De Novo Transcriptome Analysis of Dalbergia odorifera and Transferability of SSR Markers Developed from the Transcriptome

Dalbergia odorifera T. Chen (Fabaceae), indigenous to Hainan Island, is a precious rosewood (Hainan hualimu) in China. However, only limited genomic information is available which has resulted in a lack of molecular markers, limiting the development and utilization of the germplasm resources. In this study, we aim to enrich genomic information of D. odorifera, and develop a series of transferable simple sequence repeat (SSR) markers for Dalbergia species. Therefore, we performed transcriptome sequencing for D. odorifera by pooling leaf tissues from three trees. A dataset of 138,516,418 reads was identified and assembled into 115,292 unigenes. Moreover, 35,774 simple sequence repeats (SSRs) were identified as potential SSR markers. A set of 19 SSR markers was successfully transferred across species of Dalbergia odorifera T. Chen, Dalbergia tonkinensis Prain, and Dalbergia cochinchinensis Pierre ex Laness. In total, 112 alleles (3–13 alleles/locus) were presented among 60 Dalbergia trees, and polymorphic information content ranged from 0.38 to 0.75. The mean observed and mean expected heterozygosity was 0.34 and 0.40 in D. odorifera, 0.27 and 0.32 in D. tonkinensis, and 0.29 and 0.33 in D. cochinchinensis, respectively. The cluster analysis classified these 60 trees into three major groups according to the three Dalbergia species based on the genetic similarity coefficients, indicating these newly developed transferable markers can be used to explore the relationships among Dalbergia species and assist genetic research. All these unigenes and SSR markers will be useful for breeding programs in the future.


Introduction
The genus Dalbergia belongs to the Fabaceae which is a member of Angiosperms, and comprises about 100 species of trees, shrubs, and woody lianas [1].Many tree species in this genus are of great values due to their precious heartwood, which are used in the high-grade furniture and crafts markets.Their population sizes have been dramatically reduced due to overexploitation and illegal logging.Often, the most highly valued timber trees are the most threatened species in their native habitats [2].Examples include Dalbergia tonkinensis Prain, Dalbergia cochinchinensis Pierre ex Laness, and Dalbergia odorifera T. Chen.All the three Dalbergia tree species have been listed on the International Union for Conservation of Nature's (IUCN) red list of threatened species since 1998 [3][4][5].Dalbergia tonkinensis is a medium-sized floral species with a height of 5-13 m, located in Vietnam and Hainan Island of China [5].Dalbergia cochinchinensis is a hardwood tree species, distributed in the remaining forested areas of Thailand, Laos, Cambodia, and Vietnam [6].Dalbergia odorifera is a semi-deciduous perennial tree species, confined to a relatively narrow tropical geographic area of Hainan Island in China [7].The three endangered species play important roles in the biodiversity of their habitats, let alone their ecological and economic importance.Therefore, it is imperative to establish efficient strategies for conservation and sustainable use of the three Dalbergia tree species.The first essential part is a comprehensive knowledge of their genetic diversity, especially for D. odorifera where only limited numbers of individuals are found in parts of their original habitat [7].
Molecular markers have proven to be an important and effective tool for genetic diversity analysis and marker-assisted selection [8][9][10][11][12].However, there is a significant shortage of molecular markers for D. odorifera, resulting from the scarce genomic information with only 112 expressed sequence tag (EST) sequences recorded in the public GenBank sequence database (http://www.ncbi.nlm.nih.gov/genbank/).Currently, reported molecular markers are restricted to six random amplified polymorphic DNA (RAPD) markers [7] and 25 sequence-related amplified polymorphism (SRAP) markers [13].Yet there are no reports on microsatellite markers (SSR markers) or simple sequence repeats (SSRs) which are widely used in diverse areas, particularly in genetic diversity and structure studies [14][15][16][17].
Microsatellite markers are the ideal choice compared to other molecular markers for their advantages: abundance, high polymorphism, co-dominance, reproducibility, and transferability to related species [18].According to the origins of SSR markers, they are generally categorized into genomic SSRs of genomic sequences and EST-SSRs of transcribed RNA sequences.However, the discovery of genomic SSRs and development using traditional methods are laborious, time-consuming, and costly [18].The identification and development of EST-SSRs have been revolutionized along with the development of next-generation sequencing-based transcriptome sequencing (RNA-seq), especially for species with no reference genome, and extensive similar studies have been performed for many tree species [19][20][21].Additionally, transcriptomics is also a powerful tool for genome-wide analysis of RNA transcripts, allowing not only genomic data mining, but also facilitating genetic and molecular breeding approaches for endangered species [22,23].Therefore, we presented the first transcriptome of D. odorifera prepared using the Illumina Hi Seq sequencing platform.The main objectives of this study were to (1) provide high-quality transcriptome data and enrich the current knowledge of the genome background for D. odorifera, (2) identify a large number of SSR markers based on the D. odorifera transcriptome information obtained, (3) develop, validate, and transfer the identified SSR markers across Dalbergia species, and (4) evaluate the genetic relationships among 60 individuals from the three Dalbergia species (D. odorifera, D. tonkinensis, and D. cochinchinensis) using these validated SSR markers.And all these findings will provide useful information for breeding, hybridization, and conservation of Dalbergia germplasm.

