Open Access

Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits

  • Anjan Hazra1, 2,
  • Nirjhar Dasgupta1,
  • Chandan Sengupta2 and
  • Sauren Das1Email author
BMC Research Notes201710:261

https://doi.org/10.1186/s13104-017-2577-x

Received: 31 January 2017

Accepted: 28 June 2017

Published: 6 July 2017

Abstract

Tea (Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions are being worthwhile for reviewing the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea, the trait-specific Simple Sequence Repeat (SSR) markers, leading to be useful in marker assisted breeding technique, are yet to be identified. Micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and thus effects on related phenotype. Present study deals with identification of the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers from SSR flanking regions in order to PCR validation. In addition to the earlier reports, two new miRNAs are predicting here from tea expressed tag sequence database. Furthermore, 18 SSR motifs are found to be in 13 of all 33 predicted miRNAs. Trinucleotide motifs are most abundant among all followed by dinucleotides. Since, miRNA based SSR markers are evidenced to have significant role on genetic fingerprinting study, these outcomes would pave the way in developing novel markers for tagging tea specific agronomic traits as well as substantiating non-conventional breeding program.

Keywords

Micro RNA Simple sequence repeats Tea quality Trait specific marker

Introduction

Tea (Camellia sinensis) is most popularly consumed beverage across the world and being a cash-crop, it receives much attention to the scientific community. The vibrant research interest basically stands for its massive demand to health conscious people for its antioxidant and a broad spectrum therapeutic potentiality [1, 2]. China is the largest tea producer as well as exporter preceding by India and Sri Lanka [3]. The fermented tea or black tea is the most common among all different types of tea consumed, although antioxidant and other health benefit properties lies maximum on non-fermented green tea [4]. As a result, demand for quality tea has increased much to the end users and tea planters. Challenges for exploring superior cultivars with better agronomic traits are still point of interest to the researchers.

Molecular marker assisted technique in breeding programme for the selection or development of cultivars with desired trait from a large population is well established [5]. Among different markers used in crop improvement and molecular breeding technique, microsatellite markers are profoundly used for its reliability and time saving method. Moreover, due to being co-dominant, abundant, hyper-variable and co-operative to high-throughput analysis, microsatellite markers are considered as ideal for plant genetic linkage mapping, physical mapping, population studies, genotype identification and crop improvement [6]. Predominant of such markers like SSR, ISSR, EST-SSR have been effectively utilized in several crop improvement program [711]. MicroRNA precursor based SSR markers are very recently incorporated in this chapter and mostly utilized in marker trait association analysis in several species [1215]. These micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and [16] thus effects on related phenotype [17, 18]. There are a large number of molecular markers available for tea so far [19], however a very few are reported to be linked with some specific trait [20]. Therefore exploration and characterization of novel and already available markers are of prime point of interest. Considering the above, present study aims to identify the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers for PCR validation.

Materials and methods

Retrieval of data, filtering and trimming

Already predicted tea miRNA candidates were fished out from available literature [2123]. Furthermore, to screen if any pre miRNA were within the tea EST database updated so far (November 2016), standard methodology of miRNA screening [24] were followed with some minor customization. All available reported miRNAs of viridiplantae from online repository miRBase v21.0 [25] and the entire EST collection of tea were retrieved from NCBI dbEST [26], followed by elimination of redundant sequences and trimming polyA tails using PRINSEQ v0.20.4 [27].

Prediction of miRNAs

The set of published miRNAs were used for a homology search against tea EST collection and the best hits with a minimum length of 18 nucleotides and a maximum miRNA length cover up to 26 nucleotides and not more than 3 mismatches were taken for further analysis. After elimination of protein coding transcripts utilizing BLASTx [28], the remaining candidates were subjected to the prediction of stem-loop structure using Mfold [29] to check possibility of their pre-miRNA existence. The potential miRNA was mined considering the criteria: (a) position of mature miRNA on arm of the hairpin, (b) minimum paired residues in miRNA = 14 and unpaired residues not more than = 5, (c) maximum number of G–U pairs in miRNA = 5, (d) maximum bulge size of 3nt, (e) the negative minimal folding free energy (MFE) is low (≤−18 kcal/mol) [22], and (f) minimal folding free energy index (MFEI = [(MFE/length of the RNA sequence) * 100]/(G+C)%) is high (>0.85) [3032].

