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Development of cost efficient and rapid method for developing homozygous SSR markers in mungbean Vigna radiata (L.) Wilczek and its validation in Vigna species

Thesis submitted in partial fulfillment of the requirement for the degree of

MASTER OF SCIENCE IN BIOTECHNOLOGY to the Tamil Nadu Agricultural University, Coimbatore-641003

By NIDHI SINGH, B.Sc. (Ag.) (I.D.No. 09-607-010)

DEPARTMENT OF PLANT MOLECULAR BIOLOGY AND BIOTECHNOLOGY CENTRE FOR PLANT MOLECULAR BIOLOGY AGRICULTURAL COLLEGE AND RESEARCH INSTITUTE TAMIL NADU AGRICULTURAL UNIVERSITY COIMBATORE-641003 2011

CHAPTER I

INTRODUCTION

Mungbean (Vigna radiata L. Wilczek) is an important pulse crop in developing countries of Asia, Africa and Latin America, where it is consumed as dry seeds, fresh green pods (Karuppanapandian et al., 2006). Mungbean serves as vital source of vegetable protein (19.1-28.3%), mineral (0.18-0.21%) and vitamins. It is native of India-Burma and is cultivated extensively in Asia (Khattak et al., 2007). India is the leading mungbean cultivator, covers up to 55% of the total world acreage and 45% of total production (Rishi, 2009). Molecular markers are indispensable for genomic study. Among various marker systems such as Restriction Fragment Length Polymorphism (RFLP), Random Amplified Polymorphic DNA (RAPD), Sequence Tagged Sites (STSs) and Amplified Fragment Length Polymorphism (AFLP), Simple Sequence Repeats (SSRs) have occupied a pivotal place because of their reproducibility, multiallelic nature, codominant inheritance, relative abundance and good genetic coverage. SSRs are clusters of short tandem repeated nucleotide bases distributed throughout the genome. They are co-dominant, multi-allelic and require small amount of DNA for scoring. Within a short time they became the markers of choice for plant and animal genomes, because of small sample size (genomic DNA) require for their analysis. Major features that made SSRs very popular are their abundant distribution in the genomes examined to date and their hyper variable nature (Toth et al., 2000). Genomic study in this crop is far behind other legume crops. SSR markers have been limited in mungbean due to the lack of genetic variation in this species (Somta et al., 2008). However, SSRs from azuki bean [V. Angularis (Willd.) Ohwi & Ohashi], common bean and

cowpea can be used in mungbean (Somta and Srinives, 2007). As high as 72.7% of the azuki bean SSRs amplify mungbean genomic DNA (Chaitieng et al., 2006). While 60.6% of common bean SSRs amplify mungbean genomic DNA. With a genome size of 579 Mb the number of SSR markers in the mungbean genome would be in the order of 0.8 to 1.0×103. This may be an overestimate as the mungbean genome has been reported to contain about 65% single-copy sequences. Methods for the isolation of SSR loci in Vigna have been developed (Kumar et al. 2002 a, b; Miyagi et al. 2004; Wang et al. 2004). Production of SSR markers can be achieved by methods such as database searching, cross-species amplification, screening genomic libraries and screening of RAPD amplicons. The traditional method of SSR marker development involves construction of SSR-enriched library, cloning and sequencing, which is costly and labour intensive (Kalia et al., 2011). Bioinformatics approaches are increasingly being used for molecular marker development since the sequences from many genomes are made freely available in the public databases (Kantety et al., 2002; Varshney et al., 2002). Information available at databases can be utilized to design SSR markers in a very short span of time and thus providing a cost efficient method to develop SSR markers. These computational tools can eliminate the need for costly library construction and screening required for obtaining sequence information. One of the source of sequences for marker development are expressed sequence tags (ESTs) that are obtained by sequencing 5' and/or 3' ends of a complementary DNA (cDNA) molecule synthesized from a messenger RNA (mRNA). ESTs are particularly attractive for marker development since they represent coding regions of the genome and are also being developed at an extremely faster pace for many genomes (Kantety et al., 2002). Genome Survey Sequences (GSS) are nucleotide sequences similar to EST's, with the exception that most of them are genomic in origin, rather than mRNA. Genome survey

sequences are often used for the mapping of genome sequences. Hence, genomic survey sequences were utilised to develop SSR markers in Vigna radiata. With this background knowledge, the present investigation was taken up with the aim to design primers for SSR markers isolated from Vigna radiata genomic, EST and GSS sequences using in silico techniques. Based on these facts, the present study was undertaken with the following objectives: 1) Retrieval of the Nucleotide, EST and GSS sequences related to Vigna radiata from National Center for Biotechnology Information. 2) Analysis of repeat patterns and designing of SSR primers for repeat motifs. 3) Validation of designed primers on mungbean and urdbean accessions using wet lab experiments.

CHAPTER II REVIEW OF LITERATURE 2.1. Mungbean

Mungbean [Vigna radiata (L.) Wilczek] is a socioeconomically important legume crop in Asia, especially in India, Thailand and the Philippines. Mungbean is grown as a monocrop and as a component crop in many cropping systems. Mungbean seed has 24% digestible protein with low flatulence and is rich in vitamin A, iron, calcium, zinc and folate (Shanmugasundaram, 2007). Although wild, weedy and cultivated germplasm or populations of mungbean are available, very little is known regarding diversity, population structure, and gene flow and/or introgression. In addition, taxonomy of mungbean at variety or subspecies level is still arguable, and in some cases, this species is mischaracterized as other related Vigna species (Tomooka et al., 2006).

2.1.1. Morphological Description and Growth Characteristics

Mungbean is a short-duration (70 to 110 days), warm season grain legume adapted to tropical and subtropical conditions. Despite photoperiod response to short day length, mungbean can be grown over a range of latitudes provided temperatures exceed 15 degree Celsius and production areas are frost free. Mungbean crops are short-stature, less than 1.25 m, depending on the variety and growing conditions. Plants are generally branched and habit can vary from erect to sub erect in the cultivated types to prostrate in wild progenitors. Leaves are trifoliate and roots bear nodules that fix atmospheric nitrogen via a symbiotic relationship with the bacterium Rhizobium. Flowers are yellow and have typical legume ,,butterfly floral morphology with a large standard petal, two wing petals and two fused

petals that form the keel, ten anthers and a single style. Seeds are smaller (< 8.0 g/100) than those of many other grain legumes. The seed appearance can vary greatly depending on the color of the testa and presence or absence of a texture layer. The texture layer is a secretion from the epidermis of the seed testa and gives the seed a dull or buff appearance when present. Cultivated types are generally green or golden and can be shiny or dull depending on the presence of a texture layer (Lambrides and Godwin, 2007).

2.1.2. Uses and Nutritional Information

Mungbean is a pulse or food legume crop used primarily as dried seed and occasionally as forage or green pods and seeds for vegetables (Lawn, 1995). On a dry-weight basis mungbean contains (25 to 28)% protein, (1.0 to 1.5)% fat, (3.5 to 4.5)% fiber, (4.5 to 5.5)% ash and (60 to 65)% carbohydrate. The seed protein is rich in lysine, but low in sulphur amino acids methionine and cystine. The seeds are also rich in ascorbic acid (vitamin A), potassium, iron, phosphorus and calcium, but low in sodium. Generally, mungbean provides an excellent complement for cereal-based diets, particularly in Asia where it used in various ways. Dried seeds may be eaten whole or split, cooked, fermented or milled and ground into flour to make products like dahl, soups, porridge, confections, curries and alcoholic beverages. In western cultures, the beans are popular for sprouting with major use as a fresh salad vegetable (Lambrides and Godwin, 2007).

2.1.3. Karyotype, Genome Size, DNA Content, Genome Organization

Vigna species including mungbean, belonging to the subgenus Ceratotropis, have chromosome complements typical of the tribe Phaseolae with 2n=2x=22 with the exception

of the polypoid V. glabrescens 2n=4x=44. The genome size of mungbean was estimated to be 1.2 pg/2C or 579 Mb, which is about 4 times larger than the model species Arabidopsis thaliana, 0.3 pg/2C or 145 Mb, and about 30 times smaller than bread wheat (Triticum aestivum) 33.09 pg/2C or 15,996 Mb, but similar in size to other Vigna and Phaseolus

species. Forty-six per cent of the single-copy DNA of mungbean was interspersed within repetitive sequences at long periods, greater than 6.7 kb. The repetitive sequence families covered a range of about 50 to several thousand copies per haploid genome. Duplicate markers were found on more than one linkage group (LG), with evidence of tandem duplication and other linkage arrangements of duplicated loci (Lambrides and Godwin, 2007).

2.1.4. MOLECULAR GENETIC DIVERSITY OF MUNGBEAN

A large collection of mungbean germplasm encompassing 415 cultivated (V. radiata var. radiata), 189 wild (V. radiata var. sublobata) and 11 intermediate accessions from diverse geographic regions have been characterized using 19 azuki bean SSRs. Mungbean has highest diversity in South Asia, supporting the view of its domestication in the Indian subcontinent and showing that Australia and Papua New Guinea is a centre of diversity for wild mungbean. A core collection of 106 accessions representing most genetically diverse of these germplasm has been made (Somta and Srinives, 2007).

2.2. Molecular markers for Mungbean

Molecular markers are indispensable for genomic study. Not many genetic markers were developed specifically for mungbean. Molecular marker technology has greatly accelerated breeding programs for improvement of various traits including disease resistance and pest resistance in various crops by providing an indirect method of selection. The markers are typically small regions of DNA, often showing sequence polymorphism in different individuals within a species and transmitted by the simple Mendelian laws of inheritance from one generation to the next. These include Allele Specific PCR (AS-PCR) (Sarkar et al., 1990), DNA Amplification Fingerprinting (DAF) (Caetano et al., 1991), Single Sequence Repeats (Hearne et al., 1992), Arbitrarily Primed PCR (AP-PCR) (Welsh and Mc

Clelland, 1992), Single Nucleotide Polymorphisms (SNP) (Jordan and Humphries, 1994), Sequence Tagged Sites (STS) (Fukuoka et al., 1994), Amplified Fragment Length Polymorphism (AFLP) (Vos et al., 1995), Simple sequence repeats (SSR) (Anitha, 2008), Resistant gene analogues (RGA) (Chithra, 2008), Random amplified polymorphic DNA -Sequence characterized amplified regions (RAPD-SCAR) (Sudha, 2009), Random Amplified Polymorphic DNA (RAPD) (Anushya, 2009), Amplified Fragment Length Polymorphism- Resistant gene analogues (AFLPRGA) (Nawkar, 2009). Molecular markers have been used to study genetic diversity (Souframanien and Gopalakrishna, 2004) and to tag the MYMV resistance gene in blackgram (Basak et al., 2004 and Souframanien and Gopalakrishna, 2006). Random amplified polymorphic DNA (RAPD) markers and Inter Simple Sequence Repeat (ISSR) markers have been successfully used for the construction of linkage maps in many crop plants including grain legumes (Kalo et al., 2000; Lambrides et al., 2000 and Cobos et al., 2005). Only recently, microsatellite or simple sequence repeat (SSR) markers, a marker system of choice, have been developed from mungbean (Kumar et al., 2002a and Miyagi et al., 2004). Simple Sequence Repeat (SSR) markers, because of their ubiquitous presence in the genome, highly polymorphic nature and co-dominant inheritance, are another marker of choice for constructing genetic linkage maps in plants (Flandez et al., 2003; Han et al., 2005 and Chaitieng et al., 2006).

2.2.1. Randomly amplified polymorphic DNA (RAPD)

The Random Amplified polymorphic DNA (RAPD) marker technique is quick, easily generated by PCR and requires no prior sequence information. It uses small oligo nucleotide primers (10 bp in length) of arbitrary sequence to generate profile of bands. These primers bind to the complementary sequences along the genome and PCR amplification occurs when the regions between the opposing primer sites are within amplifiable distances. The

homozygous presence of fragment is not distinguishable from its heterozygote and such RAPDs are dominant markers. The RAPD technique has been used for identification purposes in many crops like mungbean (Lakhanpaul et al., 2000) and cowpea (Mignouna et al., 1998). RAPD analysis may be used to obtain the reasonably precise information on genetic relationship among the mungbean genotypes. This method is relatively quick when compared with other markers, reveals great genetic variability due to the regions in which amplification takes place and useful in differentiating closely related individuals. RAPD markers have been shown to be useful in assessing inter specific and intra specific genetic variability in many crop species. Genetic variation in gamma ray induced mutants in blackgram was revealed by RAPD and ISSR markers (Souframanien et al., 2002). Genetic linkage maps of cowpea V. unguiculata (Fatokun et al., 1992a; Menendez et al., 1997 and Ouedraogo et al., 2002). All of these maps failed to condense the basic chromosome number n = 11, with the exception of the cowpea map constructed by Ouedraogo et al., (2002), using AFLP, RAPD and RGA which now has 440 markers placed across 11 linkage groups. A genetic linkage map of black gram (V. mungo) was constructed with SSR, AFLP, RAPD and ISSR using an F9 recombinant inbred population of 104 individuals. The population was derived from an inter-subspecific cross between a black gram cultivar, TU94-2, and a wild genotype, V. mungo var. silvestris (Gupta et al., 2008).

2.2.2. Amplified Fragment Length Polymorphism (AFLP)

AFLP is a multilocus polymorphic marker technique. It is a technique, which combines the both classical restriction based and recent PCR - based approaches. This PCR based technique permits inspection of polymorphism at a large number of loci within a very short period of time and requires very small amounts of DNA. The reproducibility of AFLP is ensured by using restriction site-specific adapters and adapter specific primers with a variable

number of selective nucleotide under stringent amplification conditions. Amplified fragment length polymorphism (AFLP) methodology has been found to be a useful tool to study relationships among members of various taxa including Vigna (Tomooka et al., 2002b and Yoon et al., 2000). Thus, AFLP data can be used to infer both phenetic (bands) and genetic (nucleotide substitution) relationships among taxa. The large number of AFLP markers potentially available makes them an attractive choice for fine scale mapping. Since polymorphism is detected as the presence or absence of amplified restriction fragments, AFLPs are usually considered dominant markers.

2.2.3. SSR Markers in Mung bean

Simple sequence repeats (SSRs) or microsatellites (as short tandemly repeated DNA sequences 2­5 bp in length) markers have been developed more recently for major crop plants and this marker system is predicted to lead to even more rapid advances in both marker development and implementation in breeding programs. A potentially powerful technique for DNA fingerprinting is SSRs, which have long been known to be polymorphic and wide in plant genomes. The frequency of SSR loci in plant genomes has been estimated to be one in every 6 to 7 kb (Cardle et al., 2000) and usually have a high level of allelic diversity. With a genome size of 579 Mb the number of SSR markers in the mungbean genome would be in the order of 0.8 to 1.0×103. This may be an overestimate as the mungbean genome has been reported to contain about 65% single-copy sequences. The number of these SSRs is still very limited (Swag et al., 2006). However, SSRs from azuki bean [V. angularis (Willd.) Ohwi and Ohashi] (Wang et al., 2004), common bean (Blair et al., 1997) and cowpea (Li et al., 2001) can be used in both mungbean and blackgram. As high as 72.70 per cent and 78.20 per cent of the azuki bean SSRs amplify the genomic DNA of mungbean and blackgram respectively (Chaitieng et al., 2006), while, 60.6 per cent of common bean SSRs amplify the genomic

DNA of mungbean. Methods for the isolation of SSR loci in Vigna have been developed (Kumar et al., 2002 a, b; Miyagi et al., 2004; Wang et al., 2004). Wang et al., (2004) used an SSR enrichment method based on oligo-primed second-strand synthesis to develop SSR markers in azuki bean (V. angularis). Using this methodology 49 primer pairs were made to detect dinucleotide (AG) SSR loci. The average number of alleles in complex, wild and town populations of azuki bean was 3.0 to 3.4, 1.1 to 1.4 and 4.0 respectively. The genome size of azuki bean is 539 Mb; therefore, the number of (AG)

n

and (AC)

n

motif loci per haploid

genome were estimated to be 3500 and 2100 respectively. It is likely the SSR markers developed in this study would be an extremely useful resource for mapping studies in mungbean.

2.2.4. SCAR markers

A number of PCR-based methods, including randomly amplified polymorphic DNA and amplified fragment length polymorphism, are available that do not require previous sequence information of the genome to be studied. In a RAPD assay, a short, usually ten nucleotides long, arbitrary primer is used, which generally anneals with multiple sites in different regions of the genome and amplifies several genetic loci simultaneously. To overcome the reproducibility problem associated with the RAPD technique, RAPD markers have been converted into Sequence-Characterized Amplified Regions (SCAR). SCAR markers have been developed for several crops including lettuce (Paran and Michelmore, 1993), common bean (Adam-Blondon et al., 1994), raspberry (Parent and Page, 1995), grape (Reisch et al., 1996), rice (Naqvi and Chattoo, 1996), Brassica (Barret et al., 1998) and wheat (Hernandez et al., 1999). Transformation of RAPD markers into SCAR markers is usually considered desirable before application in marker assisted breeding due to their relative increased specificity and reproducibility. Milla et al. (2005) were able to convert two RAPD

markers flanking an introgressed QTL influencing blue mold resistance to SCAR markers on the basis of specific forward and reverse primers of at least 21 base pairs in length. Souframanien and Gopalakrishna (2006) developed ISSR and SCAR markers linked to the mungbean yellow mosaic virus (MYMV) in blackgram. Park et al, (2004) identified RAPD in an F2 population from the common bean cross Olathe (resistant) and GN Nebr.#1 sel.27 (susceptible). Park et al. (2004) identified RAPD and SCAR markers linked to the Ur-6 Andean gene controlling specific rust resistance in common bean. One species-specific SCAR marker was developed for V. umbellata by designing primers from sequenced, putatively species-specific, RAPD bands (Sudha, 2009).