Plant Materials and Preparation
Leaf samples were collected from three Dalbergia tree species including D. odorifera, D. tonkinensis, and D. cochinchinensis.The sampled D. odorifera individuals were located in the Hainan Island of China, the D. tonkinensis individuals were in Vietnam, and the D. cochinchinensis individuals were in Thailand.The latitudes and longitudes of the collection sites were recorded with a portable GPS (PokeNavi map21EX; Empex Instruments, Tokyo, Japan) (Table 1).In total, 60 Dalbergia individuals (20 samples from each Dalbergia species) were used to analyze the genetic diversity.Ten leaves were harvested from each individual and sealed in plastic bags with desiccant.Then total genomic DNA was extracted using the Hi-DNA-secure Plant Kit (Tiangen, Beijing, China) according to the  Three young leaf-samples from three D. odorifera trees were used for transcriptome sequencing on April 30, 2017.The three samples were DO27, DO98 and DO100, and collected from three different wild populations in Haikou city, Dongfang city, and Changjiang autonomous county of Hainan island (China), respectively.Total RNA was isolated from each sample using TRIzol reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer's instructions.The RNA quality and integrity were accurately confirmed using the NanoDrop 2000 (Thermo Scientific, Wilmington, DE, USA) and the Bioanalyzer 2100 system (Agilent Technologies, Columbia, MD, USA).Three cDNA libraries were built from each RNA sample following the Illumina protocol.The transcriptome sequencing was conducted at Beijing Novogene Biological Information Technology Co., Ltd., Beijing, China (http://www.novogene.com/).The generated 100 bp paired-end reads were filtered by strict principles [24,25], and the assembly of these high-quality reads were carried out by Trinity [26,27] with min_kmer_cov set to 2 and other parameters set to default.The longest transcripts of each gene were pooled as "unigenes", and unigene annotations were performed and analyzed by searching and comparing the public databases [25].Additionally, the transcript sequence data used here were submitted to the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/).The accession number is SRP175426 (SRR8398210 for DO27, SRR8398212 for DO98, and SRR8398211 for DO100).

SSR Identification and Marker Development
Micro Satellite (MISA; http://pgrc.ipk-gatersleben.de/misa)was used to detect, locate and identify SSRs.The minimum number of motifs used to select the SSRs was ten for mono-nucleotide repeats, and six for di-nucleotide motifs, five for tri-, tetra-, penta-, and hexa-nucleotide repeats.Primers were designed using Primer 3.0 [28] by default settings and selected according to the following criteria: primer lengths of 18-24 bases, GC content of 40-60%, annealing temperature of 56-62 • C, and the predicted PCR product sizes of 150-300 bp.