MicroRNA target predictions and their function

The exclusively predicted miRNAs were analyzed for their putative target genes employing the psRNATarget server [33] with default parameters. Subsequently, to recognize the functions of such predicted targets, they were undergone BLAST programme in NCBI. Finally a complete list was prepared taking all previously and presently reported tea miRNAs with their putative function.

Exploration of SSRs within predicted microRNAs

The simple sequence repeat motifs within all available and reported pre-miRNA sequences were investigated by the Websat online program [34]. The parameters were set for identifying perfect di-, tri-, tetra-, penta-, and hexa-nucleotide motifs with minimum repeat numbers of 6, 4, 3, 3 and 3 respectively.

Designing primers from SSR flanking region

The primer pairs from SSR flanking regions were designed with BatchPrimer3 server [35]. For the same, parameters were set as follows: length range = 18–23 nucleotides with 21 as optimum; PCR product size range = 100–400 bp [36]; optimum annealing temperature = 55 °C; and GC content 40–60%, with 50% as optimum.

Result and discussion

In the present study, previously reported miRNA sequences were utilized to find their homolog ones from tea as it is already known that plant mature miRNAs are highly conserved within the plant kingdom, and miRNA genes in one species may exist as orthologs or homologs in other species [30, 37]. With the help of this hypothesis, known miRNAs were utilized to discover novel potential miRNAs in tea. All 8442 miRNAs reported from viridiplantae so far were utilized and after elimination of 3676 exact duplicates using bioinformatics tool, a non-redundant collection was taken for further analysis. Similarly, tea EST collection of 49,670 sequences were made into a non-redundant 40,686 numbers. The BLAST search could fish out 52 number of ESTs with required level of homology i.e. minimum 18nt length similarity with not more than 3 mismatches. A total 15 of them already reported earlier as pre-miRNA [2123]. Leftover candidates were analyzed through Mfold program as the miRNA precursors should be able to form stem-loop hairpin in their secondary structure for processing by Dicer enzyme [38] and subsequently possible false miRNA precursors were manually removed. In present study two more potential pre-miRNA (Fig. 1) were identified with accession JK478587.1 and FS955851.1 for having miR1533 and miR8002-3p respectively.
Fig. 1

The predicted secondary step-loop structures of new tea pre-miRNAs with mature miRNA sequence highlighted in green

The identified pre-miRNAs belonging to two families had sequence lengths of 419 and 787 bp respectively (Table 1). Length variation was also evident in previous report [22, 39, 40]. Beside, commonly studied parameter in miRNA prediction is the minimum free energy (MFE) level which indicates the stability of RNA secondary structure [32] and longer pre-miRNA sequences generally have lower MFEs for maintaining its stability [41]. Here MFE values calculated by Mfold server were −89.99 and −155.67 kcal (Table 1). The MFE index or MEFI values were also calculated to distinguish miRNA from other RNAs precisely [41, 42]. Accordingly, plant pre-miRNA should have a MFEI greater than 0.85, whereas mRNAs, tRNAs, and rRNAs have a lower MFEI. In this study both identified candidate had MEFI values more than 1 indicating there possible existence in reality. Moreover sequence alignment of the new tea based miRNAs with its homolog ones from reported data showed only the initial 1 nucleotide was missing in case of miR1533 and a few nucleotides were missing from both ends and a single mismatch in entire length of miR8002-3p that strengthens the findings of this computational prediction.
Table 1

List of predicted microRNAs and occurrence of SSR motifs with designed primer

Predicted miRNA

Precusor id

MFE (−kCal)

References

SSR start

SSR end

SSR motif

SSR length

Primer F

Primer R

Product size

miR164

CV013669

20

[21]

      

miR169

GE650220

7.8

[21]

81

93

CGC

3

GGAGTGAAGGAGGGAAGA

CCGAGAGAATAATAAGGAGGA

205

miR1846

DN976181

40.5

[21]

      

miR1863

GH623864

19.2

[21]

224

236

GAG

3

AGTGATTATTGGTGGTGGTC

TCTCAACCAATTCAACAAGTC

152

miR 408

GD254786.1

120.1

[22]

519

541

TA

2

CTGTTACTGCAGCTTAACCAA

AAATATGCTGCTCATTCAAAC

152

 

142

154

GGC

3

AGAAGATGTCTCAGGGAAGAG

ACACAAGTATGTCACCAGCTC

177

csi-miR1171

FE943069.1

45.97

[22]