2.2.5. Inter simple sequence repeats (ISSR)

This technique is a PCR based method, which involves amplification of DNA segment present at an amplifiable distance in between two identical microsatellite repeat regions oriented in opposite direction. The technique uses microsatellites, usually 16-25 bp long as primers in a single primer PCR reaction targeting multiple genomic loci to amplify mainly the inter-SSR sequences of different sizes. The microsatellite repeats used as primer can be di-nucleotides or tri-nucleotides. ISSR markers are highly polymorphic and are used in studies on genetic diversity, phylogeny, gene tagging, genome mapping and evolutionary biology (Reddy et al., 2002). ISSR PCR is a technique, which overcomes the problems like low reproducibility of RAPD, high cost of AFLP, the need to know the flanking sequences to develop species specific primers for SSR polymorphism. ISSR segregate mostly as dominant markers following simple Mendelian inheritance. However, they have also been shown to segregate as codominant markers in some cases, thus enabling distinction between homozygote and heterozygote (Sankar and Moore, 2001). DNA polymorphism in blackgram accessions was identified with ISSR markers (Souframanien and Gopalakrishna, 2004).

Using this technique, genetic diversity studies were conducted in common bean (Galvan et al., 2003), Cicer (Sudupak, 2004 and Rajesh et al., 2002) and Oryza (Joshi et al., 2000).

2.3. Methods to develop SSR markers

Production of SSR markers can be achieved by methods such as database searching, cross-species amplification, screening genomic libraries and screening of RAPD amplicons. The initial step in the development of SSR markers is the identification of sequences containing such repeats from the genomic sequences. This will be followed by the design of PCR primers flanking the SSR repeat stretch. There are approaches for the identification of SSR containing sequences; namely Molecular and Computational approach.

2.3.1. Molecular approaches 2.3.1.1. Development of SSR markers from shotgun sequencing

Over the past few years, the introduction of a massively parallel pyrosequencing technology developed by 454 Life Sciences Technology has opened new possibilities for high-throughput genome analysis (Margulies et al., 2005). Sequencing of Vigna radiata genomic DNA was carried out using 454 Life Sciences technology on the Genome Sequencer (GS) FLX System. A total of 470,024 genome shotgun sequences covering 100.5 Mb of the mungbean (Vigna radiata (L.) Wilczek) genomes have been generated using 454 sequencing technology. One thousand four hundred and ninety SSR motifs have been identified that could be used as potential molecular markers. Among the 192 tested primer pairs in 17 mungbean accessions, 60 loci revealed polymorphism with polymorphic information content (PIC) values ranging from 0.0555 to 0.6907 with an average of 0.2594. Majority of microsatellite markers were transferable in Vigna species, whereas transferability rates were

only 22.90% and 24.43% in Phaseolus vulgaris and Glycine max, respectively. The average GC content of mungbean genomic DNA generated in this study is 34.69%. There is report of development and testing of a set of SSR markers derived from shotgun sequencing from Populus trichocarpa (Sithichoke et al., 2009).

2.3.1.2. Cultivating SSR markers by constructing SSR enriched genomic library

The relatively low efficiency of SSR identification in genomic libraries using traditional hybridization has led scientists to seek more efficient protocols, such as enrichment methods (reviewed in Zane et al., 2002; Squirrell et al., 2003). The use of genomic libraries enriched for microsatellite sequences is a strategy devised to decrease the cost of marker development, while increasing the opportunity for marker discovery. Most of the enrichment methods recently reported for plants is modifications based on the method of duplex-hybridization- based formation. In mammals, enrichment based on oligo-primed second-strand synthesis has been reported to be highly efficient for AC motif discovery and has been used for the large-scale discovery of microsatellite repeat sequences. (AG) n-SSR enriched library in azuki bean is constructed in order to obtain a comprehensive range of SSR markers efficiently, the method applied in this study resulted in a 116-fold enrichment over the non-enriched genomic library, with a high percentage (98%) of successful single-locus amplification by the primer pairs designed, consequently, this method can be applied to construct SSR-enriched libraries suitable for large-scale sequencing (Wang et al., 2004). There is a research on development of SSR markers using a (CA) n-enriched SSR library and construction of an SSR-based linkage map using a two-way pseudo-testcross F1 population in Italian ryegrass. SSR markers were developed from SSR-enriched genomic libraries for Cucumis sativus (Nobuko et al., 2008).

2.3.1.3. cDNA library to manufacture SSR markers from EST sequences

Searching SSRs using EST information is an alternative way for marker discovery (Wang et al., 2006; Proite et al., 2007; Liang et al., 2009). Immature peanut seeds cDNA library of a Chinese cultivar were produced to generate more sequence information for Gene cloning (Li et al., 2009 and Li et al., 2010) and marker development, SSR markers were developed from peanut using EST sequences. In recent years, a large-scale cDNA single-pass sequencing from multiple tissues was accomplished in many crops, and numerous sequences were released in GenBank (http: //www.ncbi. nlm.nih.gov/Genbank/index.html). This greatly enhanced the development of EST-derived SSR markers (Wang et al., 2006).

2.3.1.4. PCR isolation of microsatellite arrays

PCR based methodology can be used to isolate many kinds of genomic components, although the lack of flanking sequence information has precluded many of these for microsatellite isolation. PCR isolation of microsatellite arrays (PIMA) is an approach to isolate and characterize microsatellite flanking sequences from small quantities of genomic DNA. The protocol is cheap and efficient with the advantage that it requires minimum of specialized equipment, removes the need to carry out radio nucleotide hybridization techniques and produce template suitable for sequencing both flanks with universal vector primers. Briefly, several RAPD primers are used to obtain randomly amplified fragments from the target species genome. These amplicons are cloned by using a T-vector and arrayed clones are screened using repeat specific and vector primers. This and similar techniques take advantage of the fact that RAPD fragments seem to contain microsatellite repeats more frequently than random genomic clones (Jensen et al., 2008).

2.3.1.5. Isolation methods based on selective Hybridization

The most popular method of enriched library construction is selective hybridization of DNA fragments using streptavidin-coated magnetic beads or nylon membranes. Selective

hybridization protocols appear to be extremely popular being used in over 25% of all reviewed primer notes and 70% of those employing enrichment procedures. This enrichment method has been successfully applied to plants by several authors with minor modifications, such as additional screenings for the presence of SSRs or the use of phagemids instead of E. coli. The efficiencies obtained in all cases were higher than in the traditional method and ranged from 55 percent up to 100 percent of the clones containing microsatellites and being suitable for primer designation (Zane et al., 2002).

2.3.2. Computational methods

Bioinformatics approaches are increasingly being used for molecular marker development since the sequences from many genomes are made freely available in the public databases (Kantety et al., 2002; Varshney et al., 2002). De novo generation of micro-satellite markers through laboratory-based screening of SSR enriched genomic libraries is highly time consuming and expensive. An alternative is to screen the public databases of related model species where abundant sequence data is already available. This strategy of developing SSR markers is based on searching for sequences containing microsatellites deposited in the data bases (EMBL, GenBank). This method is cost-effective, simple and relatively quick. Using this method 2100 genomic sequences of chickpea were searched for SSRs and analyzed for the design of PCR primers amplifying the SSR rich regions, thus highly polymorphic SSR markers for chickpea was developed (Qadir et al., 2007). All the genomic sequences of Medicago from the public domain database were searched and analysed of di, tri, and tetra nucleotide repeats. Of the total of about 156,000 sequences which were searched, 7325 sequences were found to contain repeat motif (Mahalakshmi et al., 2002). Data mining of public databases for marker development for crops not receiving sequencing attention was suggested as an approach (Mahalakshmi and Ortiz, 2001).

2.4. Tools to search SSR in genome

2.4.1. SSR Locator

This tool is recently developed and it integrates lots of functions together such as detection and characterization of SSRs and minisatellite motifs between one and ten base pairs, primer design for each locus found, simulation of PCR, amplifying fragments with different primer pairs from a given set of fasta files , global alignment between amplicons generated by the same primer pair, estimation of global alignment scores and identities between amplicons, generating information on primer specificity and redundancy (Carlos daMaia et al.,2008).

2.4.2. Sputnik

This is a simple program written in C programming language that searches DNA sequence files in FASTA format for microsatellite repeats (Abajian, 1994). A sequence file is provided as an input to the program and the resulting hits are written to standard output along with their position in the sequence, length, and a score determined by the length of the repeat and the number of errors. Sputnik is intended to search for repeated patterns of nucleotides of length between 2 and 5. Insertions, mismatches and deletions are tolerated but affect the overall performance score. Sputnik is suited for low throughput applications and it cannot identify mononucleotide repeats. Also, it is not currently supported by a web interface (Duran et al., 2009).

2.4.3. Repeat Finder

This program was originally developed for identifying repeats in a single input sequence, however, later upgraded to handle batch files containing multiple sequences (Duran et al., 2009). Although Repeat Finder is a good program for identifying SSRs from small to medium throughput datasets, currently it has the following limitations: (i) (ii) Cannot identify single nucleotide repeats; Slower performance with large batch files

(iii) Speed of the program reduces significantly for sequences larger than 3 kb; (iv) Output is a single long concatenated sequence that makes the identification of individual sequences time consuming.

2.4.4. SSRIT (Simple Sequence Repeats Identification Tool)

SSRIT is a simple program available through Gramene / Genome databases portal at Cornell University (http://brie2.cshl.org:8082/gramene/searches/ssrtool) (Temnykh et al., 2001). The program as available is good for the identification of "perfect" simple sequence repeats and can handle moderate-sized datasets. Although the output does contain sequence ID, motif (repeat) type, no. of repeats, SSR start and end, it does have the following limitations against criteria: (i) the program currently is not capable of detecting mononucleotide repeats; (ii) the output is not perfected currently due to which it requires some additional work by the user which is especially cumbersome when dealing with medium-sized (hundreds of sequences) datasets (Duran et al., 2009).

2.4.5. TRF-Tandem Repeat Finder

Tandem repeat finder work by alignment of two tandem copies of a pattern of length n by a sequence of n-independent Bernoulli trials (coin tosses). The probability of success, P (heads), represents the average per cent identity between the copies. Each head in the Bernoulli sequence is interpreted as a match between aligned nucleotides. Each tail is a mismatch, insertion or deletion. A second probability Pi or indel probability specifies the average percentage of insertions and deletions between the average percentage of insertions and deletions between the copies. This programme has detection and analysis components. The detection component uses a set of statistically based criteria to find candidate tandem repeats. The analysis component attempts to produce an alignment for each candidate and if successful gathers a number of statistics about the alignment and the nucleotide sequence (Duran et al., 2009).

2.4.6. TROLL (Tandem Repeat Occurrence Locator)

It is a light-weight Simple Sequence Repeat (SSR) finder based on a slight modification of the Aho-Corasick algorithm. It is fast and only requires a standard Personal Computer (PC) to operate. Running times is 127s to find all SSRs of length 20bp or more on the complete Arabidopsis genome- approx 130 Mbases divided in five chromosomesusing a PC Athlon 650 MHz with 256 MB of RAM (Duran et al., 2009).

2.5. PCR PRIMER DESIGN

Set of parameters are there to design primer such as melting temperature, string-based alignment scores for complementarity, primer length, and GC content. Most programs establish real values for these primer criteria and involve trade-offs to find the optimal primer for a particular use (Kampke, et al., 2001). Many programs include additional parameter objectives such as minimizing the total number of primers for a project (and therefore cost), excluding various target sections (repeat rich regions, or GC content <20 or >80%), target length, and so forth to improve primer quality. Various parameters which decide PCR primer are briefly explained below:

2.5.1. Melting Temperature

Regardless of the type of PCR to be conducted, the melting temperature of the primers used must be similar to ensure as consistent performance as possible between forward and reverse primer pairs. Almost all the programs analyzed here enable user defined melting temperature ranges for both primers and PCR products. Many programs use either the original nearest neighbour method or the same method with empirically determined thermodynamic values to determine primer melting temperature (Tm). A more intuitive

approximation of primer melting temperature assigns 2°C for each A-T pair and 4°C for G-C pairs. The PCR product melting temperature is determined by, Tm (prod) = 0.41 x (%GC) + 16.6 x log [K+] ­ 675 /length + 81.5 where length is the number of nucleotides in the PCR

product. For primer pairs, the annealing temperature is calculated using the values from above with the formula, Ta = 0.3 x Tm (prim) + 0.7 x Tm (prod) ­ 14.9. The primer melting temperature is a straightforward estimation of a DNA-DNA hybrid stability and critical in determining the annealing temperature. AT too high will result in insufficient primer template hybridization and, therefore, low PCR product yield. While a TA too low might possibly

lead to non-specific products caused by a higher number of base pair mis matches, where mismatch tolerance has been found to have the strongest influence on PCR specificity (Mann, et al., 2009).

2.5.2. G/C Content

A general rule followed by most primer design programs is to bracket the G/C content of primers to between 40- 0 %. A G-C pairing involves three hydrogen bonds versus two for an A-T pair, where an optimal balance of GC content enables stable specific binding, yet efficient melting at the same time. While program default settings and user input value ranges lie within some middle limits, such as the Genetics Computer Groups PRIME+ with a G/C content limit of 40-55% (Mann, et al., 2009), some programs allow for a lower or higher G/C content. This may be appropriate for an application such as differential display PCR(DDPCR), where primers are designed to anneal to a 3 untranslated region where the A/T content as high as 60-80%.

2.5.3. GC Clamp

Some programs enable the requirement to have a GC-type nucleotide pair at the 3 end of primers. These pairs include CC, GG, CG, or GC and are believed to create a more stable hybridization, or clamp-like effect, at the point of polymerization with Taq polymerase. Programs like PRIME+ enable the specification of any combination of clamps through the use of nucleotide ambiguity codes (Mann, et al., 2009). Prescribing the two 3 bases may be restrictive and will not generate primer solutions for large scale projects.

2.5.4. Self-Complementarity

Self-complementarity of a primer enables either the formation of secondary structure in the single-stranded oligonucleotide, or binding to another copy of itself such that it forms a primer dimer that is able to extend during polymerization. Either case will prevent the primer from annealing to the target DNA. A straight forward approach used in programs such as PRIDE and DOPRIMER, is to conduct a pairwise comparison of a primer to a reverse copy of itself to identify the primer dimer with the highest number of complementary matches where there is a 3 terminal base and 5overhang. A lower weighting results for primers that form primer dimers (Kampke et al., 2001).

2.5.5. Primer Length

Primer length is a function of the competing criteria of uniqueness, hybridization stability, and cost-minimization by seeking the shortest oligonucleotide. Early algorithms, such as Lowes Turbo Pascal program to GCGs PRIME+, limit primer size from 18-22 nucleotides. While short primers, 8-11mers, may yield several products, increasing primer size counter-intuitively does not indefinitely increase specificity according to real PCR data. It has been speculated that increasing primer length may also increase nucleotide mismatch tolerance. Another aspect to primer length is cost, where oligonucleotide cost is measured in terms of expense per nucleotide. Short 8-12mer oligo nucleotides, which have multiple annealing sites, are used in a Greedy algorithm to minimize the total number of primers needed for applications, where all the target sequences are known (Mann et al., 2009).

2.5.6. Other Parameters

Many primer design programs enable the user to define other evaluation criteria such as salt and DNA concentrations; number of bases to skip after each acceptable primer; PCR product size range; total number of primers or primer pairs; penalties for ambiguous

bases in the target sequence; and excluding regions with non random sequences or poor base quality. These additional user defined parameters vary depending on whether the PCR seeks to amplify defined target regions with a primer pair for each, or seeks a smaller number of primers to amplify all sequences. Primer3 was found to have the most user input controls over the design process (Mann et al., 2009).

2.6. Analysis of designed primers

PCR is a well known popular technique which amplify specific region of DNA and produce large number of nearly identical copies. Success of PCR entirely depends on primers used. Research done on markers development shows that PCR is the best way to check that primer is worth or not and also to determine polymorphism of markers. Denaturation temperature, annealing temperature and melting temperature of PCR programme depends on crop and primer designed. PCR provides several technological advantages that can facilitate SSR genotyping. PCR is broadly applicable to plant genomics and marker assisted breedin (Mann et al., 2009). Capillary and gel electrophoresis coupled with fluorescence- based detection is the most commonly reported method for the assay of SSRs. Electrophoresis and visualization of SSRs can be performed on a GelScan2000 (Corbett Research) and ABI3730 DNA analyser (Applied Biosystems). For analysis on the GelScan2000, PCR products have to be mixed with an equal volume of gel loading buffer (98% formamide, 10 mM EDTA and 0.5% basic fuchsin as tracking dye), heated for 3 min at 95°C, chilled quickly on ice and separated on a 4% sequencing gel (Hayden et al., 2008).

