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. 2019 Jul 2;9(8):292. doi: 10.1007/s13205-019-1823-4

Development of EST-based SSR and SNP markers in Gastrodia elata (herbal medicine) by sequencing, de novo assembly and annotation of the transcriptome

Yunsheng Wang 1,✉, Muhammad Qasim Shahid 2,3,4, Fozia Ghouri 2,3,4, Sezai Ercişli 5, Faheem Shehzad Baloch 6
PMCID: PMC6606689  PMID: 31321198

Abstract

Tianma (Gastrodia elata Blume) has unique biological characteristics and high medicinal value. The wild resource of G. elata is being overutilized and should be conserved as it is already included in the list of endangered species in China. The population size of cultivated G. elata is small because of domestication bottleneck. Therefore, it is of utmost importance to evolve high-quality varieties and conserve wild resources of G. elata. In this study, we sequenced tuber transcriptomes of three major cultivated sub-species of Gastrodia elata, namely G. elata BI. f. elata, G. elata Bl. f. glauca S. Chow, and G. elata Bl. f. Viridis, and obtained about 7.8G clean data. The assembled high-quality reads of three sub-species were clustered into 56,884 unigenes. Of these, 31,224 (54.89%), 25,733 (45.24%), 22,629 (39.78%), and 11,856 (20.84%) unigenes were annotated by Nr, Swiss-Port, Eukaryotic Ortholog Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. Here, a total of 3766 EST-SSRs and 128,921 SNPs were identified from the unigenes. The results not only offer huge number of genes that were responsible for the growth, development, and metabolism of bioactive components, but also a large number of molecular markers were detected for future studies on the conservation genetics and molecular breeding of G. elata.

Electronic supplementary material

The online version of this article (10.1007/s13205-019-1823-4) contains supplementary material, which is available to authorized users.

Keywords: Traditional Chinese medicine, Tianma (Gastrodia elata Blume), Neuron, SNP, SSR

Introduction

Genetic markers, especially DNA markers, have become indispensable tools for life science research about population genetics, molecular ecology, and conservation genetics. In the last three decades, six DNA markers namely restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter-simple sequence repeat (ISSR), simple sequence repeat (SSR), and single nucleotide polymorphism (SNP) have been globally accepted, among them, SSR and SNP are versatile markers (Sunnucks 2000; Grover and Sharma 2016; Nadeem et al. 2018). SSRs, also named microsatellites, have been widely used for its characteristics of codominant markers, genome abundance, even distribution, small locus size, and high polymorphism. Single nucleotide polymorphisms (SNPs) derived from the point mutation of genomic DNA represent the most widespread type of sequence variation in genomes of organism, besides, they are biallelic, nature abundant, and ubiquitous (Nadeem et al. 2018; Mammadov et al. 2012). With the improvement of second-generation sequencing technologies, high-throughput sequencing had quickly become a tool to develop SSR and SNP markers on large scale in different plant species (Gardner et al. 2011; Kumar et al. 2012). For example, if the reference genome is available, then re-sequencing could identify millions of SNPs (Xu and Bai 2015). Simplified genome parallel sequencing such as restriction association site DNA-tag (RAD-tag) and specific-locus amplified fragment sequencing (SLAF) could produce hundreds and thousands of SNP markers under the situation of no reference genome availability (Baird et al. 2008; Sun et al. 2015). Not only the DNA sequencing, RNA sequencing such as transcriptome sequencing has also become a common method to exploit SSR and SNP markers on large scale in the era of high-throughput sequencing (Wit et al. 2015). The mRNA sequencing yielded SSR and SNP solely in genic regions, and these markers are believed to be very powerful tool for molecular biologist to identify some causative mutations (Djari et al. 2013; Codina-Solà et al. 2015). Today, SSR and SNP markers have been used widely in multiplex research fields such as population biology (Gramazio et al. 2018), association analysis (Xie et al. 2016), modern breeding (Duarte et al. 2014), and molecular ecology and evolution (Wit and Palumbi 2013).

