reverse primer sequence,

melting temperature, and coordinates of the primers within the sequence (Fig 5.3).

Fig 5.3: WebSat export file in MS excel format



5.2.4 Compositional analysis of SSR mining results

The analysis of occurrence and frequency of SSRs was carried out by exporting the

WebSat results to Microsoft Excel spread sheets. Results on repeat types, number of

repeats as well as frequency were first collected using a combination of sorting and

counting functions. The final results were tabulated.

5.2.5 Insilico validation of designed EST SSR primers

For in silico validation of primers, the web software NetPrimer

(http://www.premierbiosoft.com/netprimer/index.html) was explored. All primers

were analyzed for primer melting temperature and also for all primer secondary

structures including hairpins, loops, self-dimers, and cross-dimers in primer pairs, to

ensure the availability of the primer for the reaction as well as minimizing the

formation of primer dimer. A comprehensive analysis report was generated for

individual primers or primer pairs.

5.3 RESULT AND DISCUSSION

5.3.1 SSR analysis:

Simple sequence repeats have proven to be highly abundant and uniformly distributed

in human and other mammalian genomes (Weber et al., 1989). Several studies have

demonstrated the occurrence, distribution, informativeness and Mendelian inheritance

of SSRs in plant genomes (Wang et al., 1994; Anon, 2004). It has also been reported

that SSRs occur as frequently as once in about 6 kb in case of plant genomes (Cardle

et al., 2000). Studies on several plant genomes have also demonstrated that the

frequencies of SSRs were significantly higher in ESTs than in genomic DNA

(Morgante et al., 2002). The knowledge of the occurrence and frequency of different

types of SSRs in different genomes is valuable for an understanding of their

distribution and also in developing SSR markers for genetic analysis and diagnostics.

In this study, we have characterized informative SSR markers from a large collection

of EST (42,483 ESTs), which provides a clear picture of occurrence, distribution, and

informativeness of the EST- SSRs in Jatropha curcas. In order to assess the

frequency of SSRs in EST sequences of J. curcas, percentages of SSR-containing



ESTs were calculated. A total of 3682 ESTs contained SSRs (8.66% of the total

42,483 ESTs). This is a relatively higher abundance of SSRs for Jatropha ESTs,

compared to the previous reports for maize (1.4%), barley (3.4%), wheat (3.2%),

sorghum (3.6%), rice (4.7%) (Kantety et al.,2002), Medicago truncatula (3.0%)

(Eujayl et al., 2004) and Peanut (6.8%) species (Liang, 2009). Studies on the

abundance of SSRs in monocots revealed that SSRs were present in about 7% to 10%

(Varshney et al., 2002) of the total ESTs. Kumpatla et al., 2005 analysed 1.5 million

ESTs derived from 55 dicotyledonous species and found that 2.6 to 16.8% of ESTs

contained at least one SSR. The observed frequencies of ESTs containing SSR in

several of the dicotyledonous species are much higher (as many species contain more

than 10%) as compared to monocots (Anon, 2004). Two most likely reasons for these

observations are: (i) the frequency estimates in some species may not represent the

actual values due to the availability of smaller number of ESTs in public database and

(ii) several of the ESTs in species with high frequency of SSR-ESTs may be

redundant.

The relative abundance of mono-, di-, tri- and tetra-nucleotide repeats were

determined by calculating their frequencies in ESTs containing single SSR stretches.

According to the repeat motif classification criteria we divided the SSRs into three

groups: perfect, imperfect and compound types (Weber, 1990). Table 5.1 shows these

different classes of SSRs and their frequencies in EST database of J. curcas. Most

repeats (SSRs ≥20bp: 673, 20.72%; SSRs 12-20bp: 1777, 54.72%) were perfect

repeats. Of these, mono and di-nucleotide repeats were the most abundant motif type.

In the imperfect and compound SSR categories, only mono-, di- and tri-nucleotide

SSR units were present. Most of repeat motifs in mono-nucleotide SSR units were of

the A/T type. AG/CT, GA/TC and AT/TA repeat motif types were present in di-

nucleotide SSR units, while AAG/AGA/GAA/CTT/TTC/TCT repeat motifs were

found in tri-nucleotide SSR units. Of the six types of SSR units, mono-mono, di-di-,

tri-tri-, mono-di-, mono-tri- and di-tri-nucleotide types were found in both perfect and

imperfect compound SSR categories. The distribution of different types of EST- SSR

is shown in Figure 5.4; the numbers inside them indicate the actual numberand % of

sequences of that particular category.



The SSR loci were categorized into two groups based on the length of their SSR

tracts: class ISSRs, 12 to 20 nucleotides in length and class II, containing perfect

SSRs>20 nucleotides in length (Fig. 5.5). Dinucleotide was the most abundant repeat

motif in both Class I (722,41%) and class II (269,40%) category. The class I repeats

were largely composed of 49% mononucleotide, 41% dinucleotide and 10%

trinucleotide repeats, whereas Class II repeats have 40% dinucleotide, 35%

trinucleotide, 23% mononucletide, 2% pentanucleotide and 1% tetra-hexanucleotide

repeats.

