http://orcid.org/0000-0002-9540-0458
Figures
Abstract
The present investigation was carried out aiming to use the bioinformatics tools in order to identify and characterize, simple sequence repeats within the third Version of the date palm genome and develop a new SSR primers database. In addition single nucleotide polymorphisms (SNPs) that are located within the SSR flanking regions were recognized. Moreover, the pathways for the sequences assigned by SSR primers, the biological functions and gene interaction were determined. A total of 172,075 SSR motifs was identified on date palm genome sequence with a frequency of 450.97 SSRs per Mb. Out of these, 130,014 SSRs (75.6%) were located within the intergenic regions with a frequency of 499 SSRs per Mb. While, only 42,061 SSRs (24.4%) were located within the genic regions with a frequency of 347.5 SSRs per Mb. A total of 111,403 of SSR primer pairs were designed, that represents 291.9 SSR primers per Mb. Out of the 111,403, only 31,380 SSR primers were in the genic regions, while 80,023 primers were in the intergenic regions. A number of 250,507 SNPs were recognized in 84,172 SSR flanking regions, which represents 75.55% of the total SSR flanking regions. Out of 12,274 genes only 463 genes comprising 896 SSR primers were mapped onto 111 pathways using KEGG data base. The most abundant enzymes were identified in the pathway related to the biosynthesis of antibiotics. We tested 1031 SSR primers using both publicly available date palm genome sequences as templates in the in silico PCR reactions. Concerning in vitro validation, 31 SSR primers among those used in the in silico PCR were synthesized and tested for their ability to detect polymorphism among six Egyptian date palm cultivars. All tested primers have successfully amplified products, but only 18 primers detected polymorphic amplicons among the studied date palm cultivars.
Citation: Mokhtar MM, Adawy SS, El-Assal SE-DS, Hussein EHA (2016) Genic and Intergenic SSR Database Generation, SNPs Determination and Pathway Annotations, in Date Palm (Phoenix dactylifera L.). PLoS ONE 11(7): e0159268. https://doi.org/10.1371/journal.pone.0159268
Editor: Chunxian Chen, USDA/ARS, UNITED STATES
Received: April 7, 2016; Accepted: June 29, 2016; Published: July 19, 2016
Copyright: © 2016 Mokhtar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Date palm (Phoenix dactylifera L., 2n = 36) [1] is one of the most economic important fruit trees in the Middle East and North Africa. It is a dioecious, cross pollinated, perennial monocotyledon and belongs of the order Arecaceae [2]. It has been cultivated in the Middle East from about 4000 B.C. [3]. There are over 2000 varieties that vary in shape, color, size, and weight [4]. Despite such a large number of varieties, for many years the detection of genetic variation in date palm was relying on the morphological variation between cultivars [5]. Morphological variations are uncertain and unstable because they could be affected by environmental factors, epistasis, pleiotropic effects and other factors. Therefore, as long as breeding programs depended on morphological markers alone, the pace of progress was bound to be slow. During the last few decades molecular markers were employed to detect the genetic polymorphism more accurately, and as useful tools in crop improvement and breeding programs. Among the most widely used molecular markers are amplified fragment length polymorphism (AFLP) [6], simple sequence repeats (SSRs) [7], randomly amplified polymorphic DNA (RAPD) and inter simple sequence repeats (ISSR) [8]. Compared with other types of molecular markers, SSRs have many advantages, such as simplicity, effectiveness, abundance, hyper variability, reproducibility, co-dominant inheritance and extensive genomic coverage [9].
In recent years date palm has been subjected to intensive genome sequencing studies. Yang et al. [10] reported the complete chloroplast genome sequence. The mitochondrial genome was assembled by Fang et al. [11] with an approximate length of about 715,001 bp. While, the first nuclear genome sequence of date palm was published by Al-Dous et al. [12]. It covered ~60% of the genome which represent ~380 Mb (25,059 genes) out of the 658Mb estimated genome size. They recognized more than 3.5 million polymorphic sites among the nine investigated varieties of date palm. Also, Al-Mssallem et al. [13] reported the second nuclear genome assembly which is about 605.4 Mb and covers 90% of the date palm genome.
