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Article

The Identification of Several Dipterocarpaceae and Fagaceae Trees by Barcode DNA Coupled with High-Resolution Melting Analysis

by
Maslin Osathanunkul
1,2,* and
Panagiotis Madesis
3,4
1
Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
2
Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
3
Laboratory of Molecular Biology of Plants, Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, 38221 Volos, Greece
4
Institute of Applied Biosciences, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Forests 2021, 12(11), 1466; https://doi.org/10.3390/f12111466
Submission received: 2 September 2021 / Revised: 10 October 2021 / Accepted: 12 October 2021 / Published: 27 October 2021
(This article belongs to the Section Genetics and Molecular Biology)
Browse Figures
Versions Notes

Abstract

:
The loss of forests is a major environmental, social, and economic problem. The disappearance has been occurring to an extreme degree in many parts of Southeast Asia, including Thailand. Logging and clearing of forests for agriculture, cash crops, and food production has destroyed much of the tropical forests in Thailand. Floristic inventory could provide essential information for forest conservation but species identification as a part of inventory creating could be challenging in some cases. Barcode DNA coupled with High Resolution Melting analysis (Bar-HRM) was used here in aiding species identification of plant in Dipterocarpaceae (Dipterocarpus alatus, D. costatu, D. intricatus, D. obtusifolius, Hopea ferrea, H. odorata, Shorea guiso, S. obtuse, S. roxburghii, and S. siamensis) and Fagaceae (Castanopsis echinocarpa, C. inermis, Lithocarpus wallichianus, Quercus aliena and Q. oidocarpa) families. Two main experiments were conducted including: (1) a comparing method for primer design and (2) testing the robustness of the Bar-HRM by trying to identify tree samples that did not have sequences in the GenBank. In experiment 1, the manual design primer pair was found to be the best fit for the work. Of key importance is finding the primers which give the most nucleotide variations within the generated amplicon; this is a parameter that cannot be set in any web-based tools. Next, in experiment 2, Bar-HRM using primers of ITS1 and ITS2 regions were able to discriminate all 10 tested tree species without any problem, even when there were no sequences of the samples to be analysed before performing the HRM. Here, Bar-HRM poses potential to be a game-changer in tropical forest conservation, as it will be useful for species identification.

1. Introduction

Tropical forests host the most diverse plant communities on earth and play an important role in the global carbon cycle. Almost 15% of tropical forests are in Southeast Asia, which continues to be threatened or destroyed. According to recent research, Southeast Asia lost about 1.6 million hectares of forest per year, and it is estimated that such deforestation could lead to almost 50% of Southeast Asia’s biodiversity loss by 2100 [1]. The effect of losing tropical forests is more devastating than previously thought, as also shown in a recent report [2]. Loss of forests is a major environmental, social, and economic problem. Thailand, Indonesia, and Malaysia are ranked top countries in Southeast Asia with high loss of forests.
Dipterocarpaceae and Fagaceae are two ecologically important plant families in tropical forests, often forming the backbone of the forest ecosystem. Both are main plant families for forest regeneration, conservation programmes and sustainable forest management [3,4,5]. Identification of plant species is also found to be important for conservation and management planning [6]. However, species identification of the plants in these families is still challenging. It is not only time-consuming but also requires experts. Most importantly, fruit and floral characteristics are needed to be identified [7], so it is almost impossible to identify them when they are seedlings or in a vegetative phase.
As stated previously, molecular technologies have recently paved the way in species identification. DNA barcoding is one of the biggest drivers to make identification easier and more reliable [8]. DNA barcoding involves sequencing a short standard region of DNA. Basically, DNA barcoding identification relies on genetic variability to establish the limits between species with lower levels of genetic divergence among individuals of the same species [9,10,11]. In animals, mitochondrial cytochrome c oxidase subunit I (COI) gene has been proposed as the standard barcode [8,12]. However, mutation rate of the COI is too low in plants so it cannot be used for plant species identification [13,14,15,16]. Although many researchers have made great contributions to the search for a core DNA barcode for plant identification, a consensus has not been reached [17,18,19,20]. Nevertheless, the CBOL Plant Working Group has suggested the use of two DNA regions (rbcL and matK) as a universal plant barcode [15]. Evaluation for candidate DNA barcodes in plants identified have also found other potential loci. The P6 loop of the trnL (UAA) intron is a good candidate as a supplemental barcoding locus as it can be routinely amplified across land plants with adequate species-level resolution, and can be used for highly degraded samples (e.g., [21,22]). Also, another commonly sequenced supplementary locus in plant identification is the nuclear internal transcribed spacer (ITS) (e.g., [23,24,25]).
Recently, DNA barcoding was used in combination with high-resolution melting analysis (called Bar-HRM). Bar-HRM analysis starts with PCR amplification of the selected barcode region in the presence of fluorescent dye. Amplification is then followed by a melting step under high-resolution conditions, which require a real-time PCR detection system with HRM analysis software. Bar-HRM requires no manual post-PCR processing as it is performed in a closed-tube system [26]. Species are discriminated according to base composition, length, and GC content [27,28]. Bar-HRM has been applied to authenticate commercial food, herbal, and agricultural products, e.g., [29,30,31,32,33]. Here, we show two different experiments for species identification of Dipterocarpaceae and Fagaceae using Bar-HRM with different DNA barcodes. In experiment 1, in order to have an effective Bar-HRM for species discrimination, two main approaches of primer design were tested. In experiment 2, Bar-HRM was evaluated to identify the tree samples. The developed method will be useful for forest health monitoring of the two ecologically important plant families.