Table 5.1: The total number of EST-SSRs identified in J. curcas

SSR Markers

<10

Nucleo

- tides

10-11

Nucleo -

tides

12-20

Nucleo -

tides

>20

Nucleo

- tides

Total

1) Interrupted

a) Compound - - 2 269 271

b) Simple - - 2 104 106

2) Non-interrupted

a) Imperfect - - 1 57 58

b) Perfect

· Mono - nucleotide - 781 869 157 1087

· Di - nucleotide - - 722 269 991

· Tri - nucleotide - - 186 232 418

· Tetra - nucleotide - - - 6 6

· Penta - nucleotide - - - 2 2

· Hexa - nucleotide - - - 7 7

3) Overlapped 2 - 12 2 16

TOTAL 2 781 1794 1105 3682



Fig 5.4: Distribution of SSR Markers mined from EST Database of Jatropha curcas L

Fig 5.5 Comparative distribution of different repeat motifs (a) Class I

Fig 5.5 (a): Comparative distribution of different repeat motifs (a) Class II

271, 7% 106, 3%58, 2%

3231, 88%

Interrupted, Compound

Interrupted, Simple

Non-interrupted, Imperfect

Non-interrupted, Perfect



5.3.2 Compositional analysis of SSRs

Computational mining and analysis of SSRs in ESTs of some cereal species revealed

that trimeric repeats are the most abundant class followed by dinucleotide repeats

(Varshney et al., 2002). As a general trend in dicotyledenous species, dinucleotidesis

the most abundant repeats (Anon, 2004). In our present study on J. curcas

mononucleotide repeats formed the largest group which may be because several of the

ESTs in GenBank still contain polyA / polyT stretches at their ends due to lack of

processing prior to deposition in GenBank. Thus, during SSR mining the As and Ts at

the ends of ESTs would be identified by the WebSat as mononucleotide repeats.

Mononucleotide repeats were followed by dinucleotides which were further followed

by trinucleotides repeats.

Theoretically, the probability of finding mononucleotide repeats in a genome is higher

followed by dinucleotide repeats and then by trinucleotide repeats followed by

tetranucleotide repeats. The results observed for ESTs in Jatropha curcas (Table 5.1)

show this trend, in which the mononucleotide repeats formed the largest group

(55.7%) (Fig 5.5). Dinucleotides was the second largest group (30.5%) (Fig 5.6). This

was followed by trinucleotides (13.2%), tetra and hexanucleotides (0.21%) and

pentanucleotides (2 SSRs) (Fig 5.7). Similar results are reported by Anon, 2004, in

Allium cepa, Hevea brasiliensis, Linum usitatissimum, Phlomis armeniaca, Capsicum

annuum, Gossypium arboreum, Gossypium hirsutum and, Medicago truncatula. On

the other hand, in some plant species like Coffea Arabica and Lactuca sativa

trinucleotide repeats are the most abundant class. Whereas in Mentha piperita, di- and

tri-nucleotide repeats are observed in equal proportions while the mononucleotide

repeats are predominant class (Anon, 2004).

In this study, a total of 3682 SSRs were identified, i.e, SSRs exist in 8.6 % of EST

sequences, in which the mononucleotide repeats formed the largest group (55.7%)

consisting of 95.1% A/T and 4.9% G/C motifs (Fig 5.6). Dinucleotides was the

second largest group (30.5%) consisting of 42.5% AG/CT, 17.3% AT/TA, 4.3%

AC/TG, 34.8% TC/GA and 1.1% GT/CA motifs (Fig 5.7). This was followed by

trinucleotides (13.2%) (Fig 5.4), tetra- hexanucleotides (0.21%) and penta nucleotides

(2 SSRs).



The available SSR motif combinations could be grouped into unique classes based on

the property of DNA base complementarities. For mononucleotides, although A, T, C

and G are possible, A and T could be grouped into one since an A repeat on one

strand is same as a T repeat on the opposite strand and a poly C on one strand is the

same as a poly G on the opposite strand, resulting in two unique classes of

mononucleotides, A/T and C/G (Katti et al., 2001). Similarly, all dinucleotides can be

grouped into four unique classes: (i) AT/TA; (ii) AG/GA/CT/TC; (iii) AC/CA/TG/GT

and (iv) GC/CG. Thus, the number of unique classes possible for mono-, di-, tri- and

tetra-nucleotide repeats is 2, 4, 10 and 33, respectively (Katti et al., 2001; Jurka et al.,

1995). Figure 5.6 shows the frequencies of A/T and C/G repeats. It is clear that A/T

repeats are the predominant mononucleotides as A/T SSRs represent more than 95%

of the total mononucleotide SSRs in J. curcas.

Fig 5.6: Frequencies of mononucleotide SSRs in ESTs of Jatropha curcas L

Relative frequencies of four unique classes of dinucleotide repeats are shown in

Figure 5.7. Out of the dinucleotide repeats, AG/GA/CT/TC group is the predominant

class of dinucleotide repeats followed by AT/TA as the second most abundant

dinucleotide repeat typein J. curcas. The AG/GA/CT/TC is the predominant class of

repeats is in concurrence with the results observed by Varshney et al., 2002, in some

cereal species and Anon, 2004, in dicot species. In contrast, SaiSug et al., 2013,

recently reported that for the dinucleotide motif sequences, the TC motif was the most

common followed by CT and AT motifs, whereas the AC motif was the least common

95%

5%

Mononucleotide Frequency

A/T

G/C



in J.curcas EST. However, the second most abundant repeat observed by Varshney et

al., 2002, was AC repeat (same as AC/CA/TG/GT group in the present study),

whereas repeat observed by Anon, 2004, and also in present investigation AT/TA is

the second most frequent repeat observed following AG/GA/CT/TC.

Fig 5.7: Frequencies of dinucleotide SSRs in ESTs of Jatropha curcas L

An analysis of the frequencies of trinucleotide repeats out of total SSRs observed

indicate the predominance of AAG/AGA/GAA/CTT/TTC/TCT repeat class,while

AAT/ATT/ATA/TTA/TAA/TAT is the second frequent repeat class observed. The

number of tetra- penta- hexanucleotide repeats observed in our study is low, that are 6

SSRs, 2 SSRs and 7 SSRs, respectively.