Nowadays, the commonly published genome data and the availability of bioinformatics tools provide the scientists with tools to identify simple sequence repeats that could be used to develop useful SSR markers. Hamwieh et al. [7] surveyed the first Version (VS 01) of date palm genome and designed 1091 SSR primers. Zhao et al. [14] also successfully designed 4,967 EST-SSRs primers according to the available date palm expressed sequence tags (ESTs).
The present investigation was carried out aiming to: 1) identify and characterize, simple sequence repeats within the third Version (VS 3.0) of the date palm genome; 2) comparatively analyze genic and intergenic SSR motifs and develop a new SSR primers database; 3) recognize all single nucleotide polymorphisms (SNPs) that are located within the SSR flanking regions and 4) annotate the pathways for the sequences assigned by SSR primers and consequently determine the biological functions and gene interaction
Materials and Methods
Source of genomic sequences and SNPs Data
In this study, we used the Version 3.0 of the date palm draft genome sequence of Khalas female variety published by Al-Dous et al. [12]. It contains 57,277 scaffolds, about 381 Mb in length and covers about 60% of the genome sequence. Also, the 3,518,029 SNPs covering the whole genome were employed. All genome sequences and SNPs data were downloaded from http://qatar-weill.cornell.edu/research/datepalmGenome/download.html.
SSR motifs mining and primer design
The Perl script MISA (MIcroSAtellite identification tool; http://pgrc.ipk-gatersleben.de/misa) and primer3_core [15] were used. These were inserted in the pipeline tool developed by the research team to identify and design SSR primers and to link it to SNPs that are located within the SSR flanking regions. A total of 57,277 scaffolds containing ~381 Mb sequences were used to determine SSR motifs with the following criteria: the repeat number in mononucleotide (mono-) ≥ 10 (bp), in dinucleotide (di-) ≥ 6 bp, in trinucleotide (tri-), tetranucleotide (tetra-), pentanucleotide (penta-), and hexanucleotide (hexa-) ≥ 5 bp. The maximal number of bases interrupting two SSR motifs in a compound microsatellite was 100 bp. SSR primers were designed to satisfy the following criteria: an optimum primer length of 20 bp, the optimum melting temperature of 55°C, the product size range of 100–500 bp, and a 50% optimum G/C content. The presence of SSRs was investigated in the genic and intergenic regions. These regions were determined according to previous classification of the genome sequences by Al-Dous et al. [12].
Validation of the designed SSR primers
Two methods were used to validate our designed SSR primers. The first method was in silico validation by applying the bioinformatics tools to test 1031 SSR primer pairs using the primer search program:EMBOSS:6.5.7.0, among the two date palm genomes published by Al-Dous et al. [12] and Al-Mssallem et al. [13]. The first genome sequence was used to design our SSR primers which were given the name(PDK30 sequence) [12]. While, the second sequence is a whole genome shotgun sequence downloaded from the Genbank with BioProject: PRJNA83433 (http://www.ncbi.nlm.nih.gov) and designated as (ATBV01 sequence) [13].
In vitro validation was used as the second method. This was performed through designing and testing 31 SSR primer pairs, randomly selected from the 1031 primers previously tested in silico. In vitro test was applied to investigate the polymorphism among six Egyptian date palm cultivars (Zaghloul, Hayany, Samany, Barhee, Sewi and Bartamoda). Total genomic DNA was extracted from date palm leaves, using the DNeasy Plant Kit (QIAGEN). The PCR amplification reaction was prepared in 25 μl containing 50 ng of DNA, 2.5 mM MgCl2, 5 μl 5X PCR buffer, 0.5 mM each SSR primer, 0.5U of GoTaq Flexi DNA Polymerase, and 2.5 mM dNTPs. The PCR temperature profile was 95°C for 5 min, followed by 35 cycles of 95°C for 50 s, the annealing temperature for each SSR primer (shown on S1 & S2 Files) for 55 s, 72°C for 50 s, and the final extension step at 72°C for 10 min. The PCR products were checked and visualized on a 1.5% agarose gel in TBE buffer at 100 W for 120 min.