SSR Marker Validation, Transferability, and PCR Conditions
Subsequently, 192 SSR markers were randomly selected to test the transferability among the three Dalbergia species, and first amplified using three leaf samples (one each Dalbergia species) by PCR as described below.The PCR reactions were performed in 10 µL final volume, containing 5 µL water, 1 µL 10 × DNA polymerase buffer, 1.5 µL MgCl 2 (10 mM), 0.8 µL dNTPs (2.5 mM each), 0.3 µL of each primer at 5 µM, 0.1 µL Taq polymerase at 3 units/µL (TaqUBA), and 1 µL of genomic DNA (40-50 ng).Thermocyclers were programmed as follow: pre-denaturation at 94 • C for 4 min, followed by 35 cycles of 94 • C for 30 sec, appropriate annealing temperature for 30 sec, and 72 • C for 30 sec, and a final step at 72 • C for 5 min.The PCR products in D. tonkinensis and D. cochinchinensis, with clear, stable, and specific bands were considered as successful PCR amplifications.In D.odorifera, the clear, stable, and specific bands with approximately expected lengths of 100-350 bp (i.e., 50 nt longer or shorter than the predicted PCR product size) were considered as successful PCR amplifications.All the PCR reactions were repeated at least once, to confirm positive results and/or detect false negatives due to technical issues.Finally, a set of 19 SSR markers was randomly selected from the validated ones and 60 Dalbergia Forests 2019, 10, 98 4 of 16 leaf samples were used to confirm the polymorphism, and their diluted PCR products were separated by capillary electrophoresis.

Analysis of Marker Polymorphism and Dalbergia Genetic Relationship
Allele sizes of polymorphic SSR markers were genotyped using ABI3730 (Applied Biosystems, Foster, CA, USA) and Gene Mapper v4.0 (Applied Biosystems, Foster, CA, USA).POPGENE v1.3.2 software [29] was used to estimate the following statistics: the number of alleles (Na), observed (Ho) and expected heterozygosities (He), and the number of allele frequencies, respectively.The polymorphism information content (PIC) was calculated for each locus [30], so did the percentage of polymorphic loci (PPBs) for each species.A pair-wise similarity matrix was constructed using the SM similarity coefficient (SSC), based on which, an un-weighted pair group method with arithmetic mean (UPGMA) tree was constructed to reveal the relationship among these 60 Dalbergia trees using NTSYS-pc software (version 2.1) [31].Tree confidence was also analyzed using the NTSYS-pc software.