Varshney et al., 2002, observed that the CCG trinucleotide repeat (belongs to the

GGC/GCG/CGC/GCC/CCG/CGC class) is the most predominant SSR in cereal

species. However, this repeat is not the predominant class in J. curcas investigated

here for which large numbers of ESTs are available. Thus in terms of the abundance

of motif types, our study agrees to that of Ueno et al., 2008, 2009, and other studies

performed in dicotyledonous species (reviewed by Kumpatla et al., 2005), in which

AG and AAG were the most abundant di- and trimeric SSRs, respectively. The

extremely low number of SSR motifs containing C and G (0 CGs out of 991 dimeric

SSRs and 3 CCGs out of 428 trimeric SSRs) could be attributed to the composition of

dicot genes being less rich in G+C compared to monocots due to codon usage bias

18%

77%

5%

0%

Dinucleotide Frequency

AT/TA

AG/GA/CT/TC

AC/CA/TG/GT

GC/CG



(Morgante et al., 2002) and to the intrinsic negative correlation between GC content

and slippage rate (Schlotterer et al., 1992).

Fig 5.8: Frequencies of trinucleotide SSRs in ESTs of Jatropha curcas L

One of the important features of SSRs markers that make them ideal candidates for

genetic analysis is their highly polymorphic nature, i.e., a large number of allelic

variants are possible across different genotypes (Akkaya et al., 1992; Powell et al.,

1996). Knowledge of the distribution of SSRs into different repeat length classes is

useful in assessing the abundance of potentially informative markers. It is a general

experience in molecular genetics community that the utility or informativeness of

SSRs increases with increased number of repeats in a given SSR stretch. For example,

di- and tri-nucleotide repeats with 5 or more repeats are very likely to be informative

compared to 2-4 repeats. This is the reason behind choosing 6 repeats as the minimum

criteria for di- and trinucleotide repeats mining using WebSat program. With respect

to the distribution of mono-di-trinucleotide SSR distribution into repeat length falls in

the classes10-24 for mononucleotide, 6-25 for dinucleotides and 6-13 for trinucleotide

repeats.

19%

6%

2%

8%

9%34%

7%

14%

0% 1%

Trinucleotide Frequency

AAT/ATT/ATA/TTA/TAA/TATAAC/ACA/CAA/GTT/TTG/TGTAGT/GTA/TAG/ACT/TCA/CATAGC/GCA/CAG/GCT/CTG/TGCACC/CCA/CAC/GGT/GTG/TGGAAG/AGA/GAA/CTT/TTC/TCTATG/TGA/GAT/CAT/ATC/TCAAGG/GGA/GAG/CTC/TCC/CCT



In silico validation of designed EST SSR primers

Based on the 3682 SSR-containing ESTs identified, a total of 2236 primers were

successfully designed and used for the validation of the amplification in in silico

condition. All the 2236 primers were validated in silico with NetPrimer. From this

validation studies, 93 primers were such that which does not contain hairpin loops,

self-primers and cross primers. Out of which 4 were interrupted EST-SSR primers

and the rest 89 were non-interrupted EST-SSR primers (Table 5.2).

Thus, the distribution analysis of SSRs in ESTs of J.curcas species clearly indicates

the abundance of mononucleotide SSRs containing 10-24 repeats and di- and tri-

nucleotide SSRs containing, 6-25 repeats and 6-13 repeats, respectively. This

information coupled with the frequencies of different types of mono-, di- and tri-

nucleotide motifs detailed before demonstrates that ESTs are a rich source of SSRs

towards marker development for genetic analysis in Jatropha curcas. Results of this

study give a sight into the type, distribution, frequency of EST-SSRs and development

of EST-SSR markers in J.curcas. These EST-SSR markers would enrich the current

resource of molecular markers for Jatropha community and will be useful for

qualitative and quantitative trait mapping, marker-assisted selection, and study of

genetic diversity in Jatropha as well as related plant species.



5.4 REFERENCES

Akkaya, M.S., Bhagwat, A.A. and Cregan, P.B. (1992) Length polymorphisms of

simple sequence repeat DNA in soybean. Genetics 132: 1131-1139

Anon, 2004. Computational Mining And Survey Of Simple Sequence Repeats (

SSRs) In:Kumpatla, S.P (Eds.) Expressed Sequence Tags ( ESTS ) of dicotyledonous

plants.www.https://scholarworks.iupui.edu/handle/1805/333

Billotte, N., Risterucci, A.M., Barcelos, E., Noyer, J.L. Amblard, P and Baurens, F.C.

(2001) Development, characterisation, and across-taxa utility of oil palm (Elaeis

guineensis Jacq.) microsatellite markers. Genome 44: 413-425.

Cardle, L., Ramsay, L., Milbourne, D., Macaulay, M., Marshall, D. and Waugh, R.

(2000) Computational and experimental characterization of physically clustered

simple sequence repeats in plants. Genetics 156: 847–854

Delseny, M. (1999) Genomics: methods and early results.Ol.Corps Gras Lipides.

6:136-143.