2.7. Fast PCR analysis

The FastPCR software has integrated tools environment that provides comprehensive facilities for designing primers for most PCR applications including standard, long distance, inverse, real-time, multiplex, unique and group-specific; overlap extension PCR (OE-PCR)

multi-fragments assembling cloning; single primer PCR (design of PCR primers from closely located inverted repeat), automatically SSR loci detection and direct PCR primers design, amino acid sequence degenerate PCR, Polymerase Chain Assembly (PCA) or oligos assembly and much more. FastPCR has the capacity to handle long sequences and sets of nucleic acid or protein sequences and it allowed the individual task and parameters for each given sequences and joining several different tasks for single run. It also allows sequence editing and databases analysis. The program includes various bioinformatics tools for analysis of sequences with GC or AT skew, CG content and purine-pyrimidine skew, the linguistic sequence complexity; generation random DNA sequence, restriction analysis and supports the clustering of sequences and consensus sequence generation and sequences similarity and conservancy analysis (Kalendar et al., 2011). The complexity values were converted to a percentage value, in which 100% means maximal ,,vocabulary richness of a sequence. Primer quality describes thus the level of primer/PCR successfulness; this value varies from 100% for the "perfect or ideal" to 0% for the "worst" primer. A "perfect" primer has a wider range of executable temperatures.

2.8. Gradient PCR

A gradient PCR is done to optimize a PCR, to figure out what annealing temperatures work best. The selection of the annealing temperature is possibly the most critical component for optimizing the specificity of a PCR reaction. In most cases, this temperature must be empirically tested. The PCR is normally started at 5°C below the calculated temperature of the primer melting point (Tm). However, the possible formation of unspecific secondary bands shows that the optimum temperature is often much higher than the calculated temperature (>12°C). When selecting a PCR thermal cycler there are three key variables to consider: the number of wells available, the temperature gradient range, and programmability. The number of wells available determines the number of

combinations of temperature gradients, MgCl2 concentrations and primer levels, which are the three parameters that determine amplification optimization. Within this matrix, the temperature gradient is a key feature in optimization especially when using heterologous primers (Padmakumar and Varadarajan, 2003).

CHAPTER III

MATERIALS AND METHODS

The present study was undertaken with the aim of development of cost efficient method for the development of SSR markers in mungbean genotypes using in silico methods. In addition, computational approaches carried out to isolate SSR from genomic sequences of mungbean. EST and Genomic Survey Sequences are also utilized to ransack SSR. Bioinformatics tools and software are utilized to complete this work. The following experiments were conducted at the Department of Plant Molecular Biology and Biotechnology, Centre for Plant Molecular Biology, Tamil Nadu Agricultural University, Coimbatore during 2009-2011.

3.1. Sowing of Mungbean Seeds

Mungbean seeds were obtained from Department of Pulse at Tamil Nadu Agricultural University. Twenty four mungbean genotype seeds obtained and all were sown in pot. List of all genotypes given in Table 3.1.

3.2. Genomic DNA of Urdbean

We had urdbean genomic DNA, so its genotypes not sown. List of genotypes used for checking primers is given in Table 3.1.

3.3. Extraction of genomic DNA

Genomic DNA isolated from 15 days old mungbean seedling following the modified protocol of Karuppandiyan et al. (2006). The quality and quantity of DNA checked by

Table 3.1 Accessions of Mungbean and Urdbean

S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. Mungbean Accessions ML1108 SP84 ML818 KMG189 VBN(Gg)2 VRM(Gg)7 VC6157-B-70P VC7890A VC7960-88 VC6197A Barimung5 NM54 VC1997A VC6040A Barimung7 AMT1 SML668 SML1023 EC396117 GG-Co-6 GG-Co-4 Co(Gg)-7 Pusa Bold Blackgram Accessions

CO-6 VBN-1

VBN-2 VBG-69 VBG-85 ADT-5

24.

VBN3

agarose gel electrophoresis and nanodrop spectrophotometer. The final concentration to do PCR was adjusted to 25ng/µl.

3.3.1. Reagents required

a. Cetyl Trimethyl Ammonium Bromide (CTAB) Extraction Buffer

CTAB 2% W/V

Tris HCl (pH 8.0)

200 mM

Sodium Chloride

1.4 M

EDTA

20 mM

2-mercaptoethanol

0.1% v/v

(-Mercaptoethanol was added immediately prior to use)

b. Tris EDTA (TE) Buffer

Tris HCl (pH 8.0)

10 mM

EDTA

1 mM

c. Ice-cold Isopropanol

d. Chloroform: Isoamylalcohol (24:1 V/V)

e. Phenol: chloroform (1:1)

f. Sodium acetate (3.0 M) pH 5.2 (pH adjusted using glacial acetic acid)

g. Ethanol (100% and 70%)

h. PVP (1%)

3.3.2. Method

The leaves were ground with 600 µl of CTAB buffer, transferred into an Eppendorf tube and incubated for 30 min. at 65°C with occasional mixing after adding PVP and

mercaptoethanol. · ·

The tubes were removed from the water bath and allowed to cool at room temperature.

Equal volume of chloroform: isoamyl alcohol mixture (24: 1) was added and mixed by inversion for 15 min.

·

It was centrifuged at 4000 rpm for 20 min and the clear aqueous phase was transferred to a new sterile tube.

·

Equal volume of phenol: chloroform (1:1) was added. It was centrifuged at 4000 rpm for 20 min and the clear aqueous phase was transferred to a new sterile tube.

·

1/3 volume of ice-cold isopropanol and 3M Sodium acetate was added and mixed gently by inversion.

· Then, it was centrifuged at 4000 rpm for 20 min to pellet the DNA and the supernatant was discarded. ·

The DNA pellet was washed with 70 per cent alcohol. After washing with 70% alcohol the DNA pellet was air dried.

·

The alcohol was discarded and DNA pellet was air dried completely. Depending upon the size of the pellet, DNA was dissolved in 100-200 µl of 1X TE (pH 8.0) and stored at 4ºC.

3.3.3. Assessing the quantity and quality of genomic DNA

The DNA was checked for its purity and intactness and then quantified. The crude genomic DNA was run on 0.8 per cent agarose gel stained with ethidium bromide following a standard method (Sambrook et al., 1989) and was visualized in a gel documentation system (Alpha ImagerTM1200, Alpha Innotech Corp., CA, USA). The DNA was quantified by using Spectrophotometer. About 1 µl of crude DNA was diluted to 1 ml deionized water. The absorbance for all accessions was measured at 260 nm. The DNA should have the maximal absorbance at 260 nm. An optical density (OD) of 1.0 corresponds to 40 µg / ml for double stranded DNA. If the reading = ,,a OD And the DNA is diluted 1000 times, multiply ,,a by the dilution factor. ,,a x 1000 = ,,b

To determine the µg / ml, multiply by 40 ,,b x 40 = ,,c µg / ml

To convert µg / ml to µg / µl, divide the value ,,c by 1000 ,,c = ----------- =,,d µg / µl 1000

Based on the quantification data, the DNA dilutions were made in 1X TE buffer to a final concentration of 25ng/µl and stored in -20°C for further use.

3.4. Retrieval of nucleotide sequences from NCBI database

Nucleotide sequences of Vigna radiata variety radiata are freely available at NCBI website (http://www.ncbi.nlm.nih.gov). In the present study all genomic sequences which are 803 in numbers were downloaded in fasta format from NCBI database. There are 829 EST sequences and 82 genomic survey sequences of Vigna radiata available at NCBI database. All genomic sequences, EST and GSS sequences available at NCBI database were downloaded in fasta format.

3.5. SSR mining with SSRIT tool

SSRIT is a simple program available through Gramene / Genome databases portal at Cornell University (http://brie2.cshl.org:8082/gramene/searches/ssrtool). The program as available is good for the identification of "perfect" simple sequence repeats and can handle moderate-sized datasets. This tool finds all perfect possible SSR present in sequence submitted. Although the output does contain sequence ID, motif (repeat) type, no. of repeats, SSR start and end, it does have the following limitations against criteria: (i) The program currently is not capable of detecting mononucleotide repeats.

(ii) The output is not perfected currently due to which it requires some additional work by the user which is especially cumbersome when dealing with medium-sized (hundreds of sequences) datasets. Sequences obtained from the NCBI database were submitted in this software and following criteria is taken: Select the maximum motif length; If maximum repeat motif length is heptameric repeat then this software will search from dimeric repeat to heptameric repeat in the submitted sequence. Give minimum number of repeat, if minimum number of repeat 2 then the result will be displayed with all repeats which should be at least 2 times repeated. In the case of primer designing the length of repeat motif should be at least 10. In present work we gave minimum number of repeat 2. Since the length of repeat should be at least 10 so we gave maximum heptameric microsatellite repeats. In this study we took only those SSR which has nucleotide repeat more than 10 in length. We took minimum of two penta- or hexa or heptanucleotide, three tetra-nucleotide, four tri nucleotide, or five dinucleotide motif repeats for primer design.

3.6. Primer designing using Primer3 software

Primers for obtained SSR were designed using Primer3 (http://frodo.wi.mit.edu) software which is freely available online. The most critical step in our PCR experiment will be designing our oligonucleotide primers. Poor primers could result in little or even no PCR product. Alternatively, they could amplify many unwanted DNA fragments. Primer design requires extensive computer-based sequence analysis and this tutorial is designed to lead us

through that analysis. This software is freely available and we can design our primer of good quality. Parameters selected are:

GC content from 45 to 60 % SSR repeats are marked as target region Product size ranges from 300 to 500bp Primer length from 18 to 25 nucleotides Melting temperature of (50 to 65)0C

After giving all these parameters we need to give pick primers. The output will be displayed as sequence of left primer and right primer with length of sequence, melting temperature, GC% and product size range is also given.

3.7. Fast PCR analysis

FastPCR is freeware software. This software has integrated tools environment that provide comprehensive facilities for designing primers for most PCR applications including standard, long distance, inverse, real-time, multiplex, unique and group-specific; overlap extension PCR (OE-PCR) multi-fragments assembling cloning; single primer PCR (design of PCR primers from close located inverted repeat), automatically SSR loci detection and direct PCR primers design, amino acid sequence degenerate PCR, Polymerase Chain Assembly (PCA) or oligos assembly and much more. this software. Steps followed were: To analyze pre designed primers click on the Primer Test option given in the software. Paste or type the primer or primers sequence(s) at any TAB Editors. Primers designed were analysed in

The programme will immediately show primer characteristics its length in bases, melting temperature, CG% content, molecular weight, the extinction coefficient (e260), nmol per 1 OD, the mass - µg per 1 OD, linguistic complexity (%) and primer quality.

If the primer is self-complementary, the program will show a picture of where this self-complementarity happens.

A self-priming ability will also be detected and shown by the program.

3.8. PCR confirmatory studies to check primers 3.8.1. Gradient PCR

The selection of the annealing temperature is the most critical component for optimizing the specificity of a PCR reaction. Gradient PCR was performed in Thermal Cycler. About 50 to 100ng of DNA were used as a template. The reaction was carried in a total reaction volume of 15µl containing the following components.

PCR Mixture 3.00 L 1.50 L 2.00 L 0.50 L

DNA 25 ng/L

10X assay buffer

Primer (10m)

dNTPs (2.5 mM) (Bangalore Genei Ltd., India)

Taq polymerase (3 units/L) (Bangalore Genei Ltd., India)

0.20 L 7.80 L 15.00 L

Sterile distilled H20

Total

The reaction mixture was given a short spin for thorough mixing of the cocktail components. The amplification was carried out in a Thermal Cycler. The temperature profile used in the amplification was as follows:

Profile 1: 94°C for 5 minutes

Initial denaturation

Profile 2: 94°C for 1 minute

Denaturing

Profile 3: (53 to 63)°C for 1 minute

Annealing

Profile 4: 72°C for 1 minute

Extension

Profile 5: 72°C for 5 minutes

Final extension

Profile 6: 4°C to hold the samples for infinity.

Profiles 2, 3 and 4 were programmed to run for 40 cycles.

3.8.1.2. Agarose Gel Electrophoresis

Agarose gel (3%) electrophoresis was performed to separate the amplified products. Materials and protocols are described in the sections below.

3.8.1.3. Loading the PCR products

Eight micro litre of PCR amplified product was loaded with 3µl of loading dye.

The voltage was maintained at 100 volts for 2.5 hours. The staining is done with Ethidium bromide solution separately after agarose gel electrophoresis and the bands were visualized and documented in gel documentation system (Alpha ImagerTM1200, Alpha Innotech Corp., CA, and USA).

Viewed picture was photographed and saved for further scrutiny.

3.8.2. PCR amplification of mungbean and urdbean accessions

Primers designed using in silico methods were checked on mungbean and urdbean accessions. PCR was taken as confirmatory tool to check it. About 50 to 100ng of DNA were used as a template. The reaction was carried in a total reaction volume of 15µl containing the following components.

PCR Mixture 3.00 L 1.50 L 2.00 L 0.50 L 0.20 L 7.80 L 15.00 L

DNA 25 ng/L

10X assay buffer

Primer (10m)

dNTPs (2.5 mM) (Bangalore Genei Ltd., India)

Taq polymerase (3 units/L) (Bangalore Genei Ltd., India)

Sterile distilled H20

Total

The reaction mixture was given a short spin for thorough mixing of the cocktail components. The amplification was carried out in an Eppendorf master cycler. The temperature profile used in the amplification was as follows:

Profile 1: 94°C for 5 minutes

Initial denaturation

Profile 2: 94°C for 1 minute

Denaturing

Profile 3: 54°C for 1 minute

Annealing

Profile 4: 72°C for 1 minute

Extension

Profile 5: 72°C for 5 minutes

Final extension

Profile 6: 4°C to hold the samples for infinity.

Profiles 2, 3 and 4 were programmed to run for 40 cycles.

3.8.2.1 Agarose Gel Electrophoresis

Agarose gel (3%) electrophoresis was performed to separate the amplified products.

3.8.2.2. Materials

Loading Dye

Glycerol

50% (V/V)

Bromophenol Blue

0.5% (W/V)

3.8.2.3. Protocol

The open ends of the Pyrex gel casting plate were sealed with cello tape and placed on a perfectly horizontal platform.

Agarose (1%) was added to 1X TBE, boiled until the agarose dissolved completely and then cooled to lukewarm temperature. It was then poured into the gel mould and the comb was placed properly and allowed to solidify.

After solidification of the agarose, the comb and the cello tape were removed carefully. The cast gel was placed in the electrophoresis unit with wells towards the cathode and submerged with 1X TBE to a depth of about 1cm.

3.8.2.4. Loading the PCR products

Eight micro litre of PCR amplified product was loaded with 3µl of loading dye. The voltage was maintained at 100 volts for 2.5 hours. The staining is done with Ethidium bromide solution separately after agarose gel electrophoresis and the bands were visualized and documented in gel documentation system (Alpha ImagerTM1200, Alpha Innotech Corp., CA, and USA). Viewed picture was photographed and saved for further scrutiny.

CHAPTER IV EXPERIMENTAL RESULTS

The present study was focussed on the development of SSR markers specific for mungbean genotype. Strategy adopted to develop SSR markers was in silico methods and database mining. Genomic, Expressed Sequence Tags (EST) and Genomic Survey Sequences (GSS) sequences were obtained from National Centre for Biotechnology Information (NCBI) database and these sequences were submitted in Simple Sequence Repeat Identification Tools (SSRIT) software to find out SSR present in sequences. After retrieval of SSRs from SSRIT primers were designed using Primer3 software. From these designed primers, 15 primers were checked on 24 mungbean accessions and six urdbean accessions. Results of the experiments conducted are given below:

4.1. Retrieval of sequences from NCBI database

All genomic, EST and GSS sequences were obtained from NCBI database. It was found that there were 803 genomic sequences, 829 EST sequences and 82 GSS sequences present in Vigna radiata genome. Genomic sequences ranges from 1 kb to 3kb in size. Vigna radiata cultivar Kampangsan1 chloroplast genome was found to have maximum size of 150kb among all other genomic sequences. Both EST and GSS sequences were 1kb in size. No sequences were more than 1kb in size in EST and GSS database.

4.2. Submission of sequences in SSRIT tool

All genomic, EST and GSS sequences were submitted in SSRIT tool. SSRIT tool scrutinizes all SSR presents in submitted sequences. Maximum motif length was given heptamers and minimum number of repeat was given two. Hence, this tool searched all dinucleotide to heptanucleotide repeats which was at least two times repeated in a submitted sequence. SSRs which were more than or equal to ten nucleotides in length were selected for primer designing.

4.3. Result after submission of sequences in SSRIT tool

Result was displayed in a tabular form with sequence ID, motifs, number of repeats, SSR start, SSR end and sequence length. Motif was simple sequence that was repeated. SSR start was start coordinate of the SSR, this number was nth nucleotide from the beginning and SSR end was end coordinate of the SSR the number was nth nucleotide from the beginning of SSR in addition the length of SSR. Sequence length was total length of sequence in which SSR was present. Eight hundred and forty two SSR repeats were obtained from genomic sequences which were 803 in number and from these 842 SSR repeats 45 repeat motifs are listed in Table 4.1. Two hundred and forty two SSR repeats were obtained from EST sequences which were 829 in number and from these 240 SSR repeats 45 repeat motifs are listed in Table 4.2. Similarly, 60 SSR repeats motifs were present in GSS sequences which

were 82 in number and from these repeat motifs 25 repeat motifs are listed in Table 4.3. All repeat motifs do not function as SSR markers and primer designing for all repeats is not possible since primer designing depends upon flanking sequences. Thus, only selected repeats were taken to design primers.