280

295

GTGGA

5

GAACCTTTCCTCCAGAATTTA

TCACATTTAGCTTTTCACTCC

162

csi-miR414a

GD254734.1

63.18

[22]

      

csi-miR414d

GW342817.1

37.72

[22]

170

188

GATGAC

6

ACGATGGTGGTTATGAATATG

GGGTTTTTGTTAAGTTGTTCA

139

csi-miR414f

GE651542.1

18.5

[22]

99

111

TTC

3

AGACAAAAACCAAGGCTAGAT

CATCTTGTGCAGATCTCAGTT

149

 

121

139

ATC

3

cas-miR1122

EU849076.1

91.23

[22]

      

csi-miR414 g

CV013826.1

25.2

[22]

504

519

CTC

3

TGATGATGAGGAAGGAGATAA

TTGCTTTAGTGAAACAACTCC

136

csi-miRf10132-akr

CV014169.1

69.5

[22]

      

cja-miR2910

U42815.1

−91

[22]

      

csi-miR2914

AB120309.1

20.9

[22]

      

cas-miRf10185-akr

GH623933.1

51.1

[22]

      

cas-miR11590-akr

GE651674.1

23.8

[22]

      

csi-miR414 h

GW863581.1

86.83

[22]

      

miR156

HS396956.1

45.8

[23]

113

125

AAC

3

CGTTAGGCTATTTTGTTTCAA

TTCTGTCAATCATCCAATTTC

159

miR171a

FS948108.1

39.2

[23]

115

127

TC

2

TACTTCCAACCAAACACAAGT

TAGCTTACCACCTCAATCAAA

168

miR171b

FS948109.1

40.4

[23]

      

miR397

CV699725.1

39.2

[23]

      

miR399

FS958856.1

52.8

[23]

      

miR2863

FS950435.1

21.5

[23]

57

69

TCTA

4

CTCCTGTACACTCTCTCTCTCC

GATGAACAGCATAGGTATCCA

127

miR2911a

JK476023.1

65.4

[23]

      

miR2911b

FS953337.1

69.2

[23]

      

miR5021a

GW690847.1

71.9

[23]

72

84

GGA

3

AGACACAGGCAGACATAGAGA

AAGATGCGATGAGATCAGATA

165

    

239

254

TTC

3

AGATACACATTGGGAAGAAGG

TAGAACTTGCAGAGAGAAACG

149

    

252

264

TC

2

miR5021b

GE651759.1

72.2

[23]

60

78

GA

2

TAGAACTTGCAGAGAGAATCG

TACACATTGGGAAGAAGAAGA

152

    

75

90

AGA

3

miR5368a

GE653011.1

76.1

[23]

      

miR5368b

FS945766.1

68.5

[23]

      

miR6483a

HS398296.1

29.8

[23]

      

miR6483b

JK714410.1

30

[23]

      

miR1533

JK478587.1

89.99

Reported

403

419

AC

2

   

miR8002-3p

FS955851.1

155.67

Reported

       

Plant microRNAs do regulate the transcripts expression for growth, development and stress responses by altering leaf morphology and polarity, organ development, lateral root formation, hormone signalling, cell death, signal transduction, cell differentiation and proliferation, transition from juvenile to adult vegetative phase, vegetative to flowering phase, flowering time, floral organ identity and reproduction [41, 4346]. Meanwhile, miRNAs are involved in the regulation of gene expression through mRNA cleavage or translational inhibition [47] has been reported, therefore obtaining information about target genes of potential microRNAs was an essential part of the study. Since the full genomic information is lacking in case of tea, DFCI Gene Index of Arabidopsis thaliana, Glycine max, and Zea mays, Solanum tuberosum etc. were used as target database. Among the presently reported two microRNAs, miR1533 were not found to have any target similarity with significant threshold values of input parameter. The other one, miR8002-3p were predicted to have cleavage activity on General transcription factor 2-related zinc finger protein (Target Accession: AT1G42710.1), phosphoglycerate mutase (Target Accession: TC194811). In addition the target genes of other predicted miRNAs of tea were explored from the literature to elucidate their role in pre-miRNA SSR based polymorphism assessment. Zhu and Luo [23] reported that their predicted miRNA target genes encoded transcriptional factors, involved in stress response, transmembrane transport, and signal transduction and transcription regulation. Target genes encoding transcription factors and cell integrity maintenance machinery during stress response was also mentioned by Prabu and Mandal [21]. Multiple target of a single miRNA is the system biology network, when they control expression of different transcription factors which in turn regulates specific genes for different metabolism [48].