Transcriptome Assembly and Annotation
To select different genotypes and achieve a comprehensive overview of D. odorifera for SSR identification, leaves from three D. odorifera trees (DO27, DO98, and DO100) were used for Illumina draft reads and sequence assembly.In total, more than 143 million raw reads were obtained via the deep paired-end sequencing (Table 2).Followed rigorous quality control, 138,516,418 clean Illumina reads were obtained, of which, 40,896,098 was for DO27, 43,885,716 for DO98, and 53,734,604 for DO100, with Q20 ranging from 97.46% to 98.07% and GC contents from 44.72% to 45.09%.In total, 154,854 individual transcripts and 115,292 unigenes were obtained with a mean length of 881 bp (N50 = 1634 bp) and 676 bp (N50 = 1080 bp), respectively.The length of unigenes ranged from 201 to 12,117 bp with a total of 77,979,362 nucleotides.Among these unigenes, 63.95% (73,730) had lengths ranging from 201 to 500 bp, 18.76% (21,634) from 501 to 1000 bp, 10.48% (12,091) from 1001 to 2000 bp, and 7837 (6.80%) with lengths longer than 2000 bp (Figure 1).
After the functional annotation, 56,898 of all the 115,292 unigenes were successfully annotated in at least one of the seven databases, and 7508 were annotated in all the databases (Table S1).A total of 43,597 unigenes showed significant BLAST (basic local alignment search tool) hits in the Nr database and 99.86% (43,537) showed significant similarity to known proteins.Additionally, 36,710 (84.20%) presented similarity to known proteins in the Swiss-Prot database.
Based on Nr annotations, 33,219 unigenes assigned to gene ontology (GO) terms clustered into biological process, cellular component and molecular function categories, including 56 sub-categories (Figure 2a).In the biological process category, cellular process (18,131, 54.58%), metabolic process (17,343, 52.21%), and single-organism process (13,536, 40.76%) were the most abundant of the 24 sub-groups.Within the cellular component category, the cell (28.48%) and cell part (28.47%) components, both with unigenes more than 9400, were the most abundant terms of all the 22 sub-groups, whereas only a few unigenes were assigned to synapse part (4), synapse (4), symplast (2), extracellular matrix component (1) and nucleoid (1).Of the last 10 sub-groups in the molecular function category, the binding and catalytic activity were prominently represented, assigned with 17,712 and 14,669 unigenes, respectively, whereas the metallochaperone activity sub-group contained only eight unigenes.After the functional annotation, 56,898 of all the 115,292 unigenes were successfully annotated in at least one of the seven databases, and 7,508 were annotated in all the databases (Table S1).A total of 43,597 unigenes showed significant BLAST (basic local alignment search tool) hits in the Nr database and 99.86% (43,537) showed significant similarity to known proteins.Additionally, 36,710 (84.20%) presented similarity to known proteins in the Swiss-Prot database.
Based on Nr annotations, 33,219 unigenes assigned to gene ontology (GO) terms clustered into biological process, cellular component and molecular function categories, including 56 sub-categories (Figure 2a).In the biological process category, cellular process (18,131, 54.58%), metabolic process (17,343, 52.21%), and single-organism process (13,536, 40.76%) were the most abundant of the 24 subgroups.Within the cellular component category, the cell (28.48%) and cell part (28.47%) components, both with unigenes more than 9400, were the most abundant terms of all the 22 sub-groups, whereas only a few unigenes were assigned to synapse part (4), synapse (4), symplast (2), extracellular matrix component (1) and nucleoid (1).Of the last 10 sub-groups in the molecular function category, the binding and catalytic activity were prominently represented, assigned with 17,712 and 14,669 unigenes, respectively, whereas the metallochaperone activity sub-group contained only eight unigenes.
To characterize the active biological pathways in D. odorifera, we performed a kyoto encyclopedia of genes and genomes analysis (KEGG).The classification showed 16,571 unigenes were annotated to 129 pathways, and categorized into 5 clades (cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems) and According to the KOG (eukaryotic ortholog groups) functional classification, 17,860 assigned unigenes were classified into 26 functional groups (Figure 2b).The largest group was general function prediction with 2987 unigenes (16.72%), followed by posttranslational modification, protein turnover, chaperones (2352, 13.17%), and signal transduction mechanisms (2018, 11.30%).
To characterize the active biological pathways in D. odorifera, we performed a kyoto encyclopedia of genes and genomes analysis (KEGG).The classification showed 16,571 unigenes were annotated to 129 pathways, and categorized into 5 clades (cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems) and 19 sub-groups (Figure 2c).Of the five clades, cellular processes contained only one sub-group of transport and catabolism (877 unigenes), so did organismal systems with the sub-group of environmental adaptation (680 unigenes).Among the 19 sub-groups, carbohydrate metabolism (1775) and translation (1670) were the most represented pathways.19 sub-groups (Figure 2c).Of the five clades, cellular processes contained only one sub-group of transport and catabolism (877 unigenes), so did organismal systems with the sub-group of environmental adaptation (680 unigenes).Among the 19 sub-groups, carbohydrate metabolism (1775) and translation (1670) were the most represented pathways.

Frequency and Distribution of SSRs in D. odorifera Transcriptome
From the 115,292 unigenes (77,979,362 bp), 35,774 potential SSRs were identified and distributed in 26,880 unigenes.Of which, 24.7% (6629) contained more than one SSRs (Table S2).The frequency of SSRs in the D. odorifera transcriptome was 31.0%with a distribution density of 469.72 SSRs per Mb.

Development and Transferability of Polymorphic SSR Markers
A set of 192 SSRs was randomly selected, and their primers were designed to test specificity of amplification for three leaf samples (one sample from each Dalbergia species).Of these designed primers, 88 (45.8%) amplified successfully in D. odorifera, followed by 66 (34.4%) in D. tonkinensis, and 83 (43.2%) in D. cochinchinensis.Moreover, 63 (32.8%) produced clear amplicons across the three Dalbergia species.Details on these SSRs, including ID of the template DNA sequence carrying the SSR, SSR type, repeat motif, position in template sequence, primer sequence, annealing temperature, and expected amplicon length (for developing alternative primers if desired) are presented in Table S3.
Ultimately, 19 of these 66 successfully amplified SSR markers were randomly selected for polymorphic validation and all these selected markers showed polymorphism among the 60 Dalbergia individuals (Table 4).In total, 112 alleles represented across these individuals.The allele number of per locus ranged from 3 to 13 with an average of 5.89.Observed (Ho) and expected heterozygosity (He) was in a range of 0.05-0.65 and 0.44-0.79,respectively.The polymorphic information content (PIC) varied from 0.38 (S01) to 0.75 (S26) with the mean 0.59.Moreover, 78.9% (15) of these markers were highly polymorphic with the PIC values higher than 0.5.