2. Materials and Methods

2.1. Data Mining for Primer Design

To design primers for identification of Fagaceae plants based on the Bar-HRM technique, a dataset of a selected DNA region (Ribulose bisphosphate carboxylase large chain; rbcL) was constructed to conduct the sequence profile analysis. The rbcL sequences were retrieved from GenBank which included sequences from the entire family of Fagaceae (in total of 290 sequences from 92 species) (Supplementary Data A). Sequences obtained from public databases, including GenBank, are of low quality with no known associated herbarium vouchers. For this reason, all of the sequences were subjected to critical evaluation and any low-quality sequences were removed. Criteria used to filter the sequences were: (1) sequences are not ‘unverified’ without a species name, (2) contain <3% ambiguous base ‘N’, (3) maximum of 3 samples (sequences) are included from a species. After processing, multiple alignments were made from the selected sequences using MEGA10 and sequence length (bp), conserved sites (%), variable sites (%), and GC content (%) of each dataset were recorded. Primers were designed manually by researcher and web-based programmes, including Primer3, Primer-BLAST, GenScript, and PrimerQuest IDT. All candidate primers were then compared and the best one selected for identification of the tested samples.

2.2. Plants Samples and DNA Extraction

Plant tissues of five Fagaceae species and ten Dipterocaparceae species used in experiment 1 and 2, respectively (Table 1), were collected from a forest in Doi Suthep-Pui and provided by the Queen Sirikit Botanical Garden. The plant tissues were ground with liquid nitrogen. DNA from all samples was extracted using the Nucleospin Plant® II kit (Macherey-Nagel, Düren, Germany) according to the manufacturer’s instructions. DNA concentrations were adjusted to a final concentration of 20 ng/μL. The DNA was stored at −20 °C for further use.

2.3. In Silico Analysis of the Most Suitable Region for HRM Analysis

To address the most suitable DNA region for identification of Dipterocarpaceae and Fagaceae species based on Bar-HRM technique, a dataset was constructed to conduct the sequence profile analysis. Sequence profile of maturase K (matK), Ribulose bisphosphate carboxylase large chain (rbcL), trnL (UAA) intron (trnL), and Internal transcribed spacer (ITS) were retrieved from GenBank, which included sequences from the Dipterocarpaceae. There are in total 469 sequences from 188 species of matK, 425 sequences from 152 species of rbcL, 1112 sequences from 246 species of trnL, and 161 sequences from 75 species of ITS (Supplementary Data A). All the retrieved sequences were processed, as described in the data mining for primer design section.