Eujayl, I., Sledge, M., Wang, L., May, G., Chekhovskiy, K., Zwonitzer, J. and Mian,

M. (2004) Medicago truncatula EST-SSRs reveal cross species genetic markers for

Medicago spp. Theorital and Applied Genetics 108: 414–422

Ewing, R.M. and Claverie, J.M. (2000) EST databases as multi-conditional gene

expression datasets. Pacific Symposium on Biocomputing 5: 427-439

Gu, Z., Hillier, L. and Kwok, P.Y. (1998) Single nucleotide polymorphism hunting in

cyberspace. Human Mutation 12: 221-225

Han, Z., Wang, C., Song, X., Guo, W., Gou, J., Li, C., Chen, X. and Zhang, T. (2006)

Characteristics, development and mapping of Gossypium hirsutum derived EST-SSRs

in allotetraploid cotton. Theorital and Applied Genetics 112: 430–439



Ince, A., Karaca, M. and Onus, A. (2010) Polymorphic microsatellite markers

transferable across Capsicum species. Plant Molecular Biology Reporter 28: 285–291

Jurka, J. and Pethiygoda, C. (1995) Simple repetitive DNA sequences from primates:

compilation and analysis. Journal of Molecular Evolution 40: 120-126

Kantety, R.V., La Rota, M., Matthews, D.E. and Sorrells, M.E. (2002) Data mining

for simple sequence repeats in expressed sequence tags from barley, maize, rice,

sorghum and wheat. Plant Molecular Biology48: 501-510.

Katti, M.V., Ranjekar, P.K. and Gupta, V.S. (2001) Differential distribution of simple

sequence repeats in eukaryotic genome sequences. Molecular Biology Evolution 18:

1161-1167

Kumar, Y.H., Ranjan, A., Asif, M., Mantri, S., Sawant, S. and Tuli, R. (2011) EST-

derived SSR markers in Jatropha curcas L.: development, characterization,

polymorphism, and transferability across the species/genera. Tree Genetics and

Genomes 7: 207–219

Kumpatla, S.P. and Mukhopadhyay, S. (2005) Mining and survey of simple sequence

repeats in expressed sequence tags of dicotyledonous species. Genome 48(6): 985-998

Kumpatla, S.P., Manley, M.K., Horne, E.C., Gupta, M. and Thompson, S.A. (2004)

An improved enrichment procedure to develop multiple repeat classes of cotton

microsatellite markers. Plant Molecular Biology Reporter 22: 85a-85i

Li, D., Deng, Z., Qin, B., Liu, X. and Men, Z. (2012) De novo assembly and

characterization of bark transcriptome using illumine sequencing and development of

EST-SSR markers in rubber tree (Hevea brasiliensis Muell. Arg.). BMC Genomics

13: 192

Liang, X., Chen, X., Hong, Y., Liu, H., Zhou, G., Li, S. and Guo, B. (2009) Utility of

EST-derived SSR in cultivated peanut (Arachis hypogaea L.) and Arachis wild

species. BioMedCentral 9: 35



Liu, Z.W., Biyashev, R.M. and Maroof, M.A. (1996) Development of simple

sequence repeat DNA markers and their integration into a barley linkage map.

Theorital and Applied Genetics. 93: 869-876

Morgante, M. and Olivieri, A.M. (1993) PCR-amplified microsatellites as markers in

plant genetics. Plant Journal 3: 175-182

Morgante, M., Hanafey, M. and Powell, W. (2002) Microsatellites are preferentially

associated with nonrepetitive DNA in plant genomes. Nature Genetics 30: 194-200

Powell, W., Machray, G.C. and Provan, J. (1996) Polymorphism revealed by simple

sequence repeats. Trends in Plant Sciences1: 215-222

Rafalski, J.A. and Tingey, S.V. (1993) Genetic diagnostics in plant breeding: RAPDs,

microsatellites and machines. Trends in Genetics9: 275-280

Robinson, A.J., Love, C.J., Batley, J., Barker, G. and Edwards, D. (2004) Simple

sequence repeat marker loci discovery using SSR primer. Bioinformatics 20: 1475–

1476

Schlotterer, C and Tautz, D (1992) Slippage synthesis of simple sequence DNA.

Nucleic Acids Research 20: 211-215

Sobreira, T.J., Durham, A.M. and Gruber, A. (2006) TRAP: automated classification,

quantification and annotation of tandemly repeated sequences. Bioinformatics 22:

361-362

Tautz, D. (1989) Hypervariability of simple sequences as a general source for

polymorphic DNA markers. Nucleic Acids Research 17: 6443-6471

Thiel, T., Michalek, W., Varshney, R. and Graner, A. (2003) Exploiting EST database

for the development and characterization of genederived SSR-markers in barley

(Hordeum vulgare L.).Theoritical and Applied Genetics 106: 411–422



Ueno, S. and Tsumura, Y. (2008) Development of ten microsatellite markers for

Quercus mongolica var. crispula by database mining. Conservation Genetics 9: 1083-

1085

Ueno, S., Aoki, K. and Tsumura, Y. (2009) Generation of expressed sequence tags

and development of microsatellite markers for Castanopsis sieboldii var. sieboldii

(Fagaceae). Annals of Forest Science 66: 509-509

Ukoskit, K., Thipmongkolcharoen, P. and Chatwachirawong, P. (2012) Novel

expressed sequence tag- simple sequence repeats (EST SSR) markers characterized by

new bioinformatic criteria reveal high genetic similarity in sugarcane (Saccharum

spp.) breeding lines. African Journal Biotechnology 11: 1337–1363

Varshney, R.K., Thiel, T., Stein, N., Langridge, P. and Graner, A. (2002) In silico

analysis on frequency and distribution of microsatellites in ESTs of some cereal

species. Cell and Molecular Biology Letters 7: 537-546

Wang, Z., Weber, J.L., Zhong, G. and Tanksley, S.D. (1994) Survey of plant short

tandem DNA repeats. Theoritical and Applied Genetics 88: 1-6

Weber, J.L (1990) Informativeness of human (dC-dA)n (dG-dT)n polymorphisms.

Genomics 7: 524–530.

Weber, J.L. and May, P.E. (1989) Abundant class of human DNA polymorphisms

which can be typed using the polymerase chain reaction. American Journal of Human

Genetics 44: 388-396.