4.4. Primer designing using Primer3 software

Primers determined for SSRs were obtained using Primer3 (http://frodo.wi.mit.edu) software. The most critical step in PCR experiment is to design oligonucleotide primers. Poor primers could result in little or even no PCR product. Alternatively, they could amplify many unwanted DNA fragments. Primer design requires extensive computer-based sequence analysis and this tutorial is designed to lead you through that analysis. One hundred and nine

Table 4.1 SSRs Obtained from genomic sequences

S. NO. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. Sequence gi|45331284|gb|AY485988.1|-132 gi|38045974|gb|AY437639.1|-6 gi|2502086|gb|AF022926.1|-69 gi|1478369|gb|S81594.1|-39 gi|1184120|gb|U20808.1|VRU20808-87 gi|1141783|gb|U31211.1|VRU31211-12 gi|1006804|gb|U34986.1|VRU34986-105 gi|967124|gb|U08140.1|VRU08140-125 gi|951322|gb|U31467.1|VRU31467-127 gi|849135|gb|U26709.1|VRU26709-119 gi|506851|gb|L20507.1|VIRCALMODU-36 gi|506849|gb|L20691.1|VIRCALMOD-40 gi|458337|gb|U06046.1|VRU06046-75 gi|295447|gb|L07843.1|VIRNADPHP4-153 gi|169324|gb|L07634.1|PHVC4HYDRO117 gi|189169789|gb|EU239689.2|-100 gi|9587210|gb|AF279252.1|-118 gi|9587204|gb|AF279249.1|-45 gi|8954297|gb|AF139470.2|-52 gi|8954296|gb|AF139469.2|-45 gi|8954294|gb|AF139468.2|-38 gi|8954288|gb|AF139464.2|-85 Motif caccga ta tcaga gatga ctatttc ac tattac gggaca taaaac atggc aagaa gatga tcagt caacta cccgca gtggg agaca ggtga caaatt gtgccg aca gcaaag Repea ts 2 8 2 2 2 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 2 SSR Start 221 113 51 409 1104 355 1727 186 404 188 42 392 270 44 324 202 1545 597 961 93 189 827 SSR End 232 128 60 418 1117 364 1738 197 415 197 51 401 279 55 335 211 1554 606 972 104 203 838 Seq. Length 2314 402 956 921 1261 655 1892 2022 2522 2371 820 878 1294 2617 1766 1779 1649 932 1067 782 885 1302

23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.

gi|7682676|gb|AF229794.1|-114 gi|6979535|gb|AF195806.1|-95 gi|5305365|gb|AF071550.1|-405 gi|9587206|gb|AF279250.1|-43 gi|8954291|gb|AF139466.2|-61 gi|269980508|gb|FJ857948.1|-37 gi|16930801|gb|AF441854.1|-18 gi|13682803|gb|AF126871.2|-83 gi|9587208|gb|AF279251.1|-74 gi|7682679|gb|AF229795.1|-115 gi|7025484|gb|AF229849.1|-53 gi|158251952|gb|EF990627.1|-90 gi|158251950|gb|EF990626.1|-87 gi|162296029|gb|EU288914.1|-21 gi|90969278|gb|DQ445950.1|-118 gi|90968745|gb|DQ445738.1|-28 gi|7211426|gb|AF156667.1|-133 gi|6934187|gb|AF143208.1|-83 gi|259019991|gb|GQ893027.1|-446 gi|223886027|gb|FJ591131.1|-6 gi|251831253|gb|GQ227550.1|-185 gi|238915390|gb|FJ883469.1|-23 gi|238915388|gb|FJ883468.1|-26

tgcaa tcccac tgtaaag aaatt ttcggg ctt actttt tcttg tcttg tgcaa tggaa aatac aacgac tcagg ttctaa ggag agaag tggga ta ta cccatt ggcaag ggcaag

2 2 2 2 2 4 2 3 3 2 3 2 2 2 2 3 2 2 12 9 2 2 2

441 57 6610 854 157 71 155 1507 1394 496 620 25 455 317 242 56 363 258 15595 89 3272 25 40

450 68 6623 863 168 82 166 1521 1408 505 634 34 466 326 253 67 372 267 15618 106 3283 36 51

2166 1566 7145 941 1118 786 528 1601 1488 2554 1031 1482 1436 360 2519 625 2340 1551 151271 323 3305 492 507

Table 4.2 SSRs Obtained from EST sequences

S.No. Sequence Motif Repeat SSR s Start 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 163 187 326 93 157 64 117 113 132 99 210 308 92 99 210 308 92 223 103 168 156 409 SSR End 172 196 335 104 166 75 126 124 141 108 221 317 103 108 221 317 103 234 114 177 165 418 Seq. Lengt h 702 408 404 410 351 435 380 361 413 268 634 406 444 268 634 406 444 329 275 273 331 464

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

gi|213645856|gb|AM910789.1|AM910789-39 gi|186877713|gb|AM696683.1|AM696683-26 gi|186875963|gb|AM696658.1|AM696658-17 gi|186835460|gb|AM696644.1|AM696644-15 gi|186835453|gb|AM696637.1|AM696637-22 gi|186834740|gb|AM696633.1|AM696633-22 gi|186834002|gb|AM696613.1|AM696613-19 gi|186833259|gb|AM696592.1|AM696592-19 gi|186830309|gb|AM696538.1|AM696538-18 gi|186830308|gb|AM696537.1|AM696537-14 gi|186830306|gb|AM696535.1|AM696535-32 gi|186830304|gb|AM696533.1|AM696533-23 gi|186795581|gb|AM696516.1|AM696516-18 gi|186830308|gb|AM696537.1|AM696537-14 gi|186830306|gb|AM696535.1|AM696535-32 gi|186830304|gb|AM696533.1|AM696533-23 gi|186795581|gb|AM696516.1|AM696516-18 gi|186794691|gb|AM696508.1|AM696508-22 gi|186793793|gb|AM696491.1|AM696491-15 gi|186793789|gb|AM696487.1|AM696487-9 gi|186791996|gb|AM696457.1|AM696457-18 gi|186791110|gb|AM696453.1|AM696453-23

tgtct ccaaa aaaat gccaag ttaat aatcag tgagg atgaga ctttg catcc ttttac gccct ctggtt catcc ttttac gccct ctggtt accaca ttgctg accaa ggatg agtga

23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.

gi|186789281|gb|AM696419.1|AM696419-17 gi|186789273|gb|AM696411.1|AM696411-29 gi|186729655|gb|AM696395.1|AM696395-12 gi|186728696|gb|AM696364.1|AM696364-38 gi|186727742|gb|AM696345.1|AM696345-25 gi|186727249|gb|AM696322.1|AM696322-22 gi|186727247|gb|AM696320.1|AM696320-22 gi|186726319|gb|AM696315.1|AM696315-9 gi|186726315|gb|AM696311.1|AM696311-24 gi|183206217|gb|AM696051.1|AM696051-29 gi|183206215|gb|AM696049.1|AM696049-24 gi|183206214|gb|AM696048.1|AM696048-42 gi|183206213|gb|AM696047.1|AM696047-35 gi|183206210|gb|AM696044.1|AM696044-49 gi|183206208|gb|AM696042.1|AM696042-34 gi|183206206|gb|AM696040.1|AM696040-37 gi|183206205|gb|AM696039.1|AM696039-33 gi|183206202|gb|AM696036.1|AM696036-28 gi|183206201|gb|AM696035.1|AM696035-26 gi|183206199|gb|AM696033.1|AM696033-32 gi|183206198|gb|AM696032.1|AM696032-40 gi|183206197|gb|AM696031.1|AM696031-45 gi|183206195|gb|AM696029.1|AM696029-32

aagaga gaaca aagag tcctgc atgagga cagag aga ca tttcc gtatg ctg aaagga gaaaa gatgtg actttc ttggg agaatc ggtgt gcttt cctttt gatgaa attgt catcaa

2 2 2 2 2 2 6 5 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2

234 101 207 127 198 125 85 261 178 392 413 272 173 292 223 196 194 273 430 137 332 484 631

245 110 216 138 211 134 102 270 187 401 424 283 182 303 234 205 205 282 439 148 343 493 642

362 420 363 675 328 425 447 438 477 699 743 689 696 801 795 740 744 744 725 726 766 777 767

Table 4.3 SSRs Obtained from Genomic Survey Sequences

S.No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. Sequence gi|257367024|gb|GS377372.1|GS377372-23 gi|166709893|gb|ET203890.1|ET203890-28 gi|149939382|gb|ER896028.1|ER896028-27 gi|149939381|gb|ER896027.1|ER896027-41 gi|149939380|gb|ER896026.1|ER896026-39 gi|149939378|gb|ER896024.1|ER896024-29 gi|144925907|gb|EI522402.1|EI522402-29 gi|149939378|gb|ER896024.1|ER896024-29 gi|144925907|gb|EI522402.1|EI522402-30 gi|8602614|gb|AZ254294.1|AZ254294-26 gi|8602604|gb|AZ254289.1|AZ254289-23 gi|8602600|gb|AZ254287.1|AZ254287-37 gi|8602580|gb|AZ254277.1|AZ254277-29 gi|8602569|gb|AZ254272.1|AZ254272-27 gi|8602559|gb|AZ254267.1|AZ254267-22 gi|8602535|gb|AZ254255.1|AZ254255-27 gi|8602533|gb|AZ254254.1|AZ254254-25 gi|8602527|gb|AZ254251.1|AZ254251-30 gi|8602510|gb|AZ254243.1|AZ254243-9 gi|8602504|gb|AZ254240.1|AZ254240-18 gi|8602502|gb|AZ254239.1|AZ254239-25 Motif ttgcg gggtt aaaat aataaa t aaccc aaccc cattt aaccc ttgtg agttt agaacc tgcaa cattt tgtcga aacct aagaa ttcttg tgacaa tcaaag accca ggtgac g Repeat s 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 SSR Start 209 234 178 152 610 601 243 601 499 106 264 390 76 240 341 330 287 418 150 198 153 SSR End 218 243 187 165 619 610 252 610 508 115 275 399 85 251 350 339 298 429 161 207 166 Seq. Length 439 676 622 601 791 642 637 642 637 600 509 633 516 451 467 457 500 505 251 434 470

22. 23. 24. 25.

gi|8602497|gb|AZ254237.1|AZ254237-29 gi|8602493|gb|AZ254235.1|AZ254235-30 gi|8602488|gb|AZ254233.1|AZ254233-20 gi|8602484|gb|AZ254231.1|AZ254231-26

tacag actga tgtcag gccct

2 2 2 2

308 107 310 415

317 116 321 424

503 467 451 528

SSR primers were designed from genomic sequences, 110 SSR primers were designed from EST sequences and 25 SSR primers were designed from GSS sequences. From 109 SSR primers from genomic sequences 45 primers listed in Table 4.4, from 110 SSR primers from EST sequences 45 primers listed in Table 4.5 and 25 SSR primers from GSS sequences listed in Table 4.6. Parameters selected were GC content from 45 to 60 %, SSR repeats were marked as target region, product size ranges from 300 to 500bp, primer length from 18 to 25 nucleotides and melting temperature of (50 to 65)0C. After giving all these parameters we gave pick primers. The output will be displayed as sequence of left primer and right primer with length of sequence melting temperature GC% and product size range is also given.

4.5. Fast PCR analysis

FastPCR is freeware software. The FastPCR software has integrated tools environment that provide comprehensive facilities for designing primers for most PCR applications including standard, long distance, inverse, real-time, multiplex, unique and

group-specific; overlap extension PCR (OE-PCR) multi-fragments assembling cloning; single primer PCR (design of PCR primers from closely located inverted repeat), automatically SSR loci detection and direct PCR primers design, amino acid sequence degenerate PCR, Polymerase Chain Assembly (PCA) or oligos assembly and much more. FastPCR has the capacity to handle long sequences and sets of nucleic acid or protein sequences and it allowed the individual task and parameters for each given sequences and joining several different tasks for single run. It also allows sequence editing and databases analysis. The program includes various bioinformatics tools for analysis of sequences with GC or AT skew, CG content and purine-pyrimidine skew, the linguistic sequence complexity; generation random DNA sequence, restriction analysis and supports the clustering of sequences and consensus sequence generation and sequences similarity and conservancy analysis.

Table 4.4 SSR Primers designed from Genomic Sequences

S. No. GenBank no. Primer sequences Tm (0C) 61.00 59.82 60.19 60.80 60.02 59.99 60.00 60.48 60.20 59.69 60.45 59.10 59.97 58.58 60.10 59.91 60.58 60.33 GC% Motif No. Of repeat s 2 8 2 2 2 Produc t Size (bp) 327 301 364 355 322

1. 2. 3. 4. 5.

6.

7.

8.

9.

gi|45331284|gb| AY485988.1|-132 gi|38045974|gb| AY437639.1|-6 gi|2502086|gb|A F022926.1|-69 gi|1478369|gb|S 81594.1|-39 gi|1184120|gb|U 20808.1|VRU208 08-87 gi|1141783|gb|U 31211.1|VRU312 11-12 gi|1006804|gb|U 34986.1|VRU349 86-105 gi|967124|gb|U0 8140.1|VRU0814 0-125 gi|951322|gb|U3 1467.1|VRU3146 7-127

F: GGTGTTGTCGCTGTGGTTTT R: CATCGCTGAATCTACGACCA F: CAGCTTCTTGTTCTTGCTCCTT R: TTGACGAGGCAATAGCAGGT F: GTGGGGAAACCGGAATATCT R: ACAGGCAAGACCAGAGGAGA F: GGGACTGTAATGCGGTCACT R: GTCCTCACTTGGCCATCATC F: TGATGGTGATTTGCTGGAGA R: CATGCTGGAAGATCCAAAGTC F: GTTGAGGCTCAGCAACACCT R: CGACACACATGACACCTTGA F: CATGAACGGGTTGAAGACCT R:CCAAATGGATAGAGTGTTCGTC F: GGCCTAGACAACCAGGCATA R: TATAGTGGCCCCTCTGGATG F: ATTTCCGAAGGAGCAACCTC R: CCTTCCCAACACCCTTTCTT

50.00 50.00 45.45 50.00 50.00 55.00 55.00 55.00 45.00 47.62 55.00 50.00 50.00 45.45 55.00 55.00 50.00 50.00

caccga ta tcaga gatga ctatttc

ac

5

347

tattac

2

333

gggaca

2

352

taaaac

2

304

10.

11.

12. 13.

14.

15.

16.

17. 18.

gi|849135|gb|U2 6709.1|VRU2670 9-119 gi|506851|gb|L2 0507.1|VIRCALM ODU-36 gi|506849|gb|L2 0691.1|VIRCALM OD-40 gi|458337|gb|U0 6046.1|VRU0604 6-75 gi|295447|gb|L0 7843.1|VIRNADP HP4-153 gi|169324|gb|L0 7634.1|PHVC4HY DRO-117 gi|189169789|gb |EU239689.2|100 gi|9587210|gb|A F279252.1|-118 gi|9587204|gb|A F279249.1|-45 gi|8954297|gb|A F139470.2|-52 gi|8954296|gb|A F139469.2|-45 gi|8954294|gb|A F139468.2|-38 gi|8954288|gb|A F139464.2|-85 gi|7682676|gb|A F229794.1|-114 gi|6979535|gb|A F195806.1|-95 gi|5305365|gb|A F071550.1|-405 gi|9587206|gb|A F279250.1|-43 gi|8954291|gb|A F139466.2|-61 gi|269980508|gb |FJ857948.1|-37 gi|16930801|gb| AF441854.1|-18 gi|13682803|gb| AF126871.2|-83

F: GTTTCTCGCATCGGATCTTC R: AGGGCTTGTGTGTCCGTAAC F: TCGATCGAAGAAACTCGAAC R: AATACCCGGAATGCCTCTTT F: CAACTGAGGCAGAGTTGCAG R: GTCCTCACTTGGCCATCATC F: CTGGGGTTTCTTTGAGTTGG R: GGTACCCTTTCTCCAGTCCA F: TAGCCCCCTCTCTCTCCTCT R: TTCCTCTTCCTCCTCCATCA F: ACCGCAACCTCACTCAACTC R: TCTTCCTGACGTCGTCCAC F: GGAATGGCACCTATCAATGG R: CCCAAACACAATGTCGTCAG F: CCCTGGAGATGGCAGAGTAA R: TTGATCTACGCTGAGCTTCC F: TTCAAGGCTGGGTCTCAGAT R: CAGTGACAATGGCTTGAACG F: TGAACAAGGGTACCCAGGAG R:CGGTGGCTACATTAGAGTACTG A F: TCTCCTCTCCAGCTGTTACGA R: GCGTCCTTATGGCTCAACTC F: TCCCACCAATCTATCCAAGC R: CTTCGCGTAGTTGTCGAACC F: TGGTGTTTGCTTGCTCAGAC R: GCACAACTCAGCAAAAGGTG F: GCAAGCAGGCCTCTATGTTC R: AGACCAACAGCCATTTGAGC F: GGTTTTGGCTCTGTTTCTGC R: GCGTCTTATGGCTGAGGTTT F:AGAAGACTGTGGGAACAGTGG R: ACGGCCACCAGAATAGTCAC F: CGTGGAGGGTTACCGTATTG R: CGGTGGTAGTTTCCCACTGT F:CCAAGCACCACAACTTCTCA R: TCTGTCCTGGTTCCGATGAT F: CGCTCCTTCTGCTTCTCTCA R: GTCACTGAAGGCGGTGATTT F: GCTTGGCAATCCTTGGTAGA R: AAAAGGTGCTAACGGCAGTG F: CAGGTTGTGAGTGATCCAAGC R: AAGGATTCATCGAGAGTAGTC

59.78 60.04 58.02 59.80 59.77 60.48 59.56 58.99 59.53 59.73 60.31 59.81 60.16 60.00 60.21 58.20 59.80 60.30 59.96 58.49 60.14 59.84 59.89 60.83 60.03 59.49 59.98 60.26 59.86 59.34 59.21 60.00 60.24 59.88 59.87 60.47 60.95 60.12 60.21 60.30 60.71 55.64

50.00 50.00 45.00 45.00 55.00 55.00 50.00 55.00 60.00 50.00 55.00 57.89 50.00 50.00 55.00 50.00 50.00 50.00 55.00 47.83 52.38 55.00 50.00 55.00 50.00 50.00 55.00 50.00 50.00 50.00 52.38 55.00 55.00 55.00 50.00 50.00 55.00 50.00 50.00 50.00 52.38 40.91

atggc

2

306

aagaa

2

344

gatga

2

324

tcagt

2

338

caacta

2

390

cccgca

2

327

gtggg

2

314

agaca ggtga

2 2

315 313

19.

caaatt

2

355

20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

gtgccg aca gcaaag tgcaa tcccac tgtaaag aaatt ttcggg ctt actttt tcttg

2 5 2 2 2 2 2 2 4 2 3

304 367 344 312 307 362 325 386 358 303 326

31.