2.4. Simulation High-Resolution Melting (HRM)

uMelt Quartz was used for simulation of high-resolution melting assays (https://dna-utah.org/umelt/quartz/ (accessed on 27 February 2021). For experiment 1, sequences of five Fagaceae species (Table 1) were trimmed to leave only the region that would be generated by each pair of the candidate primers (one pair form a manual design and four pairs from each web-based tool). The amplicons were then put into the uMelt Quartz to get the melting profiles of each species. The primer pair that gives the best result in uMelt Quartz was then selected for HRM analysis. All melting profiles are shown in Supplementary Data B.

2.5. High-Resolution Melting (HRM) Analysis

In order to determine the characteristic melting profile for each sample that could be used to distinguish the two different species, DNA amplification using real-time PCR was performed using the Rotor-Gene Q 5plex HRM system (Qiagen, Hilden, Germany). The reaction mixture for the real-time PCR and HRM analysis consisted of a total volume of 10 µL, containing 4 µL of Evagreen HRM Master Mix, 0.2 µL of 10 mM forward primer, 0.2 µL of 10 mM reverse primer, 1 µL of 20 ng DNA, and 3.6 µL of ddH2O. The nucleotides of forward and reverse primers were detailed in Table 2. Fluorescence dye was used to monitor both the accumulation of the amplified product and the high-resolution melting process in order to derive the Tm value during PCR. The reaction conditions were as follows: an initial denaturing step at 95 °C for 5 min followed by 40 cycles of 95 °C for 30 s, 57 °C for 30 s and 72 °C for 20 s. Melting curves were generated after the last extension step. The temperature for the HRM analysis was increased from 60 to 95 °C at 0.1 °C/s. The negative derivative of the fluorescence (F) over temperature (T) (dF/dT) curve displays the Tm, and the normalised raw curve depicts the decreasing fluorescence vs. increasing temperature. To generate normalised melting curves and differential melting curves, pre- and post-melt normalisation regions were set to define the temperature boundaries of the normalised and difference plots.

3. Results

3.1. Experiment 1: Comparing Primer Design Methods

Sequences of plants in Fagaceae (Castanopsis, Lithocarpus and Quercus) retrieved from GenBank were used to design primers. Two strategies (manual design and programme-based design) were performed to find the best primer pair for identification of the tested samples. Basically, primers should have the following properties: (i) length of 18–24 bases, (ii) 40–60% GC content, (iii) melting temperature (Tm) of 50–60 °C, (iv) primer pairs (forward and reverse) should not have complementary regions. However, for the primers for species identification/discrimination here, variable nucleotides within the product generated from the primer pair (amplicon) are important.
For manual design, a region with high variable sites was targeted. First, rcbL sequence (1428 bp) was divided into seven segments (A–G), 200 bp each, except the last segment, which is 228 bp in length (Figure 1). The retrieved rbcL sequences from GenBank were analysed to find the most suitable rbcL segment for HRM analysis. The variable sites within the segment were found to be as follows: segment D > segment C > segment E > segment F > segment G > segment B > segment A. There are two primer pairs obtained using the manual design, the sequence of which was located within segment B and C (Table 3). Primer M1 contains more variable sites (6.0%) than M2 (2.0%); thus, the latter was chosen as a candidate of a manual design primer and taken for further analysis.
For the programme-based primer design, a consensus sequence of rbcL sequences was built from all the retrieved sequences. Four web-based primer design tools, namely GenScript, Primer3, Primer-BLAST, and PrimerQuest IDT, were selected. All the selected programmes are similar in term of ease of use, quantity of time taken to get the results, and all are free to use. Different numbers of primers were obtained from each tool: three primer pairs were generated from GenScript, four from Primer3, ten from Primer-BLAST, and five from PrimerQuest IDT. All generated pairs of primers were then analysed. Most of the primers designed by web-based tools were located in segment B and C. Variable nucleotides within the amplicon of each primer pairs were calculated and shown in Table 3. The primer pair of each programme with the highest variable nucleotides within amplicon (G3, P3, PB4, and PQ1) were chosen for further analysis (Table 4).
The length, GC content, and annealing temperature (Tm) of the five chosen primer pairs were recorded. All five pairs were also evaluated in simulation HRM (uMelt Quartz) to find the best one that can discriminate five selected Fagaceae sequences (Table 1). Nucleotide variable and GC content of analysed amplicons are important factors to be considered for HRM analysis, so the two values were calculated and focused. Amplicons generated from a manual designed primer pair (M1) was found to have the highest nucleotide variable (6.0%) in comparison to all five chosen pairs. Among four programme-based designed primers, G3 contain the highest %variation site, which is 2.3%, followed by P3 (2.0%), PB1 (0.6%), and PQ1 (0.5%). GC content of amplicon generated from primer M1 had the highest GC content (46.9). Furthermore, the GC content of others is still at an acceptably value (Table 4). Simulation HRM (uMelt Quartz) was carried out, revealing that none of the designed primers were able to discriminate between all five tested species. None of the designed primers can discriminate between all the tested species from each other based on their melting profile obtained from Simulation HRM. M1 and P3 enable separation of the five tested species into three groups; PB4 and PQ1 can only divide the five species into two groups and G3 cannot separate any species so they are showed in one group (Supplementary Data B).
As the M1 seems to be the best candidate for the task here (highest variable sites), it was then used in HRM analysis in the lab. The DNA of five Fagaceae species (same as used in the simulation HRM) were used as a template for in vitro HRM. The melting profiles of all amplicons are illustrated in Figure 2. The M1 primer enabled division of the five tested species into four groups. Only Castanopsis inermis and Quercus oidocarpa cannot be discriminated from each other, which is consistent with the simulation HRM result.