Microsatellite markers are widely used tool for estimating the genetic variation and especially used for construction linkage map, understanding of marker trait association, identification of disease resistant loci [14, 49]. The distribution of simple sequence repeats or SSRs in the genome is inherently unstable and therefore highly polymorphic [50]. People assumed that increment of the repeat unit and repeat tracts gives rise the chances of the mutation rate [51]. Some reports have already established the fact that SSR expansions or contractions within genome sequences can affect functions of these sequences and even lead to phenotypic changes [17]. Evidences have shown the effect of SSR unit variation within protein coding regions. However, the consequences of the same in non-coding transcripts are less studied. A very few reports demonstrated the significance of analysis of SSRs in non-coding miRNA [1215, 49]. In current study, 13 of 33 total predicted pre-miRNAs had one or more SSR motifs (Table 1). A total of 9 sequences had SSR motif in a single region, whereas 3 sequences contained SSR motifs in two locations and 1 sequence was with 3 different SSR motifs. Trinucleotide motifs were most abundant among all followed by dinucleotides. There was one each of penta and hexa-nucleotide motifs. Forward and reverse primers from the each microsatellite motifs flanking region could be generated in all members excluding only one where SSR motif present toward the terminal end. The microsatellite motifs may be conserved among group or become signature [13] which might be used in studies of genetic fingerprinting work of tea. Some traits rely on specific repeats of microsatellites and their numbers. Such advantage has been efficiently employed by rice researchers when miRNA-SSR markers were employed to differentiate the salt tolerant and susceptible genotypes [15]. They found more repeat variation of the salt responsive miRNA genes among the susceptible rice genotypes than tolerant one. Such extensive work can be applied in tea to distinguish the cultivars with varying agronomic traits on the basis of miRNA-SSR polymorphism.

Finally, newly predicted microRNAs in tea would enrich the assemblage in absence of whole genomic information of tea and make easier subsequent studies for experimental validation. Some of them might be related to certain metabolic functions thereby phenotypes as well. Excavating of microsatellite motifs from predicted microRNA precursors and designing primers from SSR flanking regions would pave the way in developing novel markers for tagging tea specific agronomic traits as well as accelerating non-conventional breeding program. This can freely be followed by genetic diversity assessment of tea cultivars with varying characters.

Declarations

Authors’ contributions

AH and SD have conceived the work, designed methodology, interpreted data and written the manuscript, NDG and CS participated in data interpretation and manuscript writing. All authors read and approved the final manuscript.

Acknowledgements

AH is thankful to National Tea Research Foundation, Tea Board, India for providing research fellowship.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data retrieved from public database, not deposited any.

Funding

National Tea Research Foundation, Tea Board, India.

Publisher’s Note

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Authors’ Affiliations

(1)
Agricultural and Ecological Research Unit, Indian Statistical Institute
(2)
Department of Botany, University of Kalyani