Enzymes and pathways annotation
The Blast2GO program [16] was used to determine the enzyme codes (EC numbers) or enzyme annotations for 12,274 genes that contain 23,957 SSR primer pairs. The Blast2GO program provides EC annotation through the blast of all sequences against the NCBI database, then determine the gene ontology (GO) annotation. Consequently, the enzyme codes are highlighted on KEGG maps.
Results and Discussion
The present study was based on the date palm genome sequences data published by Al-Dous et al. [12] and Al-Mssallem et al. [13].
Distribution of the different SSR motifs in the date palm genome
Searching for SSRs in the PDK30 sequence [12] revealed that out of the examined 57,277 date palm scaffolds, 28,394 (49.5%) contain SSR repeats satisfying the chosen criteria for detecting SSRs. The number of identified SSR motifs was 172,075, with a frequency of 450.97 SSRs per Mb. The results of identified SSR motifs are shown in Table 1.
The SSRs motifs were not equally distributed in the genic and intergenic regions. Out of the 172,075 SSR motifs, 130,014 SSRs (75.6%) were located within the intergenic regions with a frequency of 499 SSRs per Mb. While, 42,061 SSRs (24.4%) were located within the genic regions with a frequency of 347.5 SSRs per Mb. Interestingly, Zhang et al. [17] surveyed the full-length of 30,854 genes generated from (assembled ESTs) of date palm, and identified 2,043 SSRs motifs. This number is lower than the number of SSRs identified in the present study. This could be due to the different criteria and the use of the full genome sequence adopted in our investigation.
Among the genic regions 1,785 SSRs were located in exons. This represents about 4.24% of the total genic regions (121018751 bp). However, 40,276 SSRs (95.76%) were located within the introns. The details of the identified SSRs in date palm are summarized in the S3 File.
Our investigation of the different SSR motifs present in the intergenic region showed that the most abundant type was the mono- (63.6%), followed by the di- (27.8%), tri- (6.2%), tetra-(2%), penta- (0.3%) and hexa- (0.1%). However, in the introns of the genic regions the percentages of mono-, di-, tri-, tetra-, penta- and hexa- repeats were 62.7%, 29.3%, 5.8%, 1.9%, 0.2% and 0.1% respectively. While in the exons, the most frequent type was the tri-, followed by the mono-, di-, hexa- and tetra- repeats (86.2%, 7.6%, 5.3%, 0.6% and 0.3%, respectively). The penta- repeat was not found in the exon regions (Fig 1).
Similarly, Zhao et al. [14] reported that the trinucleotide repeats were the most abundant in the exon regions. In general, the distribution of SSR motifs in the genic regions was consistent with Zhang et al. [17]. However, all the previous studies on date palm did not mention the presence of mononucleotide repeats [7,14,17]. To our knowledge this is the first investigation that shed light on the presence of the mononucleotide repeats in date palm.
The total population of the detected SSRs across the genome was classified as non-triplet repeats (mono, di, tetra and penta) and triplet repeats (tri and hexa). The results are summarized in Tables 2 and 3. As shown in Table 2, the frequency of the different non-triplet types with different repeat number was variable in the genic and intergenic regions. In general, there was a negative correlation between the number of repeats and the number of detected nontriplet motifs. Concerning the penta repeats, although the number was considerably lower in the genic than intergenic regions, however, they were confined to the 5–9 number of repeats.
As shown in Table 3, the number of trinucleotide repeats decreased when the number of repeats increased from 5 to 35 repeats (from 3745 to 0 in the genic region and 7259 to 2 in the intergenic region). Afterwards these numbers started to increase but with very low frequency. While, the number of hexanucleotide repeats was considerably lower than all the other repeat types and they were confined to the 5 to 9 repeat number.