3.2. Experiment 2: Using Bar-HRM to Identify Unknown Species (Dipterocarpaceae)

In a similar situation, species identification of both Fagaceae and Dipterocarpaceae plants is difficult. Several molecular approaches have been introduced in aiding the identification. One of the most promising molecular techniques is DNA barcoding. However, the main limitation of the barcoding is that sequences are needed. Results from our search of four main DNA barcode regions (matK, rbcL, trnL, and ITS) in GenBank found that in a total of 695 species in Dipterocapaceae family, there are 205, 165, 268, and 77 species having matK, rbcL, trnL, and ITS sequences deposited in the database, respectively (Supplementary Data A). All of the selected barcodes of Dipterocarpaceae species having nucleotide sequences deposited in the GenBank less than 50% of the total species number. Bar-HRM is the molecular method that could eliminate the limitation of DNA barcoding in which sequences of the studies plants are not required. According to in silico test of all five DNA regions (matK, rbcL, trnL, ITS1, and ITS2), only three DNA regions (rbcL, ITS1, and ITS2) contain variable sites that can be used to distinguish the tested plants species (10 Dipterocarpaceae species) here. Thus, three primer pairs from the three DNA regions were used. First, the melting profiles of all amplicons generated from primer M1 (rbcL) are illustrated in Figure 3A–D. The ten selected species were divided into seven groups in which D. obtusifolius, D. tuberculatus and S. obtuse are in one group, D. intricatus and S. siamensis are also grouped together; the rest are well separated (Figure 3A). When looking closely at each genus (Dipterocarpus, Hopea, and Shorea), the M1 primers can only discriminate four Dipterocarpus into three group (Figure 3B), whereas all Hopea and Shorea species were all well separated (Figure 3C,D).
The ITS1 and ITS2 primer sets were also used for the amplification of DNA fragments from all 10 samples, and the amplicons were analysed using HRM to define melting profile of each species and found that none of the melting curves of the tested species were shared or close to one another (Figure 4A–D and Figure 5A–D). Thus, both ITS1 and ITS2 primers were equally good to discriminate the 10 tested species here.

4. Conclusions

From the results showing here, manually designed primers were most suitable for Bar-HRM technique, as there were several factors affecting HRM analysis. One of the most important factors is variable nucleotides within the product generated from the primer pair (amplicon). Choice of DNA barcodes also has an impact on the success of Bar-HRM, as can be seen when comparing primer pairs generated from different DNA regions.