Wen, M., Wang, H., Xia, Z., Zou, M., Lu, C. and Wang, W. (2010) Developmenrt of

EST-SSR and genomic-SSR markers to assess genetic diversity in Jatropha curcas L.

BMC Research Notes 3: 42

79

Tab

le 5

.2:

Lis

t of

SS

R P

rim

ers

des

ign

ed f

rom

ES

T D

ata

base

of

J. cu

rcas

an

d

in s

ilic

o v

ali

date

d w

ith

Net

Pri

mer.

S.N

o.

Ge

nB

an

k

Acc

ess

ion

No

.

Ty

pe

of

Re

pe

at

Fo

rwa

rd P

rim

er

(5’-

3’)

R

ev

ers

e P

rim

er

(5’-

3’)

P

rod

uct

Siz

e

Non

In

terr

up

ted

ES

T-S

SR

Pri

mer

s

1

GI|

31

12

13

41

3

(T)1

7

TG

CT

GT

TC

TA

CT

GG

TC

TG

CT

GT

T

GC

CC

GT

TT

TC

CT

TC

TA

TC

TT

A

26

1

2

GI|

31

11

96

82

2

(CT

T)6

C

CC

TG

TT

TC

CC

AT

TA

GT

CT

CT

G

CT

CT

GT

TT

GC

TT

CC

TT

CA

TT

CC

2

73

3

GI|

31

11

96

54

9

(TC

)6

CT

GT

TT

CA

CT

GC

TC

TG

TG

GA

CT

G

CC

AC

CC

TT

TT

CT

TA

TC

CA

TT

A

37

3

4

GI|

31

11

96

42

9

(CT

)14

C

TC

TC

TC

TC

CT

TC

AC

CA

TC

AC

C

GG

CA

TC

AC

AC

AT

TC

TA

AA

AC

GA

3

06

5

GI|

31

11

91

46

4

(AG

)19

C

AC

CC

AA

AA

CC

TC

TG

CT

AA

AA

C

TC

TC

TG

CC

AA

AA

TC

AT

CA

TC

AC

2

87

6

GI|

31

11

96

89

7

(TC

)8

TC

CT

TC

CA

AC

TC

CT

GC

TT

TT

AC

C

GG

AC

TT

TC

TC

TC

TG

TT

CT

CG

T

27

2

7

GI|

31

11

96

82

2

(CT

T)6

C

CC

TG

TT

TC

CC

AT

TA

GT

CT

CT

G

CT

CT

GT

TT

GC

TT

CC

TT

CA

TT

CC

2

73

8

GI|

31

11

96

42

9

(CT

)14

C

TC

TC

TC

TC

CT

TC

AC

CA

TC

AC

C

GG

CA

TC

AC

AC

AT

TC

TA

AA

AC

GA

3

06

9

GI|

31

11

91

46

4

(AG

)19

C

AC

CC

AA

AA

CC

TC

TG

CT

AA

AA

C

TC

TC

TG

CC

AA

AA

TC

AT

CA

TC

AC

2

87

10

G

I|3

11

18

00

17

(C

T)8

T

TA

CT

CC

TC

CA

TC

TC

CC

TC

CT

T

CC

TC

CG

TT

TC

TT

TC

CT

TC

TT

TT

1

96

11

G

I|3

11

17

95

75

(A

)15

G

AG

TC

TG

GG

TG

CT

TT

GA

TT

CT

T

AG

TC

TC

CT

TT

TC

TT

CT

CG

GG

TT

3

71

12

G

I|3

11

17

52

43

(T

C)6

C

AA

AG

CA

GA

GA

AT

GA

GC

AA

GT

G

AT

GA

AG

CA

AG

AA

CC

CT

GA

GA

AG

2

12

13

G

I|3

11

17

24

99

(T

AG

)7

GA

GT

CA

AA

AG

GT

GG

GA

AG

AA

GA

T

AG

TC

AG

GA

AA

TA

GC

AG

TC

GC

A

29

1

14

G

I|3

11

16

38

99

(C

T)6

A

AC

CC

AT

TA

TC

CC

AC

TA

AA

CC

C

CC

AT

TT

CT

TG

CT

CT

TA

CC

CA

TC

2

96

15

G

I|3

11

16

38

94

(A

G)6

A

CG

GG

GA

AG

AG

AA

GG

AA

TA

AA

G

CG

CA

CA

TC

AA

GA

AA

CA

AG

AA

GT

2

45

16

G

I|3

11

16

38

01

(A

)10

A

CC

AC

TC

CT

AC

TA

CA

AC

GA

CT

AT

CT

G

TA

AC

AC

AC

AC

CA

AT

CT

CC

CA

AC

3

88

17

G

I|3

11

16

31

54

(A

)14

A

AC

AG

CA

AG

CG

AA

CA

AG

GA

G

AG

TA

GA

TG

GA

TG

AA

AG

GA

CC

GA

1

05

18

G

I|3

11

16

31

48

(A

G)1

0

AG

AG

AG

AG

AA

AA

GC

GG

AA

GG

AT

A

GA

AG

AA

GA

CG

AA

CT

GG

AG

GT

G

25

8

19

G

I|3

11

16

28

61

(A

)11

A

TG

GA

TG

AA

TG

TG

CT

TT

GT

GT

C

TT

GG

AA

CT

CT

GA

AG

GA

TG

GA

AT

1

64

20

G

I|3

11

16

28

57

(A

G)6

G

CA

AG

GA

GG

GA

GA

TT

TT

GT

TT

T

TG

AT

TT

AG

CG

AG

AG

AG

AG

AG

GG

2

02

21

G

I|3

11

16

27

61

(T

)13