32. 33. 34. 35. 36. 37. 38. 39. 40.

A gi|9587208|gb|A F: CCAAGCCTAACAAAATCAGG F279251.1|-74 R:AAGGATTCATCGAGAGTAGTC A gi|7682679|gb|A F: TGAAGGGAGGTACGATCTGG F229795.1|-115 R: TTGCAGCCCAGTTTGTGTAG gi|7025484|gb|A F: GCTGCTGATTTGATCCCTGT F229849.1|-53 R: GCCAGAGAAGAATGGAATGC gi|158251952|gb F: CAACTCCGCCAATATTCACT |EF990627.1|-90 R: AGAAGGAGGGTGTTGGGTTT gi|158251950|gb F: AACCCAACACCCTCCTTCTT |EF990626.1|-87 R: CCATGCTGCTGTTGTCTCTC gi|162296029|gb F: CGTGACCATCGAGTCTTTGA |EU288914.1|-21 R: GCTTAAACTCAGCGGGTAGC gi|90969278|gb| F: CCACGACTGATCCAGAAAGG DQ445950.1|-118 R: CGCTACCCCAAAATACCAAA gi|90968745|gb| F: CAAACCAATCCGACTCAGC DQ445738.1|-28 R: GCGTTCAAAGACTCGATGGT gi|7211426|gb|A F: CTAGTTCCGAGCTGGTGGAG F156667.1|-133 R: TCTCCCGTAGCCTGTCTTTC gi|6934187|gb|A F: GCAGCAACAAACATCCTCAC F143208.1|-83 R: GCCACACGAAGCTCATTGTA gi|259019991|gb |GQ893027.1|446 gi|223886027|gb |FJ591131.1|-6 gi|251831253|gb |GQ227550.1|185 gi|238915390|gb |FJ883469.1|-23 gi|238915388|gb |FJ883468.1|-26 F: TTCTCACTCCACCCCAGAAC R: CCTCGTGTCACCAGTTCAAA F: CAGCTTCTTGTTCTTGCTCCTT R: AGTTGACGAGGCAATAGCAG F: CTCAGGCAAATGACGTTCG R: AGCTCTTCTGATCTGGGTTG F: CCCTTCTGTCAAGGATCGAA R: AAGGATGCGGTAAAGGGTTC F: CCCTTCTGTCAAGGATCGAA R: GGTGAAGGGTTCAAAGTCCA

57.37 55.64 60.07 59.90 60.23 59.78 57.70 59.83 59.83 59.58 59.83 59.14 60.65 59.83 59.23 60.26 60.01 59.43 59.30 59.87 60.09 59.72 60.19 58.13 60.40 57.03 60.19 60.32 60.19 59.94

45.00 40.91 55.00 50.00 50.00 50.00 45.00 50.00 50.00 55.00 50.00 55.00 55.00 45.00 52.63 50.00 60.00 55.00 50.00 50.00 55.00 50.00 45.45 50.00 52.63 50.00 50.00 50.00 50.00 50.00

tcttg

3

311

tgcaa tggaa aatac aacgac tcagg ttctaa ggag agaag tggga

2 3 2 2 2 2 3 2 2

338 330 327 379 337 367 314 371 327

41.

ta

12

302

42. 43.

ta cccatt

9 2

301 391

44. 45.

ggcaag ggcaag

2 2

346 338

Table 4.5 SSR primers designed from EST sequences

S. no.

GenBank no.

Primer sequences

Tm (0C)

GC%

Motif

1.

2.

3.

4.

5.

6.

gi|213645856|gb|A M910789.1|AM9107 89-39 gi|186877713|gb|A M696683.1|AM6966 83-26 gi|186875963|gb|A M696658.1|AM6966 58-17 gi|186835460|gb|A M696644.1|AM6966 44-15 gi|186835453|gb|A M696637.1|AM6966 37-22 gi|186834740|gb|A M696633.1|AM6966 33-22

F: CCAAGGCCAACAGAGAGAAG R: CTCCTTCACATCACGGACAA F: GACAGGAGCCAGCAAATGAT R: AAGGAAGGCTGCTTCAGGAT F: GCACGTGTCAACAACTTTGG R: AGAGGCTTGCTGAGCCTTTG F: CCGTGGATTGGTTCCAGTAT R: TACTCGCCACGATGGTAAGG F: GGCTGGTTTCTTGAACTGGA R: ACATGGGATGAGCCAGAACT F: TGCCTACGCCTGGAGAGTAT R: CAGTCGAGACCGAGACACAA

59.98 59.68 60.23 60.35 60.20 62.11 59.67 61.04 60.23 59.54 59.86 60.02

55.00 50.00 50.00 50.00 50.00 55.00 50.00 55.00 50.00 50.00 55.00 55.00

tgtct

No. Of repe ats 2

Prod uct size (bp) 308

ccaaa

2

347

aaaat

2

348

gccaag

2

376

ttaat

2

301

aatcag

2

377

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

gi|186834002|gb|A M696613.1|AM6966 13-19 gi|186833259|gb|A M696592.1|AM6965 92-19 gi|186830309|gb|A M696538.1|AM6965 38-18 gi|186830308|gb|A M696537.1|AM6965 37-14 gi|186830306|gb|A M696535.1|AM6965 35-32 gi|186830304|gb|A M696533.1|AM6965 33-23 gi|186795581|gb|A M696516.1|AM6965 16-18 gi|186830308|gb|A M696537.1|AM6965 37-14 gi|186830306|gb|A M696535.1|AM6965 35-32 gi|186830304|gb|A M696533.1|AM6965 33-23 gi|186795581|gb|A M696516.1|AM6965 16-18 gi|186794691|gb|A M696508.1|AM6965 08-22 gi|186793793|gb|A M696491.1|AM6964 91-15 gi|186793789|gb|A M696487.1|AM6964 87-9 gi|186791996|gb|A M696457.1|AM6964 57-18 gi|186791110|gb|A M696453.1|AM6964 53-23 gi|186789281|gb|A M696419.1|AM6964 19-17

F: TCAGAATGCGCTGGTAACAC R: TAGACCAGCTCGCACAACAT F: CCGGTGAGGAAGTGAGGATA R: CCGCCATAAGGATATGGACT F: GATCTCAAGGGTCAGCCAAA R: TCCACCCACAATGAGAAACA F: CTGAACCAACCAAACCTACCA R: CAAAAAGGCATACAAGGAGACG F: GGGTCAGGTGCAGAGTCAAT R: GCGCCCACAAAATTGTAAAC F: CTCAAGCGTTGATCAGATGG R: ATCATCTGGGTTGGGATCTG F: GGTTGCTTAATGCCACAGGA R: TATGCTTCCACGTCTTGCAC F: CTGAACCAACCAAACCTACCA R: CAAAAAGGCATACAAGGAGACG F: GGTGGTCATCACAACCACAT R: CCCCCTCGACTCAATTTGT F: CTCAAGCGTTGATCAGATGG R: ATCATCTGGGTTGGGATCTG F: GGTTGCTTAATGCCACAGGA R: GGGTACCCTTTGTGTTTAGGG F: CTCTAATGGACCACAGAGCAGA R: GGATCTGGAATTGGGGAAAG F: AAACCTGCATGACCACACCT R: GCTTAGGCACTTGAGGATGG F: TCACCAAGCAGAGAGGGTTT R: GCCAGTTGAACAGGTTGCTT F: GCCATTAATCCCCATGCTTA R:GCCTGAAAACCTAGAGAATATAC AAGA F: CACAGGGAGAGTGATGCTGA R: CCAATGGAAGTTGCACCAG F: CTCCCCTGATGCTCTAGATTTC R: CACCAAAGACAAAGCGTTCC

59.87 59.47 60.07 58.88 60.20 59.94 59.88 60.99 60.12 60.36 59.39 59.74 61.03 59.87 59.88 60.99 59.08 59.91 59.39 59.74 61.03 59.62 59.50 60.63 60.43 59.84 59.84 60.30 59.76 59.66 59.98 60.10 59.33 60.67

50.00 50.00 55.00 50.00 50.00 45.00 47.62 45.45 55.00 45.00 50.00 50.00 50.00 50.00 47.62 45.45 50.00 52.63 50.00 50.00 50.00 52.38 50.00 50.00 50.00 55.00 50.00 50.00 45.00 37.04 55.00 52.63 50.00 50.00

tgagg

2

323

atgaga

2

305

ctttg

2

347

catcc

2

243

ttttac

2

361

gccct

2

367

ctggtt

2

325

catcc

2

243

ttttac

2

350

gccct

2

367

ctggtt

2

209

accaca

2

311

ttgctg

2

228

accaa

2

215

ggatg

2

304

agtga

2

323

aagaga

2

334

24.

25.

26.

27.

28. 29.

30.

31.

32.

34.

35.

36.

37.

38.

39.

40.

41.

gi|186789273|gb|A M696411.1|AM6964 11-29 gi|186729655|gb|A M696395.1|AM6963 95-12 gi|186728696|gb|A M696364.1|AM6963 64-38 gi|186727742|gb|A M696345.1|AM6963 45-25 gi|186727249|gb|A M696322.1|AM6963 22-22 gi|186727247|gb|A M696320.1|AM6963 20-22 gi|186726319|gb|A M696315.1|AM6963 15-9 gi|186726315|gb|A M696311.1|AM6963 11-24 gi|183206217|gb|A M696051.1|AM6960 51-29 gi|183206214|gb|A M696048.1|AM6960 48-42 gi|183206213|gb|A M696047.1|AM6960 47-35 gi|183206210|gb|A M696044.1|AM6960 44-49 gi|183206208|gb|A M696042.1|AM6960 42-34 gi|183206206|gb|A M696040.1|AM6960 40-37 gi|183206205|gb|A M696039.1|AM6960 39-33 gi|183206202|gb|A M696036.1|AM6960 36-28 gi|183206201|gb|A M696035.1|AM6960 35-26

F: TGGCACAGTCACTGCTTTCT R: CGCTGCTATGAAAGGAGCTT F: GCTAAATTGCGGCTTCTACC R: GGCTATTCCTCAACCTGTTTGC F: TGGTTGACCGCAGCATAGT R: TGTGCTGCGTGACCTTAGTT F: CCTACACGCACCAGAACCTT R: TCTGATCTCTGGCCTGCTCT F: GTGGGTCAGAAACCCAAGAG R: CAGCCTTTGCCACCAGTATT F: GGGCCAGTGACAAATGAGAG R: CACGACAGTTCACCAAGCAT F: CTTGCACCCTCCAAGCTATT R: GAGGACAACCCAAGCTGAAC F: CGCTCTTGGTTGCTATGTCA R: GAGTGGTGTGATGGCAAATG F: AAGTGGTAGGACCTGGTGGA R: TTGGAATTCTCTCCCTGCTC F: GGGCAAAGAAGAGGATCTGA R: CCAAGGGTAGAATGGGACAA F: TAGGTGGTTGGGTTGGAGAG R: TTCAGAGGTTCCGACTTTGG F: CAGAAAGGGCTTCGCATAAG R: CGAGATGTCCTTCCCACACT F: AGGATCAGGGTTGAGCATGT R: GCTACATGCAGTGGCAAGAA F: CTCTGTACTGCATCGGTTGG R: TTCTCACACCGAGGGTCTCT F: TCATCAATCTGCGTCTGACC R: AGAACCAGCAAACCCAGGAT F: GAGGCAACATCACCCTCCTA R: TCATGGACCCACCACTGAAT F: CTGAAGGGTAGCCAGCAAAG R: CAGCTACTGCAGTTTCCCAGT

59.62 59.75 58.99 62.17 60.28 59.51 60.17 60.25 59.55 60.13 60.66 59.75 59.34 59.70 60.01 59.97 59.42 59.36 59.36 59.78 59.96 60.22 59.97 60.11 59.54 60.02 59.31 59.83 59.79 60.88 60.07 61.21 60.01 59.55

50.00 50.00 50.00 50.00 52.63 50.00 55.00 55.00 55.00 50.00 55.00 50.00 50.00 55.00 50.00 50.00 55.00 50.00 50.00 50.00 55.00 50.00 50.00 55.00 50.00 50.00 55.00 55.00 50.00 50.00 55.00 50.00 55.00 52.38

gaaca

2

391

aagag

2

303

tcctgc

2

343

atgagga

2

307

cagag

2

310

aga

6

342

ca

5

313

tttcc

2

311

gtatg

2

357

aaagga

2

397

gaaaa

2

304

gatgtg

2

325

actttc

2

394

ttggg

2

352

agaatc

2

317

ggtgt

2

308

gcttt

2

321

42.

43.

44.

45.

gi|183206199|gb|A M696033.1|AM6960 33-32 gi|183206198|gb|A M696032.1|AM6960 32-40 gi|183206197|gb|A M696031.1|AM6960 31-45 gi|183206195|gb|A M696029.1|AM6960 29-32

F: TCCCCAATGGTTCGGTTA R: TCTGGATTACTGGGCCTTGA F: CACCCCCTGTCCCTAAGAA R: CTTCTTTCCCCTCCACCACT F: GCTGCACAGGAGTATGCTGA R: CCGAAAGCTATTCAGGTCCA F: ATCCACGCGTTACTGAGCAT R: GCTCACACTTGAAGCATCACAC

59.70 60.59 59.90 60.48 60.17 60.21 60.69 60.91

50.00 50.00 57.89 55.00 55.00 50.00 50.00 50.00

cctttt

2

323

gatgaa

2

370

attgt

2

332

catcaa

2

376

Table 4.6 SSR Primers designed from Genomic Survey Sequences

S. no. GenBank no. Primer sequences Tm (0C) 60.10 61.29 59.92 59.90 60.36 60.25 59.92 59.76 60.94 60.49 GC% Motif No. Of repeat s 2 Produ ct size (bp) 308

1.

2.

3.

4.

5.

gi|257367024|gb |GS377372.1|GS 377372-23 gi|166709893|gb |ET203890.1|ET2 03890-28 gi|149939382|gb |ER896028.1|ER8 96028-27 gi|149939381|gb |ER896027.1|ER8 96027-41 gi|149939380|gb |ER896026.1|ER8 96026-39 gi|149939378|gb |ER896024.1|ER8 96024-29 gi|144925907|gb |EI522402.1|EI52

F: AGCTTGGCGTAATCATGGTC R: ACCAGAAAGCAAGCCGATCT F: GTCCTCGCGAATGCATCTA R: TACGAACACTTTCGCCACTG F: TGATTCGAGCTCGGTACCTC R: CGATTCAAACGTCGGTGAG F: GTCCTCGCGAATGCATCTA R: GTTCTTTGCGCGAGAGAGTT F: AATAAAGGGGGACCACATGC R: TGGGGAGAATAACTCTGACTGG F: ATAATGGGGGACCACATGC R: GGGGGATAATTGGGAGAATAGG F: TAACCGACGCCTAGGTGATT

50.00 50.00 52.63 50.00 55.00 52.63 52.63 50.00 50.00 50.00

ttgcg

gggtt

2

400

aaaat

2

543

aataaa t aaccc

2

307

2

381

6.

60.42 61.82 59.59 60.12

52.63 50.00 50.00 55.00

aaccc

2

350

7.

cattt

2

381

8.

9.

10.

11.

12.

13.

14.

15. 16.

17.

18.

19.

20.

21.

22.

23.

24.