5. Discussion

5.1. Experiment 1: Comparing Primer Design Methods

Fagaceae is a big plant family comprising of nearly a thousand species, but less than 30% (260 species) of them have rbcL sequences deposited in GenBank. As mentioned earlier, morphological identification of the Fagaceae species is known to be difficult; thus, molecular data is necessary. Lack of sequences in public domain can hinder species identification via DNA barcoding, which relies heavily on the sequences. High-resolution melting (HRM) analysis has been proven to be alternative molecular tool in several cases, which sequence is not essential. Two main factors that contribute to the success of HRM analysis are GC content (40–60%) and variation nucleotide (more is better) within amplicon [34]. Therefore, primers generate amplicon with high variable sites and GC content within the range are desirable. Several programmes for primer design are now available; some with license some are free of charge. Many of the web-based primer design tools permit the user to set parameters as per their preference. Generally, criteria for primer design are all similar, such as length of primers (18–24 bp), melting temperature (56–62 °C), hairpin (try to avoid), and terminal nucleotide at 3′ (not complementary between forward and reverse primers) [35]. In the case of HRM for species discrimination, primers need to generate amplicon around 150–300 bp in length because a larger DNA size could reduce the distinction between variants [27,28]. Also, the generated amplicons should contain as many as nucleotide variations as possible and this is not a parameter that can be normally set in web-based primer design tools. In contrast, the manual design allows researchers to look for the most variable region and then design primers in that region. However, manual design takes longer time than web-based primer design tools.

5.2. Experiment 2: Using Bar-HRM to Identify Unknown Species (Dipterocarpaceae)

HRM coupled with DNA barcode (called Bar-HRM) has proved promising in species identification. However, none of the previous applications of Bar-HRM were used with Dipterocarpaceae before. Also, although there are several choices of DNA regions, rbcL and ITS were chosen in this study, which is based on several works on plant species identification, e.g., [15,17,18,19,20,23,24,25]. Several molecular works on Dipterocarpaceae species identification have been done, but there is still insufficient sequence data to cover a greater range of Dipterocarpaceae species [36,37,38,39] Interestingly, as mentioned earlier, not even 50% of Dipterocarpaceae species have their DNA sequences deposited in GenBank, but that is not a problem when using Bar-HRM, as proven here.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/f12111466/s1, Supplementary Data A. Dipterocarpaceae sequences of matK, rbcL, trnL and ITS were retrieved from GenBank (NCBI) with accession number. Supplementary Data B. Melting profiles obtaining from the uMelt Quartz program. The sequences of five Fagaceae species were trimmed to leave only the region that would generated by each pair of the candidate primers (M3, G3, P3, PB4 and PQ1). The amplicons were then put into the program to get the melting profiles of each species.

Author Contributions

Conceptualization, M.O. and P.M.; methodology, M.O. and P.M.; formal analysis, M.O.; data curation, M.O.; writing—original draft preparation, M.O.; writing—review and editing, M.O.; funding acquisition, M.O. and P.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Chiang Mai University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

DNA sequences with GenBank accessions are all included in Supplementary Data.