The high appearance of tri repeats in coding regions was consistent with previous studies in plant genomes [18–24]. Fig 2 illustrates the mono, di and tri repeat sequences, number, and locations of the repeats whether in the genic or intergenic regions using the Circos software developed by Krzywinski et al. [25].
The numbers in boxes (1, 2, and 3) and (4, 5 and 6) represent the repeat types in the genic and intergenic regions, respectively. Similar repeats in the genic and intergenic regions are labeled with the same color. Numbers with pink color represent the number of repeats in the genic regions, while those with blue color refer to the intergenic regions. The latter data are illustrated with the same colors as histograms.
Concerning the other types of SSR repeats, 134 SSRs (tetra, penta and hexa) repeats were present in both genic and intergenic regions (Fig 3). While, 48 repeats were discovered in the genic regions only and comprised 11 tetra, 9 penta and 28 hexa repeats. In addition, 236 repeats were unique to the intergenic regions. The latter comprised 63 tetra, 84 penta and 88 hexa repeats. The present study represents the first report concerning the comparison of these specific SSR motifs (mono, di, tri, tetra, penta and hexa) in both genic and intergenic regions in the date palm genome.
The outer most circle in blue colored letters represents the different types of repeats in the intergenic regions. While, the pink colored numbers refer to the number of their respective repeats. The middle circle in red colored letters illustrates the types of repeats present in both genic and intergenic regions. While, the number of each repeat was yellow colored for the genic and black colored for the intergenic regions. The inner most circle with green colored letters shows the repeats unique to the genic regions, and the dark red colored numbers reveal the respective number of repeats. The orange, blue and green rectangles refer to tetra, penta and hexa repeats, respectively.
Among the tri repeats, 60 different repeat sequences were determined Fig 2. The tri repeats are of special importance, since some of these repeats could represent coding sequences for amino acids, start or stop codons. This has also been suggested by Pinto et al. [26] and Kaur et al. [27]. They explained that trinucleotide repeats, can be accommodated more readily within coding regions, as changes in their length simply result in gain or loss of a single amino acid from a protein sequence.
In this context, Morgante et al. [20], Li et al. [28]and Kalia et al. [29] reported that in many species, triplet SSRs are the most abundant repeats in the exons. They claimed that this could be due to the suppression of non–trimeric SSRs in coding regions which could be the result of change in reading frame with increase or decrease in number of repeat units and the negative selection against frame shift mutations in coding regions. In the present study, the most frequent tri-repeat was AAG as its frequency was 261 in the coding regions. This finding is in good accordance with Li et al. [28].They mentioned that in plants, the most frequent triplet is AAG. However, [30–32]found that in cereals, the most common triplet is CCG. Moreover, [29,33]mentioned that dicots have more mononucleotide repeats and monocots have more trinucleotide repeats.
In non-coding regions the rate of base substitution is high [29], however it could also occur in coding regions. Therefore, we surveyed all tri repeat types in the exons to detect probable substitutions that could convert an amino acid to a stop codon. Seventeen repeat sequences were detected Table 4.
SSR primers Design
A total of 111,403 SSR primer pairs were designed, that represents 291.9 SSR primers per Mb and covers the amplification of 22,652,015 bp (5.9%) of the total genome. Out of the 111,403, only 31,380 SSR primers were developed in the genic regions. These primers could amplify about 7,727,736 bp (6.38%) of the total genic regions. However, 80,023 primers covered the amplification of about 14924279 bp (5.72%) of the total intergenic regions (Fig 4).
The first column (left) represents the total genome length and total length of primers product, and the other two columns represent total length of primers product comparing to the total length of genic and intergenic regions.
Using the Version 1.0 date palm genome, Hamwieh et al. [7] designed only 1091 SSR primers. However, in the present study, we used Version 3.0 of date palm genome and succeeded to design 111,403 SSR primers in both genic and intergenic regions. To distinguish between our SSRs and other SSR primers developed on date palm genome, such as those previous developed [14,34,35], we named the Genic and intergenic SSR primers as Pd_G_SSRx and Pd_IG_SSRx, respectively (where Pd_G_SSR refers to Phoenix dactylifera Genic SSR primers, and Pd_IG_SSR was refers to Phoenix dactylifera Intergenic SSR primers and x refers to serial number).