2402-29 gi|149939378|gb |ER896024.1|ER8 96024-29 gi|144925907|gb |EI522402.1|EI52 2402-30 gi|8602614|gb|A Z254294.1|AZ254 294-26 gi|8602604|gb|A Z254289.1|AZ254 289-23 |8602600|gb|AZ 254287.1|AZ2542 87-37 gi|8602580|gb|A Z254277.1|AZ254 277-29 gi|8602569|gb|A Z254272.1|AZ254 272-27 gi|8602559|gb|A Z254267.1|AZ254 267-22 gi|8602535|gb|A Z254255.1|AZ254 255-27 gi|8602533|gb|A Z254254.1|AZ254 254-25 gi|8602527|gb|A Z254251.1|AZ254 251-30 gi|8602510|gb|A Z254243.1|AZ254 243-9 gi|8602504|gb|A Z254240.1|AZ254 240-18 gi|8602502|gb|A Z254239.1|AZ254 239-25 gi|8602497|gb|A Z254237.1|AZ254 237-29 gi|8602493|gb|A Z254235.1|AZ254 235-30 gi|8602488|gb|A Z254233.1|AZ254

R: GAGGCAGCTAGCAAATGGAG F: ATAATGGGGGACCACATGC R: GGGGGATAATTGGGAGAATAGG F: TAACCGACGCCTAGGTGATT R: GAGGCAGCTAGCAAATGGAG F: TGTAACCTTGGCACAACGAG R: CTGTACAGGGGTGTTTAGCTTC F: TGAGGGATCCAAGTCTTTGC R: CACTGGCTTCCCCCAATAA F: CGCTCATACTAGCTCCCCAAT R: GCTGGCACAAGGGGTTACTA F: AGTGGGAGCAGGCTAAATGA R: AGAGTGCTCCAGCAAGCAAT F: CTGGAGAACAAGACGGTGGT R: CACCTGCCACTACAGAGAGC F: CTTGATCAAACTGCCTGCAA R: GCCGGAGTTTGAGTGTCAAT F: GGTGTCATTCAAGGGCATCT R: TCGATTCCTCCTTTGACCAC F: GCCAAGGTGCCAGATATGAG R: GGCATGCTAGCGAAACATTC F: TCCTCTCCTTCACCTCGTTG R: AACACAGGCTACAGCTCAACC F: ATGAGCAAGGGGCAAGTATG R: TTCCCAACAGCTCAGTGTGT F: GAGCGTAGGCTTGCTTTGAG R: CACGGGGAGGTAGTGACAAT F: CCAGTGTGGTGGAATTCTGA R: CCTCCAATGGATCCTCGTTA F: TTGCCCCTATCACCTTTCAC R: GTAGACCCGGGTTCCGAAT F: GTGCCCCAACACACTTCTTT R: CTTGCCGTACAACCTCTTGA F: GCACCACAATGCATCAACAC R:

60.42 61.82 59.59 60.12 59.76 57.95 60.20 60.84 60.61 60.13 59.84 60.16 60.15 58.62 59.99 60.12 59.93 60.05 60.62 60.75 60.38 59.42 60.10 59.31 60.29 59.84 59.52 59.89 59.93 61.09 60.01 58.92 61.03 59.39

52.63 50.00 50.00 55.00 50.00 50.00 50.00 52.63 52.38 55.00 50.00 50.00 55.00 60.00 45.00 50.00 50.00 50.00 55.00 50.00 55.00 52.38 50.00 50.00 55.00 55.00 50.00 50.00 50.00 57.89 50.00 50.00 50.00 54.55

aaccc

2

350

ttgtg

2

381

agttt

2

319

agaacc

2

303

tgcaa

2

312

cattt

2

351

tgtcga

2

325

aacct

2

331

aagaa

2

368

ttcttg

2

354

tgacaa

2

398

tcaaag

2

172

accca

2

333

ggtgac g tacag

2

328

2

365

actga

2

304

tgtcag

2

451

25.

233-20 gi|8602484|gb|A Z254231.1|AZ254 231-26

GAAGCCTGTAGACCCTTGACTC F: GGTGTTCTTTGTGACGTGGA R: AGCGTAATAAAGCGCCACAG

59.57 60.42

50.00 50.00

gccct

2

396

Designed primers were checked in this software. Paste the primer sequences in the software, the programme will immediately show primer characteristics such as length in bases, melting temperature, CG% content, molecular weight, the extinction coefficient(e260), nmol per 1 OD, the mass- µg per 1 OD and linguistic complexity(%). Results of FastPCR for the 15 ordered primer is given in Appendix16.

4.6. Mungbean accessions sown

Mungbean seeds were obtained from the Department of Pulse at Tamil Nadu Agricultural University. Twenty four mungbean genotype were obtained and all were sown in pots. Mungbean seedlings sown are given in Plate4.1. We had good quality of urdbean DNA, so urdbean accessions were not sown.

4.6.1. Testing the quality of extracted genomic DNA

The genomic DNA was extracted from 24 mungbean genotypes and six urdbean genotypes. Molecular marker profile analysis requires intact, un-sheared DNA sample of sufficient quantity. To assess the quality, the genomic DNA samples were run on a 0.8% agarose gel and stained with Ethidium bromide. The gels were documented using AlphaImagerTM

1200

(Alpha Innotech Corporation, California, and USA). The agarose gel

showed that genomic DNA samples of the 24 mungbean genotypes and 6 blackgram DNA were intact, free from RNA and high molecular weight in nature, given in Plate 4. 2.

4.6.2. Quantification of DNA

The quantity of extracted DNA was checked in a nanodrop spectrophotometer. Quantity of all 30 samples range from 200ng/µl to 1500ng/µl in undiluted DNA and after quantification samples were diluted to 50ng/µl for SSR marker analysis. Diluted DNA was also checked in agarose gel, given in Plate 4.3.

4.7. Gradient PCR

Annealing temperature of primer was optimised by gradient PCR. Annealing temperature is the most critical component for optimizing the specificity of a PCR reaction. All fifteen primers were checked in PCR thermal cycler to optimise the annealing temperature. Temperature gradient range of below five and above five to the melting temperature was given. SSR primers having annealing temperature of 58.4oC were given temperature range from 53 to 63 oC and band was obtained at 53.9 oC which was optimised as 54

o

C. SSR primers MBSSRG1, MBSSRG2, MBSSRG3, MBSSRG4, MBSSRG5,

MBSSRG6, MBSSRG7, MBSSRG8, MBSSRG11, MBSSRG12, MBSSRG13, MBSSRG14 and MBSSRG15 have melting temperature of 58.4oC has annealing temperature optimised as 54 oC, given in plate 4.4b. SSR primers MBSSRG9 and MBSSRG10 have melting

temperature 61.4oC, thus annealing temperature obtained was 56 oC, which is 5 below melting temperature, given in Plate 4.4a.

4.8. Primers Checked on mungbean and urdbean accessions

Fifteen primers were checked on 24 mungbean and six urdbean accessions. All primers amplified both mungbean and urdbean accessions. All these 15 primers were from genomic sequences. All primers checked on mungbean and urdbean accessions showed positive amplification.

4.8.1. Details of primers used

Among 15 primers checked on mungbean and urdbean genotypes all showed positive amplification. All these 15 primers were designed from SSRs obtained from genomic sequences. Primers details given in Table 4.7.

4.8.2. Result of primers checking

All primers showed positive amplification on both mungbean and urdbean accessions. Details of primers used given in Table 4.7. All primers gave good amplification but only few primers gave results which is important and can be utilized in future for further research and study. Primers showing important amplification are described here under.

4.8.2.1. Amplification by the SSR primer MBSSRG1

This primer has a product size 308bp, melting temperature og 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions given in Plate 4.5. Here, the amplicon size is 308bp, for mungbean same product size was obtained but for urdbean different size is there. There is difference in two lanes. This primer shows variation in these two species. Thus we can use it for further study and in future we can make use of it.

4.8.2.2. Amplification by SSR Primer MBSSRG2

This primer has a product size of 330bp, melting temperature of 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions is given in Plate 4.6. All genotypes were found amplified

Table 4.7 Primers Checked on Mungbean and Urdbean accessions

S. no. 1. 2. 3. 4. 5 6 7 8 9 Marker Gene Bank no. HQ148143.1 HQ148143.1 AY900122.1 AY900122.1 AY683030.1 AY233257.1 HQ148143.1 HQ148144.1 HQ148144.1 Primer sequences Repeat motif (CGG) 4 (TCCTC)2 (TTGGTG)2 (GAACA)2 (TATTC)2 (TGA)4 (ACGAA)2 (CGG)4 (CTCCT)2 Product size (bp) 308 330 325 326 307 209 330 308 383 Tm(0C) GC%

MBSSRG1 MBSSRG2 MBSSRG3 MBSSRG4 MBSSRG5 MBSSRG6 MBSSRG7 MBSSRG8 MBSSRG9

F:AATTGCAGAATCCCGTGAAC R: AAGAGCGTCTTTGCCTGTTT F: GTCGATGACCCAAATCCAAT R: TGCGTTCAAAGACTCGATG F: ATCTGACGAGAGCATGTGGA R: CTCCCCTTTAGCCACAATCA F: GAAGCGCATTCGTACTGACA R: TACAACCGAAGACACGCAAG F: TGATGTGTTCCTCCCGAGTT R: AACAAGTACCCGTTGCCAAG F: ACCTTCAGGCTTCAACAACG R: CGACGTAGAAACACACGATCA F: GTCGATGACCCAAATCCAAT R: TTGCGTTCAAAGACTCGATG F: AATTGCAGAATCCCGTGAAC R: AAGAGCGTCTTTGCCTGTTT F: CGTAATGCGTCCATACCACA R: CCGATGCTCTTTTTCATGGT

58.4 58.4 58.4 58.4 58.4 58.4 58.4 58.4 59.4

45 45 50 50 50 48 45 45 47

10 11 12 13 14 15

MBSSRG10 MBSSRG11 MBSSRG12 MBSSRG13 MBSSRG14 MBSSRG15

HQ148144.1 HQ148144.1 HQ148145.1 HQ148145.1 HQ148145.1 HQ148145.1

F: CGCCTCCTCTCCTCTTCAG R: CCGATGCTCTTTTTCATGGT F: AATTGCAGAATCCCGTGAAC R: AAGAGCGTCTTTGCCTGTTT F: TTGCAGAATCCTGTGAACCA R: AAGAGCGTCTTTGCCTGTTT F: ATCATTGTCGATGCCCAAAC R: AGGATTCTGCAATTCACACCA F: TTGCAGAATCCTGTGAACCA R: AAGAGCGTCTTTGCCTGTTT F: ATCATTGTCGATGCCCAAAC R: TTGCGTTCAAAGACTCGATG

(ACGAA)2 (CAATC)2 (CGG)4 (CTCCT)2 (CAATC)2 (GGAGGGG)2

312 308 306 301 306 327

61.4 58.4 58.4 58.4 58.4 58.4

54.1 45 45 45 45 45

and allelic variation was observed.

4.8.2.3. Amplification by the SSR Primer MBSSRG10

This primer has product size of 312bp, melting temperature of 61.40C, GC percentage of 54.1% and annealing temperature of 560C. Amplification by this primer on mungbean and urdbean accessions is given in Plate 4.7. All the genotypes were found amplified. Allelic variation was present between genotype ML1108 and SP84 and between genotype AMT1 and SML668. Since these are mungbean genotypes, hence intraspecific variation was obtained by this marker. This primer can be utilized for future research.

4.8.2.4. Amplification by the SSR Primer MBSSRG11

This primer has a product size of 308bp, melting temperature of 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions is given in Plate4.8. All the genotypes were found

amplified. Allelic variation was present between urdbean genotype VBG-85 and ADT-5 and mungbean genotype AMT1 and SML1023, thus both interspecific and intraspecific allelic variation was obtained by this primer. We can utilize this primer to study polymorphism present in mungbean and urdbean genotypes and we can check it on other species also.

4.8.2.5. Amplification by the SSR Primer MBSSRG12

This primer has a product size of 306bp, melting temperature of 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions is given in Plate 4.9. All genotypes were found amplified and allelic variation was observed. A few genotypes showed allelic variation which can be utilized for future research.

Plate 4.1 Mungbean genotypes sown

List of varieties sown:

1. ML1108 2. SP8

3. ML818 4. KMG189 5. VBN(Gg)2 6. VRM(Gg)7 7. VC6157-B-70P 8. VC7890A 9. VC7960-88 10. VC6197A 11. Barimung5 12. NM54 13. VC1997A 14. VC6040A 15. Barimung7 16. AMT1 17. SML668 18. SML1023 19. EC396117 20. GG-Co-6 21. GG-Co-4 22. Co(Gg)-7 23. Pusa Bold 24. VBN3.

Plate 4.2a Genomic DNA of Mungbean accessions

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Plate 4.2b Genomic DNA of Black gram accessions

25 26 27 28 29 30

LEGENDS

1. ML1108 2. SP84 3. ML818 4. KMG189 5. VBN(Gg)2 6. VRM(Gg)7 7. VC6157-B-70P 8. VC7890A 9. VC7960-88 10. VC6197A 11. Barimung5 12. NM54 13. VC1997A 14. VC6040A 15. Barimung7 16. AMT1 17. SML668 18. SML1023 19. EC396117 20. GG-Co-6 21. GG-Co-4 22. Co(Gg)-7 23. Pusa Bold 24. VBN3. 25. CO-6 26. VBN-1 27. VBN-2 28. VBG-69 29. VBG-85 30. ADT-5

Plate 4.3a Diluted DNA of Mungbean accessions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Plate 4.3b Diluted DNA of black gram accessions 25 26 27 28 29 30

LEGENDS

1. ML1108 2. SP84 3. ML818 4. KMG189 5. VBN(Gg)2 6. VRM(Gg)7 7. VC6157-B-70P 8. VC7890A 9. VC7960-88 10. VC6197A 11. Barimung5 12. NM54 13. VC1997A 14. VC6040A 15. Barimung7 16. AMT1 17. SML668 18. SML1023 19. EC396117 20. GG-Co-6 21. GG-Co-4 22. Co(Gg)-7 23. Pusa Bold 24. VBN3. 25. CO-6 26. VBN-1 27. VBN-2 28. VBG-69 29. VBG-85 30. ADT-5

Plate 4.4 a Gradient PCR for SSR primers with Tm of 61.4oC

1 2 3 4 5 6 7 8 9 10 11 12

Plate 4.4b Gradient PCR for SSR primers with Tm of 58.4oC

1

2

3 4

5 6

7

8 9

10 11

12

Accession used was SP84 (Mungbean genotype).

Temperature range given was 53 to 63 0C. Temperature was different in each lane. Lane wise temperature given below Lanes 1. 100bp ladder, 2. 530C, 3. 53.30C, 4. 53.90C, 5. 54.70C, 6. 55.80C, 7. 57.30C, 8. 59.00C, 9. 60.40C, 10. 61.50C, 11. 62.30C, 12. 62.80C. In plate 4.4a band was obtained in sixth lane which has temperature of 55.80C. In plate 4.4b band was obtained in fourth lane which has temperature of 53.90C.

Plate 4.5 Amplification by SSR primer MBSSRG1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26 27 28 29 30 31 32 Amplicon size: 308bp Lanes: 1. 100bp ladder 16. Barimung7 17. AMT1 18. SML668 19. SML1023 20. EC396117 21. GG-CO-6 22. GG-CO-4 23. Co-Gg-7 24. Pusa Bold 25.VBN3 26. 100bp ladder 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A

Plate 4.6 Amplification by SSR Primer MBSSRG2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17 18 19 Amplicon size: 330bp Lanes: 1. 100bp ladder

20

21 22 23 24 25 26

27 28 29

30 31

32

16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69

31. VBG-85 32. ADT-5

2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A

Plate 4.7 Amplification by SSR Primer MBSSRG10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17 18

19

20 21

22

23

24

25

26 27

28 29 30 31

32

Amplicon size: 312bp Lanes: 1. 100bp ladder 2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN (Gg) 2 7. VRM (Gg) 7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A 16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

Plate4.8 Amplification by SSR primer MBSSRG11

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17

18

19 20

21

22

23

24

25

26

27

28

29 30

31

32

Amplicon size: 308bp Lanes: 1. 100bp ladder 2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A 16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

Plate4.9 Amplification by SSR primer MBSSRG12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17

18

19

20

21

22

23

24

25

26

27

28

29 30

31

32

Amplicon size: 306bp Lanes: 1. 100bp ladder 2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A 16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

Plate 4.10 Amplification by SSR primer MBSSRG13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

Amplicon size: 301bp Lanes: 1. 100bp ladder 2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A 16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

Plate4.11 Amplification by SSR primer MBSSRG14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

Amplicon size: 306bp Lanes: 1. 100bp ladder 2. ML1108 3. SP84 4. ML818 5. KMG189 6. VBN(Gg)2 7. VRM(Gg)7 8. VC6157-B-70P 9. VC7890A 10. VC7960-88 11. VC6197A 12. Barimung5 13. NM54 14. VC1997A 15. VC6040A 16. Barimung7 17. 100bp ladder 18. AMT1 19. SML668 20. SML1023 21. EC396117 22. GG-CO-6 23. GG-CO-4 24. Co-Gg-7 25.Pusa Bold 26. VBN3 27. CO-6 28. VBN-1 29. VBN-2 30. VBG-69 31. VBG-85 32. ADT-5

4.8.2.6. Amplification by SSR Primer MBSSRG13

This primer has a product size of 301bp, melting temperature of 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions is given in Plate 4.10. All the genotypes got amplified and showed allelic variation. Allelic variation was present between genotype ML1108 and SP84 and between genotype VC6040A and Barimung7. Since these are mungbean genotypes, hence intraspecific variation was obtained by this marker. We can say this marker is slight polymorphic and can play an important role in mungbean genetic study. Thus, we can make use of this marker for future research.

4.8.2.7. Amplification by the Primer for SSR MBSSRG14

This primer has a product size of 306bp, melting temperature of 58.40C, GC percentage of 45% and annealing temperature of 540C. Amplification by this primer on mungbean and urdbean accessions is given in Plate 4.11. All the genotypes were found amplified and allelic variation was observed. Since allelic variation was observed this implies that marker has fruitful scope to use it in future for further study.