References

  1. Hayat K, Iqbal H, Malik U, Bilal U, Mushtaq S. Tea and its consumption: benefits and risks. Crit Rev Food Sci Nutr. 2015;55(7):939–54.View ArticlePubMedGoogle Scholar
  2. Khan N, Mukhtar H. Tea and health: studies in humans. Curr Pharm Des. 2013;19(34):6141–7.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Basu Majumder A, Bera B, Rajan A. Tea statistics: global scenario. Inc J Tea Sci. 2010;8(1):121–4.Google Scholar
  4. Carloni P, Tiano L, Padella L, Bacchetti T, Customu C, Kay A, et al. Antioxidant activity of white, green and black tea obtained from the same tea cultivar. Food Res Int. 2013;53(2):900–8.View ArticleGoogle Scholar
  5. Bang H, Kim S, Leskovar D, King S. Development of a codominant CAPS marker for allelic selection between canary yellow and red watermelon based on SNP in lycopene β-cyclase (LCYB) gene. Mol Breed. 2007;20(1):63–72.View ArticleGoogle Scholar
  6. Morgante M, Olivieri A. PCR-amplified microsatellites as markers in plant genetics. Plant J. 1993;3(1):175–82.View ArticlePubMedGoogle Scholar
  7. Roychowdhury R, Taoutaou A, Hakeem KR, Gawwad MRA, Tah J. Molecular marker-assisted technologies for crop improvement. In: Roychowdhury R, ed. Crop improvement in the era of climate change; 2013: p. 241–58.Google Scholar
  8. Kumar S, Rajendran K, Kumar J, Hamwieh A, Baum M. Current knowledge in lentil genomics and its application for crop improvement. In: Kumar S, editor. Crop breeding: bioinformatics and preparing for climate change. USA: CRC Press; 2016. p. 309–27.View ArticleGoogle Scholar
  9. Varshney RK, Graner A, Sorrells ME. Genic microsatellite markers in plants: features and applications. Trends Biotechnol. 2005;23(1):48–55.View ArticlePubMedGoogle Scholar
  10. Singh RB, Srivastava S, Rastogi J, Gupta GN, Tiwari NN, Singh B, et al. Molecular markers exploited in crop improvement practices. Res Environ Life Sci. 2014;7(4):223–32.Google Scholar
  11. Kesawat MS, Kumar BD. Molecular markers: it’s application in crop improvement. J Crop Sci Biotechnol. 2009;12(4):169–81.View ArticleGoogle Scholar
  12. Wang X, Gui S, Pan L, Hu J, Ding Y. Development and characterization of polymorphic microRNA-based microsatellite markers in Nelumbo nucifera (Nelumbonaceae). Appl Plant Sci. 2016;4(1):1500091.View ArticleGoogle Scholar
  13. Nithin C, Patwa N, Thomas A, Bahadur RP, Basak J. Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures. BMC Plant Biol. 2015;15(1):140.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Ganie SA, Mondal TK. Genome-wide development of novel miRNA-based microsatellite markers of rice (Oryza sativa) for genotyping applications. Mol Breed. 2015;35(1):51.View ArticleGoogle Scholar
  15. Mondal TK, Ganie SA. Identification and characterization of salt responsive miRNA-SSR markers in rice (Oryza sativa). Gene. 2014;535(2):204–9.View ArticlePubMedGoogle Scholar
  16. Großhans H, Filipowicz W. Molecular biology: the expanding world of small RNAs. Nature. 2008;451(7177):414–6.View ArticlePubMedGoogle Scholar
  17. Fondon JW, Garner HR. Molecular origins of rapid and continuous morphological evolution. Proc Natl Acad Sci. 2004;101(52):18058–63.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Kashi Y, King DG. Simple sequence repeats as advantageous mutators in evolution. Trends Genet. 2006;22(5):253–9.View ArticlePubMedGoogle Scholar
  19. Mukhopadhyay M, Mondal TK, Chand PK. Biotechnological advances in tea (Camellia sinensis [L.] O. Kuntze): a review. Plant Cell Rep. 2016;35(2):255–87.View ArticlePubMedGoogle Scholar
  20. Elangbam M, Misra A. Development of CAPS markers to identify Indian tea (Camellia sinensis) clones with high catechin content. Genet Mol Res. 2016;15(2):1–13.View ArticleGoogle Scholar
  21. Prabu G, Mandal A. Computational identification of miRNAs and their target genes from expressed sequence tags of tea (Camellia sinensis). Genom Proteom Bioinform. 2010;8(2):113–21.View ArticleGoogle Scholar
  22. Das A, Mondal TK. Computational identification of conserved microRNAs and their targets in tea (Camellia sinensis). Am J Plant Sci. 2010;1(02):77.View ArticleGoogle Scholar
  23. Q-w Zhu, Y-p Luo. Identification of miRNAs and their targets in tea (Camellia sinensis). J Zhejiang Univ Sci B. 2013;14(10):916–23.View ArticleGoogle Scholar
  24. Zhang B, Pan X, Anderson TA. Identification of 188 conserved maize microRNAs and their targets. FEBS Lett. 2006;580(15):3753–62.View ArticlePubMedGoogle Scholar
  25. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36(suppl 1):D154–8.PubMedGoogle Scholar