Determination and Distribution of SNPs located within SSRs flanking regions
SNPs data were downloaded from http://qatarweill.cornell.edu/research/datepalmGenome/download.html, and were subjected to Perl script developed by our research team to recognize all SNPs located in the SSR flanking regions. A total of 250,507 SNPs were determined in 84,172 SSR regions which represent 75.55% of the total SSR flanking regions. Therefore, the frequency of SNPs per SSR flanking region was 2.97. The distribution of these SNPs among the genome revealed that a number of 186,953 SNPs was located in 60,764 intergenic SSR flanking regions, while 63,554 SNPs were located in 23,408 genic SSR flanking regions (Fig 5). Thus, the high range of variation among the date palm genotypes could be attributed to the high record of SNPs in the genic regions.
Development of date palm SSR primers database
The newly designed SSR primer pairs and related SNPs within the genic and intergenic regions were listed in the (S1 and S2 Files, respectively representing the two different databases). Each database contains information concerning the name of primers, repeats type, repeats sequence, repeats size, repeats start and end in the amplified sequences, forward and reverse primer sequences, primers annealing temperature, GC%, product size, corresponding genomic sequences, contig ID within date palm genome, gene product, gene ID (start and end on genome) and position of SNPs within SSRs flanking region. In addition, the gene product and SNPs location as designated by Al-Dous et al. [12] have been also incorporated in the developed database.
Detection of SSRs in disease resistance related genes
The developed genic database was exploited to recognize the designed SSR primer pairs within the disease resistance related genes. A total of 42 SSR primer pairs were located within 16 different genes related to disease resistance. These results are summarized in Table 5.
Validation of the designed primers
To validate our designed SSR primers, the first test was in silico PCR. We tested 1031 SSR primers using the primersearch program: EMBOSS: 6.5.7.0. Both publicly available date palm genome PDK Version 3.0 (PDK30) sequence [12] and ATBV01 sequence [13]were used as templates in the in silico PCR reactions. When using the date palm genome PDK30 sequence[12], all the 1031 SSR primer pairs successfully found 2450 complementary sequences with an average number of 2.37 alleles. However, only 903 SSR primers could successfully hit with 2774 complementary sequences in ATBV01 sequence [13] with an average number of 2.69 alleles. These results are summarized in (Table 6 and S4 File).
Concerning the in vitro validation, 31 SSR primers among those used in the in silico PCR were synthesized and tested for their ability to detect polymorphism among six Egyptian date palm cultivars. All tested primers have successfully amplified products, but only 18 primers detected polymorphic amplicons among the studied date palm cultivars (Fig 6).
The comparisons between the results of the in silico and in vitro PCR are summarized in Table 7. The results from both in silico and in vitro PCR were consistent in the number and length of alleles for some primers, while other primers recorded polymorphism in fragments sequence and differences in some alleles length. Moreover, some alleles could be detected in one genome and absent in other genome sequence. As shown in Table 7, out of the 104 alleles detected in vitro only 23 were consistent in both in silico and in vitro PCR.
Functional classification by KEGG pathway analyses
The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database is a useful tool for understanding genes biological functions and their molecular interactions[36]. We used the Blast2GO program [16] to determine the Enzyme (EC numbers) or Enzyme annotations for 12,274 genes that contain 23,957 SSR primers. The Blast2GO program provides EC annotation through the blast of all sequences against the NCBI database, then determine the gene ontology (GO) annotation, consequently, the enzyme codes are highlighted on KEGG maps.