T

CT

TT

CT

CT

TC

TC

TC

CA

CT

GC

C

CT

CT

TC

CC

TT

TC

AT

TT

GG

TC

TG

1

80

22

G

I|3

11

16

12

67

(T

CT

)7

GC

AT

CT

TT

CT

CT

TT

CA

TC

TT

CG

A

TC

TA

AC

AC

TT

GC

CC

AC

CA

CT

A

14

8

23

G

I|3

11

16

12

08

(T

C)6

C

CG

TT

TC

GC

TC

TT

GT

CA

TC

TA

C

GT

TG

CC

AT

TG

TC

GT

TA

TT

TC

CT

3

95

24

G

I|3

11

15

88

75

(C

AA

)10

A

CA

AA

CA

GA

AA

GA

GC

GA

TG

GA

T

GT

GT

AA

GA

GG

TG

GA

AG

TG

GG

AG

3

19

25

G

I|3

11

15

87

25

(T

)13

G

GC

TT

TG

TG

CT

CT

GG

AG

AT

TA

G

TC

CT

TT

TA

CA

CT

GG

TG

GG

TT

TC

3

93

79

26

G

I|3

11

15

85

36

(A

)10

T

GT

TT

TC

TC

GT

AT

TG

GG

GT

TC

T

AG

TA

TC

AT

TC

CT

GC

TG

GT

TG

GT

2

31

27

G

I|3

11

15

83

12

(C

T)1

4

CC

TC

TC

GT

TT

CA

TT

TC

TC

TC

GT

C

TC

TT

AC

CC

AC

TC

GT

CC

TT

CA

C

16

8

28

G

I|3

11

15

83

00

(A

AG

)7

CG

AA

AA

CA

AA

AG

CA

GA

TT

AG

GG

C

AA

AC

AA

AA

CG

GA

AA

CA

GA

GT

G

33

1

29

G

I|3

11

15

70

97

(T

C)8

G

GG

TC

CT

TT

CA

CC

TC

TC

TC

TC

T

GC

TC

CT

CT

CC

CA

AT

CT

GT

TC

TA

3

06

30

G

I|3

11

15

70

93

(T

)20

T

CA

TC

TC

CA

TA

CC

CA

CA

TA

CA

CA

C

GA

AT

CA

AG

AA

GA

GT

CA

AA

GC

A

19

1

31

G

I|3

11

15

69

13

(C

AA

)10

A

CA

AA

CA

GA

AA

GA

GC

GA

TG

GA

T

GT

GT

AA

GA

GG

TG

GA

AG

TG

GG

AG

3

19

32

G

I|3

02

37

02

91

(C

AA

)6

AC

CA

AG

AC

CA

GT

TT

TA

CC

TC

CA

T

CC

CT

CA

AC

CG

TC

AT

AG

AT

TT

T

38

0

33

G

I|3

02

37

02

06

(C

T)1

0

TG

TG

TT

CG

TG

TG

TT

CG

TG

TC

T

AT

AG

TT

GT

TG

CC

TT

CA

TT

GC

CT

1

59

34

G

I|3

02

37

00

37

(T

CT

)6

CA

GT

TC

TC

AC

AG

TT

CT

TA

GC

CG

C

GC

AT

AC

TA

CC

CT

CT

CG

TT

CT

T

29

8

35

G

I|3

02

36

93

52

(G

CA

)7

AG

TG

GG

TG

GA

TG

TT

GA

GA

GA

GT

G

GA

TT

TA

GA

GA

AT

GC

TT

GT

GC

C

25

4

36

G

I|3

02

36

89

85

(C

T)1

3

CT

CT

CT

CT

CC

TT

CA

CC

AT

CA

CC

G

GC

AT

CA

CA

CA

TT

CT

AA

AA

CG

A

30

4

37

G

I|3

02

36

79

21

(T

C)6

C

AA

TA

CG

AA

CG

AG

AG

AG

AG

CA

G

AT

TT

CC

AT

CA

AC

TT

TC

AC

CC

AC

2

33

38

G

I|3

02

36

73

09

(C

TC

)6

AT

GG

GT

AG

GA

AA

GG

AA

AA

TG

GT

C

AG

CA

GC

AA

CT

GG

AA

CA

GA

AT

A

28

3

39

G

I|3

02

36

69

32

(A

G)1

4

AG

AG

AG

AG

AA

AA

GC

GG

AA

GG

AT

A

GA

AG

AA

GA

CG

AA

CT

GG

AG

GT

G

26

0

40

G

I|3

02

36

55

97

(G

CA

)7

AG

TG

GG

TG

GA

TG

TT

GA

GA

GA

GT

G

GA

TT

TA

GA

GA

AT

GC

TT

GT

GC

C

25

4

41

G

I|3

02

36

21

80

(T

AG

)7

GA

GT

CA

AA

AG

GT

GG

GA

AG

AA

GA

T

AG

TC

AG

GA

AA

TA

GC

AG

TC

GC

A

29

1

42

G

I|3

02

36

17

28

(T

AG

)7

AA

AG

GA

GT

CA

AA

AG

GA

GG

GA

AG

T

AG

TC

AG

GA

AA

TA

GC

AG

TC

GC

A

29

5

43

G

I|3

02

36

15

53

(G

CA

)7

AG

TG

GG

TG

GA

TG

TT

GA

GA

GA

GT

G

GA

TT

TA

GA

GA

AT

GC

TT

GT

GC

C

25

4

44

G

I|3

02

35

93

00

(A

AG

A)6

A

GG

GA

CA

GC

AG

CA

GA

AG

AA

G

GA

CG

AA

GG

AG

AG

AA

CA

AG

CA

TC

2

60

45

G

I|3

02

35

81

06

(A

G)6

A

CC

AG

CC

CT

CT