Transcriptome Sequencing, De Novo Assembly, and Annotation for D. odorifera
Currently, scarce genome information has been published for a few Dalbergia species [32][33][34], and to date, there is no de novo assembly data for the D. odorifera yet.Therefore, high-quality transcriptome resources, particularly those covering full-length sequences and have been filtered for redundancy, remain essential for D. odorifera.In this research, we carried out the transcriptome for three leaf-tissues from non-cloned individuals and generated approximately 14 million clean reads assembled into 115,292 unigenes, which are valuable resources for functional genomic studies of Dalbergia species prior to sequencing of the D. odorifera genome.The N50 and average length of generated unigenes were 1080 bp and 676 bp, respectively.These results are comparable to the results from recently published tree and legume transcriptome (leaf tissue) studies, such as Camellia sinensis (L.) Kuntze (867 bp, 601 bp) [35], Morus atropurpurea Roxb.(1219 bp, 793 bp) [36], Millettia pinnata Forst.(699 bp, 534 bp) [37], Quercus kerrii Craib (1166 bp, 720 bp), and Quercus austrocochinchinensis   Currently, scarce genome information has been published for a few Dalbergia species [32][33][34], and to date, there is no de novo assembly data for the D. odorifera yet.Therefore, high-quality transcriptome resources, particularly those covering full-length sequences and have been filtered for redundancy, remain essential for D. odorifera.In this research, we carried out the transcriptome for three leaf-tissues from non-cloned individuals and generated approximately 14 million clean reads assembled into 115,292 unigenes, which are valuable resources for functional genomic studies of Dalbergia species prior to sequencing of the D. odorifera genome.The N50 and average length of generated unigenes were 1080 bp and 676 bp, respectively.These results are comparable to the results from recently published tree and legume transcriptome (leaf tissue) studies, such as Camellia sinensis (L.) Kuntze (867 bp, 601 bp) [35], Morus atropurpurea Roxb.(1219 bp, 793 bp) [36], Millettia pinnata Forst.(699 bp, 534 bp) [37], Quercus kerrii Craib (1166 bp, 720 bp), and Quercus austrocochinchinensis Hickel & A.Camus (1335 bp, 782 bp) [38].
For gene annotation, it was not surprising that more than 60% annotated unigenes was top-hit to legume species (Figure S1), particularly Glycine max (L.) Merr.(7679) [39] and Glycine soja Sieb.et Zucc.(7428) [40], the species with the earliest complete genome sequences.This may be because they all belong to the family Fabaceae and share a high level of genomes sequence similarity.Similar observations have been reported on other transcriptome studies [41,42].It is thought that KEGG provides a basic platform for systematic analysis of gene function in terms of gene product networks [43].Our KEGG analysis revealed 16,571 unigenes involved in 129 pathways.In addition, 33,219 unigenes were assigned to GO assignments, and 17,860 were functional classified according to the KOG analysis.All these annotations and classifications are useful resources for investigating biological function-specific unigenes in D. odorifera.