Based on the basic parameters of the Blast2GO program, the blast results revealed that 3787 sequences were partial genes, thus, this number was excluded from the next steps of analysis. The remaining 8487 sequences showed blast result path to gene ontology (GO) annotation step. This step indicated 3201 sequences with no GO terms, while, 5286 sequences were assigned to 13554 GO terms, ranging from 1 to 45 GO terms per sequence. Sequences with GO terms were introduced into the EC number detect step. This analysis revealed only 463 unique sequences (genes), which encode an actual number of 258 enzymes distributed among 111 different pathways. These genes comprised 896 SSR primers because each gene sequence might contain more than one SSR primer. The sequences with blast results, GO terms and EC numbers were listed in S5 File and the position of these genes within the genome and the SSR primers name for each gene were listed in S6 File.
The data for the pathways name, name of each enzyme and the position of their coding genes within the genome, in addition to the EC numbers for each enzyme were listed in S7 File.The most abundant enzymes were identified in the pathways related to: (1) the biosynthesis of antibiotics (63 enzymes encoded by 83 genes); (2) starch and sucrose metabolism (23 enzymes encoded by 57 genes); (3) glycolysis/Gluconeogenesis (18 enzymes encoded by 32 genes) and (4) amino sugar and nucleotide sugar metabolism (16 enzymes encoded by 18 genes). While, only one enzyme encoded by one gene was identified in the pathway related to insect hormone biosynthesis. The detected pathways, the number of enzymes and number of genes encoding the enzymes represented as histograms, are illustrated in (Fig 7).
The middle part of the figure shows the pathways name. The outer part of the figure reveals the histogram, number of enzymes and number of genes encoding the enzymes in each pathway (where the purple color refers to the number of enzymes in the pathway and the blue color refers to the number of genes encoding the enzyme).
To confirm the pathway annotation results, two pathways, i.e., the starch and sucrose metabolism and the amino sugar and nucleotide sugar metabolism pathways were selected to apply the Blast2GO program. This program highlighted the identified Enzyme codes on the respective KEGG map as shown in Figs 8 and 9, respectively.
Each enzyme code is highlighted with different color.
Each enzyme code is highlighted with different color.
Conclusion
This study extends the basic research on date palm in an attempt to promote applying genomics and bioinformatics to enhance breeding and improvement programs of this important crop. The comparative studies depending on SSR motifs are useful since they represent co-dominant markers, highly polymorphic regions and could be used in genetic diversity analysis and breeding programs through designing specific primers for these regions. Genic SSR primers are helpful to detect the polymorphism within functional regions. Therefore, the breeder can choose the appropriate primers according to their localization in the biological pathways related to the trait of interest. Moreover, SNPs in the SSR flanking regions could provide an additional advantage to the breeders. Furthermore, this investigation highlights the usefulness of the in silico PCR for testing the primers before their use in vitro, thus saving time, effort and cost.
Supporting Information
S1 File. The newly designed SSR primer pairs and related SNPs within the genic regions.
https://doi.org/10.1371/journal.pone.0159268.s001
(XLSX)
S2 File. The newly designed SSR primer pairs and related SNPs within the intergenic regions.
https://doi.org/10.1371/journal.pone.0159268.s002
(XLSX)
S3 File. The details of the identified SSRs in date palm genome.
https://doi.org/10.1371/journal.pone.0159268.s003
(XLSX)
S4 File. The summarized results from the in silico PCR reactions.
https://doi.org/10.1371/journal.pone.0159268.s004
(XLSX)
S5 File. List of the sequences with blast results, GO terms and EC numbers.
https://doi.org/10.1371/journal.pone.0159268.s005
(XLSX)
S6 File. The position of the genes that encoded to enzymes within the genome and the SSR primers name for each gene.
https://doi.org/10.1371/journal.pone.0159268.s006
(XLSX)
S7 File. The list of the data for the pathways name, name of each enzyme and the position of their coding genes within the genome, in addition to the EC numbers for each enzyme.
https://doi.org/10.1371/journal.pone.0159268.s007
(XLSX)
Acknowledgments
The authors would like to dedicate this work to the soul of Prof. Dr. Sami Adawy who participated in this work but passed away during preparing the manuscript. Also they would like to thank Dr Mohamed Atia, Dr Shafik Darwish, Mr Alsamman Mahmoud, MS Wessam Raslan (Molecular Genetics and Genome Mapping Lab. (MGGM), Agricultural Genetic Engineering Research Institute (AGERI), ARC, Egypt for their valuable support during this study.