Chapter V DISCUSSION

Mungbean [Vigna radiata (L.) Wilczek] is a socioeconomically important legume crop in Asia, especially in India, Thailand and the Philippines. Mungbean is grown as a monocrop and as a component crop in many cropping systems. Mungbean seed has 24per cent digestible protein with low flatulence and is rich in vitamin A, iron, calcium, zinc and folate (Shanmugasundaram, 2007). Although wild, weedy and cultivated germplasm of mungbean are available, very little is known regarding its diversity, population structure, gene flow and introgression. In addition, taxonomy of mungbean at variety or subspecies level is still arguable, and in some cases, this species is mis-characterized as other related Vigna species (Tomooka et al. 2006). Molecular markers have been utilised for variety of applications such as investigation of genetic relationships between individuals, mapping of useful genes, construction of linkage maps and marker assisted selection. Among the available molecular markers, SSR have gained considerable importance in plant genetics and breeding owing to many desirable genetic attributes including hypervariablity, multiallelic nature, codominant inheritance, reproducibility, relative abundance, extensive genome coverage, chromosome specific location, amenability to automation and high throughput genotyping (Parida et al., 2009). Thus, microsatellites are more informative when compared to other molecular markers. Hence, we should produce microsatellites specific to a particular species and linked to a particular trait. Production of SSR markers can be achieve by methods such as database searching, cross-species amplification, screening genomic libraries and screening of RAPD amplicons. Among all these methods computational approaches to produce SSR markers are one of the best methods. SSR markers were developed in Ocimum basilicum using in silico methods by Gupta et al., 2010. EST-SSR markers have been developed in Catharanthus roseus using database mining by Joshi et al. (2011). Ohyama et al. (2009) produced SSR

markers from tomato using sequence data obtained from open genome databases. Mace et al. (2008) produced SSR markers in groundnut using in silico tools. In the present study, our objective is to produce homozygous SSR markers for mungbean genotypes. It has been reported earlier that despite being an important crop little is known about its genetic structure when compared to other legumes. Research done on production of SSR markers in mungbean reveals that there are very few number of SSR markers polymorphic in this crop. However, development of SSR markers using conventional methods is time consuming and expensive. Thus, this study was taken up with the objective of developing cost efficient method to produce SSR markers in mungbean. Hence to obtain new SSR markers in mungbean we used a cost effective and simple method by searching for sequences containing microsatellite in the available database. Microsatellite markers developed by this method have been reported in many crop plants; only recently SSR markers have been developed in mungbean using in silico methods by Seehalak et al. (2009). In the present study, homozygous SSR markers were developed from sequence database of Vigna radiata available at NCBI database (http://www.ncbi.nlm.nih.gov/). Seehalak et al. (2009) produced microsatellite markers in mungbean using nucleotide sequences of mungbean and urdbean from the GenBank database

(http://www.ncbi.nlm.nih.gov/). All genomic sequences, EST sequences and GSS sequences of Vigna radiata genome available at NCBI database were retrieved. Eight hundred and three sequences from Genomic sequence database were obtained, all these sequence size ranged from 1kb to 3kb, the only exception was Vigna radiata cultivar kampangsan1 chloroplast genome, which has maximum size of 150kb among all other genomic sequences. After submission of genomic sequence in Simple Sequence Repeat (SSRIT) tool, 842 SSR repeats were obtained. EST sequence database was utilised to produce genic microsatellite markers.

Somta et al. (2009) designed 157 markers after searching for SSR in 830 EST sequences. In this study, eight hundred and thirty mungbean EST sequences were obtained from NCBIs nucleotide sequence database. All EST sequence were less than equal to 1kb in size. Two hundred and forty SSR repeats were obtained from eight hundred and thirty EST sequence after submission in SSRIT tool. Genomic Survey Sequence (GSS) were also utilized to produce SSR markers and this is a novel work, there is no previous reference on production of SSR markers in mungbean using GSS sequences. Eighty two sequences were obtained from GSS database. Sixty repeat motifs were obtained from eighty two GSS sequence. All these genomic, EST and GSS sequences were scanned for dimeric to heptameric microsatellite repeats in SSRIT programme. The sequences containing microsatellite greater than or equal to ten nucleotide in length of all possible combination di-, tri-, tetra-, hexa-, and heptanucleotide repeats were selected. For primer designing, length of repeat should be greater than or equal to ten nucleotide (Seehalak et al., 2009). The primers were determined using PRIMER3 programme (http://frodo.wi.mit.edu/primer3/). Length of Primer taken was 18 to 25 nucleotides, since length should be long enough for adequate specificity and short enough for primers to bind easily to the template at annealing temperature. Primer melting temperature given was of (50 to 65)oC, GC content of 45-60 % and amplification fragment size given was 300-500bp. Criteria for primer designing were taken with reference to work done by Seehalak et al. (2009) and Somta et al. (2009). With this reference and criteria, one hundred and nine primers were designed for SSR from genomic sequences, one hundred and ten primers were designed for SSR from EST sequence and twenty five primers were designed for SSR from GSS sequences. The remaining repeats were unsuitable for primer designing due to too short flanking regions. Most of the repeats were located either at the start or end of the sequence due to which designing of primers for these repeats was not possible.

Analysis of designed primer was done in FastPCR software. This is a freeware software; on pasting the primer sequence at any TAB editors the programme will immediately show primer characteristics, such as its length in bases, melting temperature, CG% content, molecular weight, the extinction coefficient (e260), nmol per 1 OD, the mass µg per 1 OD, linguistic complexity (%) and primer quality. The complexity values were converted to a percentage value, in which 100% means maximal ,,vocabulary richness of a sequence. Primer quality describes the level of primer/PCR successfulness; this value varies from 100% for the "perfect or ideal" to 0% for the "worst" primer (Kalendar et al., 2011). Order for the primer was placed after analysing it in FastPCR. Primers with good complexity, quality, GC% and melting temperature were ordered. Result of FastPCR analysis of the fifteen ordered primers is given in Appendix 16. Fifteen primers obtained from genomic sequences were checked on twenty four mungbean and six urdbean accessions. List of accessions and primers used is given in Table 3.1 and Table 4.7 respectively. Annealing temperature of primer was optimised by gradient PCR. Annealing temperature is the most critical component for optimizing the specificity of a PCR reaction. The PCR is normally started at 5°C below the calculated temperature of the primer melting point (Tm) (Padmakumar and Varadarajan, 2003). All the fifteen primers were checked in PCR thermal cycler to optimise the annealing temperature. Temperature gradient range of below five and above five to the melting temperature was given. After checking the amplified product on gel electrophoresis annealing temperature was optimised. SSR Primers MBSSRG1, MBSSRG2, MBSSRG3, MBSSRG4, MBSSRG5, MBSSRG6, MBSSRG7, MBSSRG8, MBSSRG11, MBSSRG12, MBSSRG13, MBSSRG14 and MBSSRG15 have melting temperature of 58.4oC, thus annealing temperature obtained was 54 oC which is 5 below melting temperature. SSR Primers MBSSRG9 and MBSSRG10 have melting temperature of 61oC, thus annealing temperature obtained was 56 oC which is 5 below

melting temperature. After optimising annealing temperature these primers were used to amplify twenty four mungbean and six urdbean accessions. PCR amplification was carried out using these primers under standard condition of total volume of 15 µL containing 3 µL of DNA template, 2 µL of primer and 10 µL of cocktail mixture of Taq buffer, Taq polymerase, dNTPs and sterile water. Details of sequences in which these SSR were present with flanking primers are given in Appendices (1 to 15). Almost all genomic sequences contain SSR repeats and in lots of sequences more than one SSR repeats found in same gene. Since EST and GSS sequences were small in size, hence frequency of occurrence of SSR repeat is less. All fifteen primers successfully amplified both mungbean and urdbean accessions. Of all these fifteen primers, seven primers gave polymorphism. Allelic variation was obtained from primers for seven SSR namely MBSSRG1, MBSSRG2, MBSSRG10, MBSSRG11, MBSSRG12, MBSSRG13 and MBSSRG14. Amplification by the Primer for SSR MBSSRG10 is given in Plate 4.6. Allelic variation was present between genotypes ML1108 and SP84 and between genotypes AMT1 and SML668, all are mungbean genotypes. Hence, we can say an intraspecific variation is observed. Amplification by the primer for SSR MBSSRG11 is given in Plate 4.7. Allelic variation was present between two urdbean genotypes VBG-85 and ADT-5 and between two mungbean genotypes AMT1 and SML1023, thus both interspecific and intraspecific allelic variation was obtained by this primer. Amplification by the Primer for SSR MBSSRG13 is given in Plate4.9. Allelic variation was present between mungbean genotypes ML1108 and SP84 and between VC6040A and Barimung7. Since, these are mungbean genotypes hence intraspecific variation was obtained by this marker. Most of the primers gave amplification with allelic variation. All the primers showed good amplification.

Hence from this study, it is evident that development of SSR markers using database searching is more cost effective and cheap in compare to the isolation of the same from genomic libraries and cross- species amplification. Bioinformatics approach produces good and more informative microsatellite markers in a very short span of time. There is a plenty number of crops which are playing very important role to meet our food security but genetic study on the development of SSR marker is lagging in such crops. However, using database searching and bioinformatics methods we can obtain nucleotide sequence of information which can be utilized to carry out genetic study on such crops. Hence, these in silico methods are playing very important role in contributing to the development and progress in the field of science and agriculture.

CHAPTER VI

SUMMARY

All nucleotide genomic sequences, EST sequences and GSS sequences of Vigna radiata genome available at NCBI database were retrieved; there were 803, 829 and 82 in numbers respectively. Upon submission of sequences in SSRIT tool 842 SSR repeats from genomic sequences, 240 SSR repeats from EST sequences and 60 SSR repeats from GSS sequences were obtained. Almost all genomic sequences contain SSR repeats and many sequences have more than one SSR. Since EST and GSS sequences were small in size, frequency of occurrence of SSR is less. The sequences containing microsatellites greater than or equal to ten nucleotides in length of all possible combination di-, tri-, tetra-, hexa-, and heptanucleotide repeats were selected for primer designing using software Primer3. Using Primer3 software 109 SSR primers from genomic sequences, 110 SSR primers from EST sequence and 25 SSR primers from GSS sequences were designed. Most of the repeats were located either at the start or end of the sequence due to which it is very difficult to design primer for these repeats. Analysis of designed primer was done in FastPCR software and the primer quality was assessed. Fifteen primers obtained from genomic sequences were checked on twenty four mungbean and six urdbean accessions. Annealing temperature of primer was optimised by gradient PCR.

All fifteen primers successfully amplified both mungbean and blackgram accessions. Of all these fifteen primers, seven primers gave polymorphism. Allelic variation was obtained from primers for seven SSR namely MBSSRG1, MBSSRG2, MBSSRG10, MBSSRG11, MBSSRG12, MBSSRG13 and MBSSRG14. These SSR primers showing allelic variation can be utilised in future to do genetic studies in mungbean and urdbean crop. The strategy of developing SSR markers using in silico methods gave successful result. Thus this method should exploit in future to develop SSR markers.

REFERENCES

Adam-Blondon, A. F., M. Sevignac, H. Bannerot and M. Dron. 1994. SCAR, RAPD and RFLP markers linked to a dominant gene (Are) conferring resistance to anthracnose in common bean. Theor. Appl. Genet., 88: 865 - 870. Anitha. 2008. Molecular fingerprinting of Vigna sp using morphological and SSR markers. M.Sc. Thesis, Tamil Nadu Agric. Univ., Coimbatore-3, India. 45p. Anushya. 2009. Marker assisted selection for yellow mosaic virus (MYMV) in mungbean [Vigna radiata (L.) wilczek] unpub. M.Sc. Thesis, Tamil Nadu Agric. Univ., Coimbatore-3, India. 56p. Barret, P., R. Delourme, N. Foisset and M. Renard. 1998. Development of a SCAR

(Sequence characterized amplified region) marker for molecular tagging of the dwarf BREIZH (Bzh) gene in Brassica napus L. Theor. Appl. Genet., 97: 828 - 833. Basak, J., S. Kundagrami, T.K. Ghose and A. Pal. 2004. Development of Yellow Mosaic Virus (YMV) resistance linked DNA marker in Vigna mungo from populations segregating for YMV-reaction. Mol. Breed., 14: 375-383. Blair, M and S. R. McCouch. 1997. Microsatellite and sequence-tagged site markers diagnostic for the bacterial blight resistance gene, xa-5. Theor. Appl. Genet., 95: 174­184. Caetano, A. G., B. J. Bassam and P.M. Gresshoff. 1991. DNA amplification finger printing using very short arbitrary oligonucleotide primers. Biotechnol., 9: 553-557. Cardle, L., L. Ramsay, D. Milbourne, M. Macaulay, D. marshall and R. Waugh. 2000. Computational and experimental characterization of physically clustered simple sequence repeats in plants. Genetics, 156:847-854. Carlos daMaia L., D. Abel Palmieri, V. Queiroz de Souza, M. Marini Kopp, F. Iraj ´a F´ elix de Carvalho, and A. Costa de. 2008. SSR Locator: Tool for Simple Sequence Repeat Discovery Integrated with Primer Design and PCR Simulation International. Journal of Plant Genomics, Article ID 412696, 9 pages.

Chaitieng, B., A. Kaga, N. Tomooka, T. Isemura, Y. Kuroda and D. A. Vaughan. 2006. Development of a black gram [Vigna mungo (L.) Hepper] linkage map and its comparison with an azuki bean [Vigna angularis (Willd.) Ohwi and Ohashi] linkage map. Theor Appl Genet., 113: 1261­9. Chithra. 2008. Analysis of resistant gene analogues in mungbean [vigna radiata (l.)wilczek] and ricebean [vigna umbellata (thunb.) ohwi and ohashi] unpub. M.Sc. Thesis, Tamil Nadu Agric. Univ., Coimbatore-3, India. 48p. Cobos, M.J., M.J. Fernandez, J. Rubio, M. Kharrat, M.T. Moreno, J .Gil and T. Millan. 2005. A linkage map of chickpea (Cicer arietinum L.) based on populations from KabuliDesi crosses: location of genes for resistance to fusarium wilt race. Theor. Appl. Genet., 110: 1347­1353. Duran, C., D. Edwards and J. Batley. 2009. Molecular Marker Discovery and Genetic map Visualisation. P. 165-189. In: Applied Bioinformatics (Eds. Edwards D, Hanson D and Stajich J), Springer (USA). Fatokun, C. A., D. Danesh, D. I. Menancio-Hautea and N. D. Young. 1992a. A linkage map of cowpea [Vigna unguiculata (L.) Walp.] based on DNA markers (2n=22). P. 6.256 6.258. In: O'Brien SJ (ed) Genome Maps. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York. Flandez-Galvez, H., R. Ford, E.C.K. Pang and P.W.J. Taylor. 2003. An intraspecific linkage map of the chickpea (Cicer arietinum L.) genome based on sequence tagged microsatellite site and resistance gene analog markers. Theor. Appl. Genet., 106: 1447­1456. Fukuoka, S., T. Inoue, A. Miyao, L. Monna, H.S. Zhong, T. Sasaki andY. Minobe. 1994. Mapping of sequence-tagged sites in rice by single strand conformation polymorphism. DNA Res., 1: 271-277. Galvan, M. Z., B. Bornet, P. A. Balatti and M. Branchard. 2003. Inter simple sequence repeat (ISSR) marker as a tool for the assessment of both genetic diversity and gene pool origin in common bean (Phaseolus vulgaris L.). Euphytica, 133: 297-301.

Gupta, S.K., J. Souframanien and T. Gopalkrishna. 2008. Construction of a genetic linkage map of blackgram based on molecular markers and comparative studies. Genome, 51,628-637. Gupta, S., R. Shukla, S. Roy, N. Sen and A. Sharma. 2010. In silico SSR and FDM analysis through EST sequences in Ocimum basilicum. POJ, 3(4):121-128. Han, O. K., A. Kaga, T. Isemura, X. W. Wang, N. Tomooka and D. A. Vaughan. 2005. A genetic linkage map for azuki bean [Vigna angularis (Wild.) Ohwi & Ohashi]. Theor. Appl. Genet., 111: 1278­1287. Han, Z., C. Wang, X. Song, W. Guo, J. Gou, C. Li, X. Chen, and T. Zhang. 2006.

Characteristics, development and mapping of Gossypium hirsutum derived EST-SSRs in allotetraploid cotton. Theoretical and Applied Genetics, vol. 112, no. 3, p. 430439. Hayden, M. J., T. M. Nguyen, A. Waterman and K. J. Chalmer. 2008. Multiplex-Ready PCR: A new method for multiplexed SSR and SNP genotyping. BMC Genomics, 9:80. Hearne, C.M., S.Ghosh and J.A.Todd. 1992. Microsatellites for linkage analysis of genetic traits. Trends Genet., 8: 288-294. He, G., M. Ronghua, N. Melanie, G. Guoquing, P. Roy and C. S. Prakash. 2003. Microsatellites as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biology, vol. 3, no. 3. Hernandez, P., A. Martin and G.Dorado. 1999. Development of SCARs by direct sequencing of RAPD products: A practical tool for the introgression and marker assisted selection of wheat. Mol. Breeding, 5: 245 - 253. Jensen, T., K. J. Nutt, B. S. Seal, L. B. Fernandes, B. Durrant. 2008. Permanent Genetic Resources: Isolation and characterization of microsatellite loci in the North Island brown kiwi, Apteryx mantelli. Mol Ecol Resour., 8(2):399-401. Jordan, S. A and P. Humphries. 1994. Single nucleotide polymorphism in exon 2 of the BCP gene on 7q31-q35. Oxford Univ. Press, 3: 1915-1915.