SSR Prediction, Validation, and Application
Molecular markers have been widely and effectively applied in plants for genetic diversity and association analysis [8][9][10][11][12].Prior to this study, however, there were only six RAPD (random amplified polymorphic DNA) markers and 25 SRAP (sequence-related amplified polymorphism) markers for the genotyping of D. odorifera germplasm [7,13], and no co-dominant SSR markers have been specifically developed for this endangered tree species.Therefore, the development of a known polymorphic SSR marker database for D. odorifera is of great use for diversity studies and breeding programs, as well as being transferable to closely Dalbergia species.In the present study, we identified 35,774 putative SSRs from the transcriptome dataset, which is of great differences in numbers, dominant repeats, and motif types reported from other legume species using Illumina sequencing [41,42,44].This may be attributable to inconsistencies in genome structure or composition, dataset size, search method, or criteria.
The effectiveness and success of SSRs rely considerably on the quality of the markers, the accuracy of the genotyping data, and the plant materials used [45].Therefore, the next step in building a working marker set for genetic improvement efforts was validation of the identified SSRs.Of these 192 primer pairs, 32.8% (63) yielded clear bands across the 60 Dalbergia trees.The success rate is comparable for SSR markers developed from the transcriptome of Robinia (25%) [46], Allium (31%) [47], and Osmanthus (35%) [14,48].Furthermore, all of these 19 markers exhibited polymorphism among these 60 Dalbergia trees, with a polymorphic information content (PIC) range of 0.38-0.75,which were comparable to, or substantially higher than the PIC values of cross-species EST-SSR markers reported for Melilotus (0.10-0.87) [48], Robinia (0.03-0.76) [46], and Casuarina (0.26-0.62) [19].
Highly polymorphic and stable SSR markers were important resources for genetic relationship analysis.In the present research, 78.9% (15) of these markers were highly polymorphic, with PIC values higher than 0.50, indicating those alleles were found in more than 50% of these germplasms.Deciding whether an SSR marker is useful depends upon the scientific issue since PIC values may be influenced by many factors, such as sampling schemes, the number of SSRs, and types of SSR motif repeats [45].Highly polymorphic SSR markers are useful for genetic diversity but using only the most polymorphic markers would bias the overall genetic diversity [15], especially in conservation studies [49].Therefore, the entire newly developed SSR markers were used to evaluate the genetic relationships of these 60 sampled Dalbergia trees.Based on genetic similarity coefficients (SSM, r = 0.999), three major clusters according to the three Dalbergia species were revealed (Figure 3), indicating these transferable SSR markers are advantageous to explore the relationships among Dalbergia species and could be available for assisting genetic research and breeding in the future.

Conclusions
This study represents the first attempt to obtain the transcriptome information and mine SSR markers of D. odorifera using RNA-seq, providing a large and well-assembled transcript data set.These abundant resources are an initial step towards understanding the genetic backgrounds of this endangered species and may serve as a basic reference for other Dalbergia species.Moreover, a novel set of SSR markers was successfully developed and transferred to other Dalbergia species.These results are useful for evaluating the genetic diversity, population genetic structure, and marker-assisted breeding applications in the future.

Forests
instructions.After determining the quality and concentration using a NanoDrop 2000 (Thermo Scientific, Wilmington, DE, USA), the DNA was finally stored at −20 • C for use.

Figure 1 .
Figure 1.Unigenes length distribution in D.odorifera transcriptome.Horizontal and vertical axes show the size and number of unigenes, respectively.

Figure 2
Figure 2 Functional classifications of D. odorifera unigenes.(a) Gene ontology (GO) classification of assembled unigenes.The left y-axis indicates the percentage of a specific category of genes in each main category.The right y-axis represents the amount of unigenes in a category; (b) Eukaryotic ortholog groups (KOG) classification of assembled unigenes.The y-axis indicates the amount of unigenes in a specific functional cluster.The x-axis shows function class; (c) Kyoto encyclopedia of genes and genomes (KEGG) classification of assembled unigenes.The y-axis indicates the KEGG pathway, and the x-axis is the ratio of the number of unigenes.

Figure 2 .
Figure 2. Functional classifications of D. odorifera unigenes.(a) Gene ontology (GO) classification of assembled unigenes.The left y-axis indicates the percentage of a specific category of genes in each main category.The right y-axis represents the amount of unigenes in a category; (b) Eukaryotic ortholog groups (KOG) classification of assembled unigenes.The y-axis indicates the amount of unigenes in a specific functional cluster.The x-axis shows function class; (c) Kyoto encyclopedia of genes and genomes (KEGG) classification of assembled unigenes.The y-axis indicates the KEGG pathway, and the x-axis is the ratio of the number of unigenes.

Figure 3 .
Figure 3.The UPGMA (un-weighted pair group method with arithmetic mean) cluster diagram of 60 Dalbergia trees based on the 19 newly developed SSR markers.

Figure 3 .
Figure 3.The UPGMA (un-weighted pair group method with arithmetic mean) cluster diagram of 60 Dalbergia trees based on the 19 newly developed SSR markers.
Transcriptome Sequencing, De Novo Assembly, and Annotation for D. odorifera

Table 1 .
Geographical location of the three Dalbergia species.

Table 2 .
Summary of D. odorifera base quality.

Table 3 .
Details of the different SSRs (simple sequence repeats) distribute in D. odorifera.