CT

TT

CT

TT

TC

T

AA

TC

TC

AT

CC

TC

TT

CC

AA

TC

CA

2

13

46

G

I|2

68

52

56

31

(A

T)7

A

CC

AT

AA

CA

AA

CC

AA

AA

CC

CA

G

CA

AC

CA

GA

AG

AG

GC

AG

AA

GT

G

35

4

47

G

I|2

68

52

43

39

(C

T)1

3

CT

CT

CT

CT

CC

TT

CA

CC

AT

CA

CC

G

GC

AT

CA

CA

CA

TT

CT

AA

AA

CG

A

30

4

48

G

I|2

68

52

43

38

(A

)10

C

AA

CA

AG

GT

CA

GA

GT

CA

AT

CA

A

GT

TC

AC

CG

CT

CA

GT

AT

CA

AA

GT

1

52

49

G

I|2

68

52

37

16

(T

C)1

6

AA

CA

TT

GG

TT

AT

GG

TT

GG

AA

GG

T

CA

GA

GA

AG

AG

GT

GA

GA

AG

AA

AG

AA

2

68

50

G

I|2

68

52

27

64

(C

TT

)6

CC

CT

GT

TT

CC

CA

TT

AG

TC

TC

TG

C

TC

TG

TT

TG

CT

TC

CT

TC

AT

TC

C

27

3

51

G

I|2

68

52

17

34

(T

CA

)6

CC

TC

AT

CC

TC

AT

CT

TC

AT

CC

TC

C

TG

TG

TC

TT

GG

TT

TA

CG

GG

TG

1

35

52

G

I|2

68

51

99

74

(T

)16

T

TG

TT

GC

TA

TG

CC

AG

AA

GA

AT

G

TT

GG

AA

AC

GG

AA

AG

GA

GT

AA

GA

2

37

53

G

I|2

68

51

94

09

(T

)13

G

AA

CT

CC

AC

TC

CT

CT

TT

CC

CT

T

AA

TC

CT

CT

TT

CC

TT

TG

GT

CT

CC

3

83

54

G

I|2

68

51

74

42

(A

)13

A

CA

TT

TT

GT

TC

TG

AC

TG

GG

TT

G

GT

CC

TT

CA

TC

TG

TT

TT

GC

CT

TT

2

20

55

G

I|2

37

68

03

90

(A

)13

T

TA

AG

CA

GT

GG

TA

TC

AA

CG

CA

G

AG

CA

TC

CA

GT

CG

TA

TC

TT

CT

CC

1

13

56

G

I|2

37

68

01

09

(T

G)6

C

TG

TT

TG

CT

TC

TG

AC

CA

TT

TT

G

AA

CC

CC

TT

GT

TT

TC

AC

TC

CA

C

27

2

79

57

G

I|2

37

67

99

04

(G

)11

C

GA

GG

GA

TT

GT

TT

CT

TG

TT

TT

C

CC

CA

CA

GC

CA

CA

CC

AC

TA

1

45

58

G

I|3

11

20

81

56

(C

GG

)6

TC

GT

CC

CT

TC

TG

CT

TC

TT

AC

TC

C

TA

TT

CC

TA

TT

CG

CG

GC

AT

AT

C

15

1

59

G

I|3

11

20

79

33

(T

A)6

T

GA

CA

AG

AA

GG

TT

AC

TC

AG

GC

A

GC

AA

GG

AC

AA

AA

TG

AT

AC

GA

CA

3

69

60

G

I|3

11

20

72

33

(C

AG

)6

AC

AG

CA

GG

AG

CA

GA

AC

CA

AC

A

TG

TA

AA

TC

AC

CG

AT

CC

AA

AC

C

28

6

61

G

I|3

15

70

85

20

(A

)14

G

GC

TC

TC

TC

TG

TC

TC

AT

TT

CG

T

GC

CA

TA

TC

TT

CG

TC

GT

CT

TC

TT

3

50

62

G

I|3

15

70

84

92

(T

)14

A

AT

GA

GG

GA

AT

CT

TG

GA

TG

AA

C

TG

AA

AT

CT

AC

AG

TT

TG

CT

GG

TC

TC

2

61

63

G

I|3

15

70

83

10

(T

)15

T

CT

CT

CT

CT

TT

CT

CT

CT

CT

CC

CC

T

CC

AA

AA

CT

AC

CT

CT

CT

CC

TT

CA

3

12

64

G

I|3

15

70

65

59

(T

)11

A

CG

GA

GT

CA

AT

GG

AA

GG

AA

GT

A

CA

CG

CA

AC

AC

GA

CA

AA

CC

2

67

65

G

I|3

15

70

58

78

(T

AG

)7

GA

GT

CA

AA

AG

GT

GG

GA

AG

AA

GA

T

AG

TC

AG

GA

AA

TA

GC

AG

TC

GC

A

29

1

66

G

I|3

15

70

35

18

(A

T)1

8

GG

TT

CA

GA

TT

CA

TC

GT

CA

GT

CA

C

TT

CT

TT

TC

AG

TT

CC

CA

GC

AG

T

34

7

67

G

I|3

15

70

34

74

(T

C)6

C

CG

TT

TC

GC

TC

TT

GT

CA

TC

TA

C

GT

TG

CC

AT

TG

TC

GT

TA

TT

TC

CT

3

95

68

G

I|3

15

70

21

37

(A

G)1

3

AG

AG

AG

AG

AA

AA

GC

GG

AA

GG

AT

A

GA

AG

AA

GA

CG

AA

CT

GG

AG

GT

G

25

8

69

G

I|3

15

70

20

55

(T

)12

C

AA

TC

AA

CC

TT

CC

AG

TG

CC

C

CC

TT

TC

TT

TT

GC

CT

TC

TC

AT