Joshi, S. P., V. S. Gupta, R. K. Aggarwal, P. K. Ranjekar and D. S. Brar. 2000. Genetic diversity and phylogenetic relationship as revealed by inter simple sequence repeat (ISSR) polymorphism in the genus Oryza. Theor. Appl. Genet., 100: 1311-1320. Joshi, R. K., B. Kar and S. Nayak. 2011. Exploiting EST databases for the mining and

characterization of short sequence repeat (SSR) markers in Catharanthus roseus L. Bioinformation, 5(9): 378-381. Kalendar, R., D. Lee, A. H. Schulman. 2011. Java web tools for PCR, in silico PCR, and oligonucleotide assembly and analysis. Genomics, 98(2). Kalia, R. K., M. K. Rai, S. Kalia, R. Singh and A. K. Dhawan. 2011. Microsatellite markers: an overview of the recent progress in plants. Euphytica, 177:309­334. Kalo, P., G. Endre, L. Zimanyi, G. Csanadi and G.B. Kiss. 2000. Construction of an improved linkage map of diploid alfalfa (Medicago sativa). Theor. Appl. Genet., 100: 641­657. Kantety, R.V., M. La Rota, D. E. Matthews, and M. E. Sorrells. 2002. Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant Mol. Biol., 48, 501-510. Kampke, T., M. Kieninger and M. Mecklenbug. 2001. Efficient primer design algorithms. Bioinformatics, 17(3), 214-225. Karuppanapandian,T., T. Karuppudurai, T. P. M. Sinha, A. Hamarul Haniya and K.

Manoharan. 2006. Genetic diversity in green gram [Vigna radiata (L.)] landraces analyzed by using random amplified polymorphic DNA (RAPD). Afr. J. Biotechnol., 5: 1214 -1219. Khattak, A. B., N. Bibi and Aurangzeb. 2007. Quality Assessment and Consumers Acceptibilty Studies of Newly Evolved Mungbean Genotypes (Vigna radiata L.). American Journal of Food Technology, 2(6):536-542.

Kumar, S. V., S. G. Tan, S. C. Quah, K. Yusoff. 2002a. Isolation of microsatellite markers in mungbean, Vigna radiata. Mol Ecol Notes, 2:96­98.

Kumar, S. V., S. G. Tan, S. C. Quah, K. Yusoff. 2002b. Isolation and characterisation of seven tetranucleotide microsatellite loci in mungbean, Vigna radiata. Mol Ecol Notes, 2:293­295. Lambrides C. J., R. J. Lawn, I. D. Godwin, J. Manners and B. C. Imrie. 2000. Two genetic linkage maps of mungbean using RFLP and RAPD markers. Aust. J. Agric Res., 51: 415 - 425. Lambrides, C. J. and I. D. Godwin. 2007. Pulses, Sugar and Tuber Crops Genome Mapping and Molecular Breeding in Plants. Biomedical and Life Sciences, Volume 3, 69-90. Lakhanpaul, S., S. Chadha and K.V. Bhat. 2000. Random amplified polymorphic DNA (RAPD) analysis in Indian mungbean (Vigna radiata L. Wilczek) cultivars. Genetica, 109: 227-234. Lawn, R. J. 1995. The Asiatic Vigna species. Vigna spp. (V. radiata, V. mungo, V. angularis, V. umbellata and V. acontifolia) (Leguminosae-Papilionoideae). pp 321­326. In: Smartt J, Simmonds NW (eds) Evolution of Crop Plants, 2nd edn. Longman, London. Li, H., J. Luo, J. K. Hemphill, J. Wang and J. H. Gould. 2001. A rapid and high yielding DNA miniprep for cotton (Gossypium spp). Plant Mol. Biol. Rep., 19: 183a­183e.

Li, M., A. LI, H. Xia, C. Zhao, C. Li, S. Wan, Y. Bi and X. Wang. 2009. Cloning and sequence analysis of putative type II fatty acid synthase genes from Arachis hypogaea L. Journal of Biosciences, vol. 34, no. 2, p. 227-238.

Li, M., H. Xia, C. Zhao, A. LI, C. Li, Y. Bi, S. Wan, and X. Wang. 2010. Isolation and characterization of putative acetyl-CoA carboxylases in Arachis hypogaea L. Plant Molecular Biology Reporter, vol. 28, no. 1, p. 56-58.

Liang, X., X. Chen, Y. Hong, H. Liu, G. Zhou, S. Li, and B. Guo. 2009. Utility of ESTderived SSR in cultivated peanut (Arachis hypogaea L.) and Arachis wild species. BMC Plant Biology, vol. 9, no. 35.

Mace, E.S. R. K. Varshney, V. Mahalakshmi, K. Seetha, A. Gafoor, Y. Leeladevi and J.H. Crouch. 2008. In silico development of simple sequence repeat markers within the aeschynomenoid/dalbergoid and genistoid clades of the Leguminosae family and their transferability to Arachis hypogaea, groundnut. Plant Science, 174: 51­60. Mahalakshmi, V. and R. Ortiz. 2001. Plant genomics and agriculture: From model organisms to crops, the role of data mining for gene discovery. Electronic Journal of Biotechnology, vol. 4, no. 3. Mahalakshmi, V., P. Aparna, S. Ramadevi and Rodomiro Ortiz. 2002. Genomic sequence derived simple sequence repeats markers a case study with Medicago spp. Electronic Journal of Biotechnology, ISSN: 0717-3458, Vol.5 No.3. Mann, T., R. Humbert, M. Dorschner and J. Stamatoyannopoulos. 2009. A thermodynamic approach to PCR primer design. Nucleic Acids Research, Volume37, Issue13, Pp. e95. Margulies, M., M. Egholm, W. E. Altman, S. Attiya, J. S. Bader, L. A. Bemben, J. Berka, M. S. Braverman, Y. J. Chen, Chen Z, et al. 2005. Genome sequencing in

microfabricated high-density picolitre reactors. Nature, 437(7057):376-380. Menendez, C. M., A. E. Hall and P. Gepts. 1997. A genetic linkage map of cowpea (Vigna unguiculata) developed from across between two inbred, domesticated lines. Theor. Appl. Genet., 95: 1210 -1217. Mignouna, H. D., N. Q. Ikca and G. Thottapilly. 1998. Genetic diversity in cowpea as revealed by random amplified polymorphic DNA. J. Genet. Breed., 52: 151-159. Milla, S. R., J. S. Levin, R. S. Lewis and R. C. Rufty. 2005. RAPD and SCAR Markers linked to an introgressed gene conditioning resistance to Peronospora tabacina D.B. Adam. in Tobacco. Crop Sci., 45: 2346 -2354. Miyagi, M., M. Humphry, Z. Y. Ma, C. J. Lambrides, M. Bateson and C. J. Liu. 2004. Construction of bacterial artificial chromosome libraries and their application in developing PCR-based markers closely linked to a major locus conditioning bruchid resistance in mungbean (Vigna radiata L. Wilczek). Theor Appl Genet., 110:151­ 156.

Naqvi, N. I and B. B. Chattoo.1996. Development of a sequence-characterized amplified region (SCAR) based indirect selection method for a dominant blast resistance gene in rice. Genome, 39: 26 - 30. Nawkar. 2009. Identification of sequence polymorphism of resistant gene analogues (RGAs) in Vigna species. M.Sc. Thesis, Tamil Nadu Agric. Univ., Coimbatore-3, India. 60p. Nobuko, F., Y. Yoshioka, N. Kubo, M. Hirai, M. Sugiyama, Y. Sakata and S. Matsumoto. 2008. Development of 101 novel SSR markers and construction of an SSR-based genetic linkage map in cucumber (Cucumis sativus L.). Breeding Science, Vol. 58, No. 4, 475-483. Ohyama, A., E. Asamizu, S. Negoro, K, Miyatake, H. Yamaguchi, S. Tabata and H.

Fukuoka. 2009. Characterization of tomato SSR markers developed using BAC-end and cDNA sequences from genome databases. Mol Breeding, 23:685­691 Ouedraogo, J. T., B. S. Gowda, M. Jean, T. J. Close, J. D. Ehlers, A. E. Hall, A. G. Gillespie, P. A. Roberts, A. M. Ismail, G. Bruening , P. Gepts , M. P. Timko and F. J. Belzile. 2002. An improved genetic linkage map for cowpea (Vigna unguiculata L.) combining AFLP, RFLP, RAPD, biochemical markers, and biological resistance traits. Genome, 45: 175­188. Padmakumar, V. C. and R. Varadarajan. 2003. A gradient PCR-based screen for use in sitedirected mutagenesis. Analytical Biochemistry, Volume 314, Issue 2, Pages 310315. Paran, I and R. W. Michelmore. 1993. Development of reliable PCR based markers linked to downy mildew resistance genes in lettuce. Theor. Appl. Genet., 85: 985 ­ 99. Parent, J. G and D. Page. 1995. Evaluation of SCAR markers to identify raspberry cultivars. HortScience, 30: 856. (Abstract). Parida, S. K., S. K. Kalia, K. Sunita, V. Dalal, G. Hemaprabha, A. Selvi, A. Pandit, A. Singh, K. Gaikwad, T. R. Sharma, P. S. Srivastava, N. K. Singh, T. Mohapatra. 2009.

Informative genomic microsatellite markers for efficient genotyping applications in sugarcane. Theor Appl Genet., 118:327­338. Park, S. O., D. P. Coyne, J. R. Steadman, K. M. Crosby and M. A. Brick. 2004. RAPD and SCAR Markers Linked to the Ur-6 Andean Gene Controlling Specific Rust Resistance in Common Bean. Crop Sci., 44: 1799 - 1807. Proite, K., C. M. Soraya Leal-Bertioli, J. David Bertioli, C. Márcio Moretzsohn, D. A. Silva, R. Felipe, F. Natalia Martins and M. Patricia Guimarães. 2007. ESTs from a wild Arachis species for gene discovery and marker development. BMC Plant Biology, vol. 7, no. 7. Qadir, S.A. and Datta, S. and Singh, N.P. and Kumar, Shiv. 2007. Development of highly polymorphic SSR markers for chickpea (Cicer arietinum L.) and their use in parental polymorphism. Indian Journal of Genetics and Plant Breeding, 67 (4). pp. 329333. Rajesh, P. N., V. J. Sant, V. S. Gupta, F. J. Muehlbauer and P. K. Ranjekar. 2002. Genetic relationships among annual and perennial wild species of Cicer using inter simple sequence repeat (ISSR) polymorphism. Euphytica, 129: 15-23. Reddy, M. P., N. Sarla and E. A. Siddiq. 2002. Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica, 128: 9-17. Reisch, B. I., N. F. Weeden, M. A. Lodhi, G. Ye and G. Soylemezoglu. 1996. Linkage map construction in two hybrid grapevine (Vitis sp.) populations. In: Plant genome IV: Proceedings of the Fourth International Conference on the Status of Plant Genome Research. Maryland, USA: USDA, ARS, 26 (Abstract). Rishi, N. 2009. Significant plant virus diseases in India and a glimpse of modern disease management technology. J. Gen. Plant Pathol., 75: 1­18. Sankar, A. and G. A. Moore. 2001. Evaluation of inter simple sequence repeat analysis for mapping in citrus and extension of genetic linkage map. Theor. Appl. Genet., 102: 206-214.

Sarkar, G., J. Cassady, C. D. K. Bottema and S. S. Sommer. 1990. Characterization of polymerase reaction amplification of specific alleles. Anal. Biochem., 186: 64-68. Shanmugasundaram, S. 2007. Exploit mungbean with value added products. Acta Horticulture, 752, 99­102.

Seehalak, W., P. Somta, W. Sommanas and P. Srinives. 2009. Microsatellite markers for mungbean developed from sequence database. Molecular Ecology Resources, Volume 9, Issue 3, pages 862­864.

Sithichoke, T., P. Somta, P. Uthaipaisanwong, J. Chanprasert, D. Sangsrakru, W. Seehalak, W. Sommanas, S. Tragoonrung and P Srinives. 2009. Characterization of

microsatellites and gene contents fromgenome shotgun sequences of mungbean (Vigna radiata (L.)Wilczek). BMC Plant Biology, 9:137. Somta, P. and P. Srinives. 2007. Genome Research in Mungbean [Vigna radiata (L.) Wilczek] and Black gram [V. mungo (L.) Hepper]. ScienceAsia 33 Supplement, 1: 69-74. Somta P, Musch W, Kongsamai B, Chanprame S, Nakasathien S, Toojinda T, Sorajjapinun W, Seehalak W, Tragoonrung S, Srinives P. 2008. New microsatellite markers

isolated from mungbean (Vigna radiate (L.) Wilczek). Mol Ecol Resource, 8:11551157. Somta, P., W. Seehalak and P. Srinives. 2009. Development, characterization and crossspecies amplification of mungbean (Vigna radiata) genic microsatellite markers. Conserv Genet., 10:1939­1943. Souframanien, J., S. E. Pawar and A. G. Rucha. 2002. Genetic variation in gamma ray induced mutants in blackgram as revealed by random amplified polymorphic DNA and inter-simple sequence repeat markers. Indian. J. Genet., 62: 291-295. Souframanien, J and T. Gopalakrishna. 2004. A comparative analysis of genetic diversity in blackgram genotypes using RAPD and ISSR markers. Theor. Appl. Genet., 109: 1687­1693. Souframanien, J and Gopalakrishna. T. 2006. ISSR and SCAR markers linked to the mungbean yellow mosaic virus (MYMV) resistance gene in blackgram [Vigna mungo (L.) Hepper] J. Plant Breeding, 125: 619 - 622.

Squirrell, J., P. M. Hollingsworth, M. Woodhead, J. Russell, A. J. Lowe, M. Gibby, W. Powell. 2003. How much effort is required to isolate nuclear microsatellites from plant. Mol Ecol., 12:1339­ 1348. Sudha. 2009. An investigation on mungbean yellow mosaic virus (MYMV) resistance in mungbean [vigna radiata (l.) wilczek] and ricebean [Vigna umbellata (thunb.) Ohwi and Ohashi] interspecific crosses unpub. Ph D Thesis, Tamil Nadu Agric. Univ., Coimbatore-3, India. 96-123p. Sudupak, M. A. 2004. Inter and intra-species Inter Simple Sequence Repeat (ISSR) variations in the genus Cicer. Euphytica, 135: 229­238. Swag, J. G, J. W. Chung, H. K. Chung and J. H. Lee. 2006. Characterization of new microsatellite markers in Mung bean,Vigna radiata(L.). Mol. Ecol. Notes, 6: 11321134. Temnykh, S., G. DeClerck, A. Lukashova, L. Lipovich, S. Cartinhour, and S. McCouch. 2001. "Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential," Genome Research, vol. 11, no. 8, pp. 1441­1452. Tomooka, N., M. S. Yoon, K. Doi, A. Kaga and D. A. Vaughan. 2002b. AFLP analysis of diploid species in the genus Vigna subgenus Ceratotropis. Genet Res Crop Evol., 49: 521­ 530. Tomooka, N., A. Kaga and D.A. Vaughan. 2006. The Asian Vigna (Vigna subgenus Ceratotropis) biodiversity and evolution. In:Plant Genome; Biodiversity and Evolution. Science Publishers, Enfield, NJ., Volume 1, Part C. pp. 87­126. Toth, G., Z. Gaspari, and J. Jurka. 2000. Microsatellites in different eukaryotic genomes: survey and analysis. Genome Res., 10:967-981. Varshney, R. K., T. Thiel, N. Stein, P. Langridge and A. Graner. 2002. In silico analysis on frequency and distribution of microsatellites in ESTs of some cereal species. Cell Mol. Biol. Lett., 7:537-54.6. Vos, P., R. Hogers, M. Bleeker, M. Reijans,T. Van De Lee, M. Hornes, A. Frijters, J. Pot, J. Peleman and M. Kuiper. 1995. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res., 23: 4407-4414.

Wang, X. W., A. Kaga, N. Tomooka, D. A. Vaughan. 2004. The development of SSR markers by a new method in plants and their application to gene flow studies in azuki bean [Vigna angularis (Willd.) Ohwi & Ohashi]. Theor Appl Genet., 109:352­360. Wang, X.J., L. Su, X. Q. Quan, L. Shan, H. T. Zhang, and Y. P. Bi. 2006. Peanut (Arachis hypogaea L.) EST sequencing, gene cloning an Agrobacteria-mediated

transformation. Abstracts p. 59. In: International groundnut conference on groundnut Aflatoxin and genomics, Guangzhou, China. Welsh, J and M. Mc Clelland. 1992. Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Res., 19: 303 - 306. Yoon, M. S., A. Kaga, N. Tomooka and D. A. Vaughan. 2000. Analysis of genetic diversity in the Vigna minima complex and related species in East Asia. J. Plant Res., 113: 375­386. Zane, L., L. Bargelloni, T. Patarnello. 2002. Strategies for microsatellite isolation: a review. Mol Ecol., 11:1­16. Web reference Abajian, C. SPUTNIK, 1994, http://www.abajian.com/ Sputnik.

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