  26. Boguski MS, Lowe TM, Tolstoshev CM. dbEST—database for “expressed sequence tags”. Nat Genet. 1993;4(4):332–3.View ArticlePubMedGoogle Scholar
  27. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27(6):863–4.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.View ArticlePubMedGoogle Scholar
  29. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003;31(13):3406–15.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA. Conservation and divergence of plant microRNA genes. Plant J. 2006;46(2):243–59.View ArticlePubMedGoogle Scholar
  31. Li X, Hou Y, Zhang L, Zhang W, Quan C, Cui Y, et al. Computational identification of conserved microRNAs and their targets from expression sequence tags of blueberry (Vaccinium corybosum). Plant Signal Behav. 2014;9(9):e29462.View ArticlePubMed CentralGoogle Scholar
  32. Zhang B, Pan X, Cox S, Cobb G, Anderson T. Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci. 2006;63(2):246–54.View ArticlePubMedGoogle Scholar
  33. Dai X, Zhao PX. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res. 2011;39(suppl 2):W155–9.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Martins WS, Lucas DCS. Neves KdS, Bertioli DJ. WebSat—a web software for microsatellite marker development. Bioinformation. 2009;3(6):282–3.View ArticlePubMedPubMed CentralGoogle Scholar
  35. You FM, Huo N, Gu YQ, M-c Luo, Ma Y, Hane D, et al. BatchPrimer3: a high throughput web application for PCR and sequencing primer design. BMC Bioinform. 2008;9(1):253.View ArticleGoogle Scholar
  36. Yu Y, Yuan D, Liang S, Li X, Wang X, Lin Z, et al. Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC 1 population between Gossypium hirsutum and G. barbadense. BMC Genom. 2011;12(1):15.View ArticleGoogle Scholar
  37. Weber MJ. New human and mouse microRNA genes found by homology search. FEBS J. 2005;272(1):59–73.View ArticlePubMedGoogle Scholar
  38. Kurihara Y, Watanabe Y. Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions. Proc Natl Acad Sci USA. 2004;101(34):12753–8.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Biswas S, Hazra S, Chattopadhyay S. Identification of conserved miRNAs and their putative target genes in Podophyllum hexandrum (Himalayan Mayapple). Plant Gene. 2016;6:82–9.View ArticleGoogle Scholar
  40. Patanun O, Lertpanyasampatha M, Sojikul P, Viboonjun U, Narangajavana J. Computational identification of microRNAs and their targets in cassava (Manihot esculenta Crantz.). Mol Biotechnol. 2013;53(3):257–69.View ArticlePubMedGoogle Scholar
  41. Bonnet E, Wuyts J, Rouzé P, Van de Peer Y. Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics. 2004;20(17):2911–7.View ArticlePubMedGoogle Scholar
  42. Zhang BH, Pan XP, Wang QL, George PC, Anderson TA. Identification and characterization of new plant microRNAs using EST analysis. Cell Res. 2005;15(5):336–60.View ArticlePubMedGoogle Scholar
  43. Pandey B, Gupta OP, Pandey DM, Sharma I, Sharma P. Identification of new stress-induced microRNA and their targets in wheat using computational approach. Plant Signal Behav. 2013;8(5):e23932.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Mallory AC, Vaucheret H. Functions of microRNAs and related small RNAs in plants. Nat Genet. 2006;38:S31–6.View ArticlePubMedGoogle Scholar
  45. Wang X-J, Reyes JL, Chua N-H, Gaasterland T. Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol. 2004;5(9):R65.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Chen X. Small RNAs and their roles in plant development. Annu Rev Cell Develop. 2009;25:21–44.View ArticleGoogle Scholar
  47. Chen R, Hu Z, Zhang H. Identification of microRNAs in wild soybean (Glycine soja). J Integr Plant Biol. 2009;51(12):1071–9.View ArticlePubMedGoogle Scholar
  48. Zhang B, Pan X, Cobb GP, Anderson TA. Plant microRNA: a small regulatory molecule with big impact. Develop Biol. 2006;289(1):3–16.View ArticlePubMedGoogle Scholar
  49. Chen M, Tan Z, Zeng G, Peng J. Comprehensive analysis of simple sequence repeats in pre-miRNAs. Mol Biol Evol. 2010;27(10):2227–32.View ArticlePubMedGoogle Scholar
  50. Heesacker A, Kishore VK, Gao W, Tang S, Kolkman JM, Gingle A, et al. SSRs and INDELs mined from the sunflower EST database: abundance, polymorphisms, and cross-taxa utility. Theor Appl Genet. 2008;117(7):1021–9.View ArticlePubMedGoogle Scholar
  51. Katti MV, Ranjekar PK, Gupta VS. Differential distribution of simple sequence repeats in eukaryotic genome sequences. Mol Biol Evol. 2001;18(7):1161–7.View ArticlePubMedGoogle Scholar

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