Author Contributions
Performed the experiments: MMM SSA SEDSEA EHAH. Analyzed the data: MMM SSA SEDSEA EHAH. Contributed reagents/materials/analysis tools: MMM SSA SEDSEA EHAH. Wrote the paper: MMM SSA SEDSEA EHAH.
References
- 1. Beal JM. Cytological studies in the genus Phoenix. Bot Gaz. 1937; 400–407.
- 2. Barrow SC. A monograph of Phoenix L.(Palmae: Coryphoideae). Kew Bull. 1998;53: 513–575.
- 3. Zohary D, Spiegel-Roy P. Beginnings of fruit growing in the Old World. Science. 1975;187: 319–327. pmid:17814259
- 4. Al-Farsi MA, Lee CY. Nutritional and functional properties of dates: a review. Crit Rev Food Sci Nutr. 2008;48: 877–887. pmid:18949591
- 5. Elhoumaizi MA, Saaidi M, Oihabi A, Cilas C. Phenotypic diversity of date-palm cultivars (Phoenix dactylifera L.) from Morocco. Genet Resour Crop Evol. 2002;49: 483–490.
- 6. Adawy SS, Hussein EHA, Ismail SEMES, El-Itriby HA. Genomic diversity in date palm (Phoenix dactylifera L.) as revealed by AFLPs in comparison to RAPDs and ISSRs. Arab J Biotech. 2005;8: 99–114.
- 7. Hamwieh A, Farah J, Moussally S, Al-Shamaa K, Almer K, Khierallah H, et al. Development of 1000 microsatellite markers across the date palm (Phoenix dactylifera L.) genome. Acta Hortic. 2010;882: 269–277.
- 8. Hussein EHA, Adawy SS, Ismail SEME, El-Itriby HA. Molecular characterization of some Egyptian date palm germplasm using RAPD and ISSR markers. Arab J Biotech. 2005;8: 83–97.
- 9. Wei W, Qi X, Wang L, Zhang Y, Hua W, Li D, et al. Characterization of the sesame (Sesamum indicum L.) global transcriptome using Illumina paired-end sequencing and development of EST-SSR markers. BMC Genomics. 2011;12: 451. pmid:21929789
- 10. Yang M, Zhang X, Liu G, Yin Y, Chen K, Yun Q, et al. The complete chloroplast genome sequence of date palm (Phoenix dactylifera L.). PLoS One. 2010;5: e12762. pmid:20856810
- 11. Fang Y, Wu H, Zhang T, Yang M, Yin Y, Pan L, et al. A complete sequence and transcriptomic analyses of date palm (Phoenix dactylifera L.) mitochondrial genome. PLoS One. 2012;7: e37164. pmid:22655034
- 12. Al-Dous EK, George B, Al-Mahmoud ME, Al-Jaber MY, Wang H, Salameh YM, et al. De novo genome sequencing and comparative genomics of date palm (Phoenix dactylifera). Nat Biotechnol. 2011;29: 521–527. pmid:21623354
- 13. Al-Mssallem IS, Hu S, Zhang X, Lin Q, Liu W, Tan J, et al. Genome sequence of the date palm Phoenix dactylifera L. Nat Commun. 2013;4: 2274. pmid:23917264
- 14. Zhao Y, Williams R, Prakash CS, He G. Identification and characterization of gene-based SSR markers in date palm (Phoenix dactylifera L.). BMC Plant Biol. 2013;12: 237.