Acknowledgments

We thank the Botanical Garden Organisation, Ministry of Natural Resources and Environment, Thailand, for providing and identifying the samples. We are thankful to our colleagues and students for their help and Lauren R. Clark for English editing.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Seven analysed segments of the rbcL sequence for manual design of a primer.
Figure 1. Seven analysed segments of the rbcL sequence for manual design of a primer.
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Figure 2. Melting profiles of five Fagaceae species generated from HRM analysis using M1 primers.
Figure 2. Melting profiles of five Fagaceae species generated from HRM analysis using M1 primers.
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Figure 3. Melting profiles of 10 Dipterocarpaceae species generating from HRM analysis using M1 primers (rbcL). Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
Figure 3. Melting profiles of 10 Dipterocarpaceae species generating from HRM analysis using M1 primers (rbcL). Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
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Figure 4. Melting profiles of ten Dipterocarpaceae species generated from HRM analysis using ITS1 primers. Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
Figure 4. Melting profiles of ten Dipterocarpaceae species generated from HRM analysis using ITS1 primers. Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
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Figure 5. Melting profiles of 10 Dipterocarpaceae species generated from HRM analysis using ITS2 primers. Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
Figure 5. Melting profiles of 10 Dipterocarpaceae species generated from HRM analysis using ITS2 primers. Normalised curves of (A) all 10 species; (B) species from genus Dipterocarpus; (C) species from genus Shorea; (D) species from genus Hopea.
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Table
Table 1. Details of the plant samples.
Table 1. Details of the plant samples.
Used inSpeciesVoucher Number
Experiment 1Castanopsis echinocarpa A.DC25819
Castanopsis inermis (Lindl. ex Wall.) Benth. & Hook.f.7316
Lithocarpus wallichianus (Lindl. ex Hance) Rehder25181
Quercus aliena Blume34038
Quercus oidocarpa DC.90636
Experiment 2Dipterocarpus alatus Roxb. ex G.Don46253
Dipterocarpus intricatus Dyer10895
Dipterocarpus costatus C.F. Gaertn.10567
Dipterocarpus obtusifolius Teijsm. ex Miq.8889
Hopea odorata Roxb.28852
Hopea ferrea Laness.27258
Shorea roxburghii G.Don32238
Shorea obtusa Wall. ex Blume28848
Shorea siamensis Miq.33182
Shorea guiso Blume20500
Table
Table 2. Primers used in HRM assays.
Table 2. Primers used in HRM assays.
PrimerLengthSequence (5′–3′)RegionAmplicon Size (bp)
M1F22GGTACATGGACAACTGTGTGGArbcL149
M1R25ACAGAACCTTCTTCAAAAAGGTCTA
ITS1F24GGTGAACCTGCGGAAGGATCATTGITS1200–230
ITS1R24CCGAGATATCCATTGCCGAGAGTC
ITS2F21CGCCTGCTTGGGCGTCATGGCITS2200–230
ITS2R22GGGCCTCGCCTGACTTGGGGCC
Table
Table 3. Details of primer pairs generated by manual and programs.
Table 3. Details of primer pairs generated by manual and programs.
ProgrammePrimerVariable NucleotideSegment
ManualM16.0B
M22.0C
GenScriptG13.9E/F
G27.5B
G38.7D/E
Primer3P15.5B
P25.9B
P36.0B
P45.9B/C
Primer-BLASTPB111.7C/D
PB211.9C/D
PB34.0F
PB412.3C/D
PB57.6B/C
PB68.2F
PB78.0F
PB89.6F/G
PB910.4C/D
PB107.4B/C
PrimerQuest IDTPQ110.5C/D
PQ29.8C
PQ38.0D/E
PQ47.7D/E
PQ55.2E/F
Table
Table 4. Details of the candidate primers from manual design and web-based tools.
Table 4. Details of the candidate primers from manual design and web-based tools.
PrimerLengthTm (°C)GC Content Sequence (5′–3′)Variable Site (%)Group in Simulation HRMAmplicon Size (bp)
M1F2255.9346.8GGTACATGGACAACTGTGTGGA6.03149
M1R2554.25 ACAGAACCTTCTTCAAAAAGGTCTA
G3F2059.0444.8CAACCATTTATGCGTTGGAG2.31266
G3R2059.24 GCACGATGGATGTGAAGAAG
P3F2059.8544.2GGACATGGACAACTGTGTGG2.03200
P3R2059.89 CAGGGCCTTGAATCCAAATA
PB4F2060.2543.6AACTTTCCAAGGTCCGCCTC0.62163
PB4R2059.83 GGTAAAATCAAGCCCACCGC
PQ1F2262.0042.1GCATCCAAGTTGAGAGGGATAA0.52190
PQ1R2062.00 CGGTCTCTCCAACGCATAAA
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Osathanunkul, M.; Madesis, P. The Identification of Several Dipterocarpaceae and Fagaceae Trees by Barcode DNA Coupled with High-Resolution Melting Analysis. Forests 2021, 12, 1466. https://doi.org/10.3390/f12111466

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Osathanunkul M, Madesis P. The Identification of Several Dipterocarpaceae and Fagaceae Trees by Barcode DNA Coupled with High-Resolution Melting Analysis. Forests. 2021; 12(11):1466. https://doi.org/10.3390/f12111466

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Osathanunkul, Maslin, and Panagiotis Madesis. 2021. "The Identification of Several Dipterocarpaceae and Fagaceae Trees by Barcode DNA Coupled with High-Resolution Melting Analysis" Forests 12, no. 11: 1466. https://doi.org/10.3390/f12111466

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