A

13

7

70

G

I|3

15

70

18

42

(T

C)6

C

AA

TA

CG

AA

CG

AG

AG

AG

AG

CA

G

AT

TT

CC

AT

CA

AC

TT

TC

AC

CC

AC

2

33

71

G

I|3

15

70

09

65

(T

AG

)7

GA

GT

CA

AA

AG

GT

GG

GA

AG

AA

GA

T

AG

TC

AG

GA

AA

TA

GC

AG

TC

GC

A

29

1

72

G

I|3

15

69

95

60

(T

)13

T

CT

CT

CT

CT

TT

CT

CT

CT

CT

CC

CC

T

CC

AA

AA

CT

AC

CT

CT

CT

CC

TT

CA

3

10

73

G

I|3

15

69

94

84

(T

)13

G

AC

AG

GA

CG

GG

AC

AA

GA

TA

AA

G

AA

CC

AG

AT

CG

TA

CC

CA

AG

AA

AA

3

08

74

G

I|3

15

69

67

95

(A

T)8

C

TG

AC

CA

GA

CA

AA

AG

CA

GA

AA

C

GG

CA

AG

AA

AG

AG

AC

CA

AG

TG

AT

1

17

75

G

I|3

15

69

66

99

(C

CG

)8

CG

CT

CT

TT

GC

CT

TA

TT

AT

GC

TT

T

GA

CA

GA

TA

GA

AC

AC

TC

GT

GG

G

30

9

76

G

I|3

15

69

66

90

(G

AA

)10

G

TA

GA

AG

GA

GA

AG

GG

GA

AG

AG

G

TA

TG

CT

TG

GA

CA

GG

GC

TT

TA

TT

3

43

77

G

I|3

15

69

26

62

(C

T)1

3

CT

CT

CT

CT

CC

TT

CA

CC

AT

CA

CC

G

GC

AT

CA

CA

CA

TT

CT

AA

AA

CG

A

30

4

78

G

I|3

15

69

18

73

(G

)11

A

GA

AG

AG

AA

AC

AG

CA

CC

AC

CA

C

TG

AA

AC

CA

TT

AC

AC

AC

AG

CA

CA

3

79

79

G

I|3

15

68

53

59

(T

A)2

1

CT

AC

GG

CT

TT

CC

TA

CC

TT

TT

CA

T

TC

TG

CT

TA

CA

AT

CC

CA

AC

CT

T

21

2

80

G

I|3

15

68

53

00

(T

TA

)6

GC

TT

GC

TT

CT

TT

GT

TC

TT

TC

CT

G

TC

TC

TT

GT

CT

GT

TC

GT

CA

TC

G

38

9

81

G

I|3

15

68

47

75

(A

AG

)6

GT

AA

GC

AA

AG

AG

AA

CC

CG

AA

GA

A

AA

TC

AT

CA

AC

GA

CA

CA

AG

CA

G

18

8

82

G

I|3

15

68

47

41

(C

CG

)8

GC

TC

TT

TG

CC

TT

AT

TA

TG

CT

TG

G

TG

AC

GG

TA

GA

GG

TT

GA

AT

GA

2

51

83

G

I|3

15

68

45

64

(T

)15

T

CT

CT

CT

CT

TT

CT

CT

CT

CT

CC

CC

T

CC

AA

AA

CT

AC

CT

CT

CT

CC

TT

CA

3

12

84

G

I|3

15

68

45

41

(C

T)6

G

AC

TG

TG

AA

AA

CT

GA

AA

AC

CC

C

GA

GG

AG

AG

AA

AG

CA

AA

GA

TG

GA

1

57

85

G

I|3

15

68

15

08

(C

)12

T

GA

TT

TG

CC

TT

GT

GA

GT

AT

TG

C

GG

AG

GA

GA

TG

AG

AG

AG

TG

GA

GA

1

95

86

G

I|3

15

68

12

28

(T

)11

A

AC

AT

AG

CG

GG

AT

GG

AA

AT

AG

A

CA

CG

CA

AC

AC

GA

CA

AA

CC

1

04

87

G

I|3

15

67

61

33

(T

)11

A

CG

GA

GT

CA

AT

GG

AA

GG

AA

GT

A

CA

CG

CA

AC

AC

GA

CA

AA

CC

2

67

79

88

G

I|3

15

67

60

19

(A

)26

T

CC

CT

CT

CT

AT

CC

AA

AA

TC

CA

A

TA

CT

TT

AT

CC

CT

AA

TC

CA

GC

GG

1

48

89

G

I|3

15

67

56

32

(A

)13

C

TA

CT

AC

CC

AT

CA

AA

TC

CC

AC

C

CC

AT

TA

GC

CA

CA

AC

AC

CA

CT

TA

2

22

Inte

rru

pte

d E

ST

-SS

R P

rim

ers

90

(TT

C)9

(T)1

1

CG

TT

AG

AT

TT

CC

AC

TC

AC

CT

CC

C

TA

TT

TA

CC

G C

TT

CC

GA

TT

CC

T

29

6

91

(T)1

5(G

A)1

7

CT

GT

CC

AT

CT

CC

CT

CT

CA

GT

AT

C

GT

GT

GT

GT

GT

GT

GT

TT

AT

TC

GC

3

37

92

(TC

)10

(TC

)11

A

G C

AA

CT

CT

TT

T C

CT

TC

CT

CC

T

TC

AC

TT

AT

CA

TC

AC

CA

GC

CA

TC

1

90

93

(GA

)11

(A)1

1

AT

AA

AG

A C

AA

AT

GG

AC

A A

GG

GG

G

CA

AA

GT

GA

AT

CT

AC

AG

CA

GG

A

35

4

chapter 5: development of ssr primers from...

Documents