- 15. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3-new capabilities and interfaces. Nucleic Acids Res. 2012;40: e115. pmid:22730293
- 16. Conesa A, Götz S, García-gómez JM, Terol J, Talón M, Robles M, et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21: 3674–3676. pmid:16081474
- 17. Zhang G, Pan L, Yin Y, Liu W, Huang D, Zhang T, et al. Large-scale collection and annotation of gene models for date palm (Phoenix dactylifera, L.). Plant Mol Biol. 2012;79: 521–536. pmid:22736259
- 18. Cardle L, Ramsay L, Milbourne D, Macaulay M, Marshall D, Waugh R. Computational and experimental characterization of physically clustered simple sequence repeats in plants. Genetics. 2000;156: 847–854. pmid:11014830
- 19. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, Mccouch S. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): Frequency, length variation, transposon associations, and genetic marker potential. genome Res. 2001;11: 1441–1452.
- 20. Morgante M, Hanafey M, Powell W. Microsatellites are preferentially associated with nonrepetitive DNA in plant genomes. Nat Genet. 2002;30: 194–200. pmid:11799393
- 21. Mun J, Kim D, Choi H, Gish J, Debellé F, Mudge J, et al. Distribution of microsatellites in the genome of Medicago truncatula: a resource of genetic markers that integrate genetic and physical maps. Genetics. 2006;172: 2541–2555. pmid:16489220
- 22. Jia X, Shi Y, Song Y, Wang G, Wang T, Li Y. Development of EST-SSR in foxtail millet (Setaria italica). Genet Resour Crop Evol. 2007;54: 233–236.
- 23. Rajendrakumar P, Biswal AK, Balachandran SM, Sundaram RM. In silico analysis of microsatellites in organellar genomes of major cereals for understanding their phylogenetic relationships. In Silico Biol. 2008;8: 87–104. pmid:18928198
- 24. Adawy SS, Mokhtar MM, Alsamman AM, Sakr MM. Development of annotated EST-SSR database in olive (Olea europaea). Int J Sci Res. 2015;4: 1063–1073.
- 25. Krzywinski M, Schein J, Birol II, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19: 1639–1645. pmid:19541911
- 26. Pinto LR, Oliveira KM, Ulian EC, Garcia AAF, DeSouza AP. Survey in the sugarcane expressed sequence tag database (SUCEST) for simple sequence repeats. Genome. 2004;47: 795–804. pmid:15499394
- 27. Kaur R, Sharma N, Raina R. Identification and functional annotation of expressed sequence tags based SSR markers of Stevia rebaudiana. Turkish J Agric For. 2015;39: 1–12.
- 28. Li Y, Korol AB, Fahima T, Nevo E. Microsatellites within genes: structure, function, and evolution. Mol Biol Evol. 2004;21: 991–1007. pmid:14963101
- 29. Kalia RK, Rai MK, Kalia S, Singh R, Dhawan AK. Microsatellite markers: An overview of the recent progress in plants. Euphytica. 2011;177: 309–334.
- 30. Cordeiro GM, Casu R, McIntyre CL, Manners JM, Henry RJ. Microsatellite markers from sugarcane (Saccharum spp.) ESTs cross transferable to erianthus and sorghum. Plant Sci. 2001;160: 1115–1123. pmid:11337068
- 31. Varshney RK, Thiel T, Stein N, Langridge P, Graner A. In silico analysis on frequency and distribution of microsatellites in ESTs of some cereal species. Cell Mol Biol Lett. 2002;7: 537–546. pmid:12378259
- 32. Thiel T, Michalek W, Varshney RK, Graner A. Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet. 2003;106: 411–422. pmid:12589540
- 33. Lawson MJ, Zhang L. Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomes. Genome Biol. 2006;7: R14. pmid:16507170
- 34. Elmeer K, Sarwath H, Malek J, Baum M, Hamwieh A. New microsatellite markers for assessment of genetic diversity in date palm (Phoenix dactylifera L.). 3 Biotech. 2011;1: 91–97. pmid:22582150
- 35. Billotte N, Marseillac N, Brottier P, Noyer JL, Jacquemoud-Collet JP, Moreau C, et al. Nuclear microsatellite markers for the date palm (Phoenix dactylifera L.): Characterization and utility across the genus Phoenix and in other palm genera. Mol Ecol Notes. 2004;4: 256–258.
- 36. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28: 27–30. pmid:10592173