Gastrodia elata Blume belongs to the Orchidaceae family, and naturally grows at elevations of 400–3200 m at the edge of forest and found in Nepal, Bhutan, India, Japan, North Korea, Siberia, and different provinces of China (Jilin, Liaoning, Inner Mongolia, Hebei, Shanxi, Shanxi, Gansu, Jiangsu, Anhui, Zhejiang, Jiangxi, Henan, Hunan, Hubei, Sichuan, Guizhou, Yunnan, and Tibet) (Zhou et al. 1987; Xu 1993). It is a typical achlorophyllous orchid without roots and leaves, and depends on two compatible mycorrhizal fungi for nutrient supply, i.e., Mycena spp. during seed germination stage and Armillaria mellea during vegetative growth (Huang et al. 2004). The dry mature tuber of G. elata, called as “Tianma” in China, contains huge quantity of bioactive components such as gastrodin, p-hydroxybenzaldehyde, p-hydroxybenzyl alcohol, vanillin, parishin, and plant polysaccharides (Yu et al. 2005; Kim et al. 2007; Chen et al. 2011). Therefore, Tianma is being used as a medicinal plant in Asia for more than one thousand years to treat the headache, vertigo, dizziness, epilepsy, rheumatism, and paralysis (Xu 1992; Kim et al. 2003). Tianma has appeared to have good therapeutic effects in improving normal cardiovascular function (Kim et al. 2017) and as anti-depressant (Chen et al. 2008), neuroprotective (Yu et al. 2010), anti-inflammatory (Hwang et al. 2009), and anti-dote (Shin et al. 2011). The G. elata is comprised of six sub-species, namely Gastrodia elata f. pilifera, Gastrodia elata Bl.f. alba S. Chow, Gastrodia elata BI. f. elata, Gastrodia elata Bl. f. glauca S. Chow, Gastrodia elata Bl. f. Viridis, and Gastrodia elata Bl. f. flavida S. Chow (Chow and Chen 1983). G. elata BI. f. elata, G. elata Bl. f. glauca S. Chow, G. elata Bl. f. Viridis ,and G. elata Bl. f. flavida S. Chow are major cultivated sub-species, and have different tuber shapes and inflorescence color. For example, the inflorescence color of G. elata BI. f. elata, G. elata Bl. f. glauca S. Chow, G. elata Bl. f. Viridis, and G. elata Bl. f. flavida S. Chow are red, dark red, green, and yellow, respectively. Of these sub-species, the mature tubers of G. elata Bl. f. glauca S. Chow are the biggest and have the highest contents of gastrodin, which generally believed to be a major medicinal component. The G. elata has been domesticated and cultivated from the second half of last century (Xu 2013), and had a great ability to fulfill the market requirement. However, wild G. elata is believed to possess higher medicinal effects than that of cultivated G. elata, which is being overexploited and has been listed as rare and endangered plant in China (Zou et al. 2006).

SSR markers were used to estimate expected heterozygosity (HE) within wild G. elata populations from Hubei province of China, and it varied from 0.356 to 0.622 with an average value of 0.468, which was lower than most of the investigated orchid species (Chen et al. 2014a, b). A previous study revealed that the population size of cultivated G. elata is small because of the domestication bottleneck (Chen et al. 2015). So the existing germplasm situation of wild and cultivated G. elata is not optimistic, and would greatly limit the potential of improvement. To solve this issue, an urgent strategy is required to conserve and expand wild G. elata population. In addition, modern breeding approaches, such as molecular breeding, maybe a good option to improve the performance of cultivated G. elata. For both conservation research and molecular breeding, SSR and SNP markers are very important tools (Arif et al. 2011; Nadeem et al. 2018).

In the current study, we sequenced young tuber transcriptomes of G. elata B1. F. elata, G. elata Bl. f. glauca S. Chow, and G elata. G. elata Bl. f. Viridis. This study was planned to enhance the genomic information of G. elata, and to identify SSR and SNP markers from the unigenes of transcriptomes, which will offer plenty of molecular tools to uplift the conservation genetics and molecular breeding of G. elata.

Materials and methods

Plant material and total RNA extraction

Plant materials used in this study were collected from Xiaochaba town, Yiliang county, Yunnan province in March 2016, which is famous for producing high-quality G. elata. We selected the juvenile tubers with approximately 3 cm long and 1 cm thick to isolate total RNA, which is also suitable for the vegetative propagation of G. elata. The fresh Gastrodia tuber tissues were washed with sterilized water and dried with sterile filter paper, and then ground into powder with liquid nitrogen. Total RNA was extracted and purified by “Plant RNA Kit” (Omega Bio-tek Int, China) following the guidelines provided by company. The quality and quantity of the purified RNA were checked using agarose gel electrophoresis and spectrophotometer (Nanodrop WND-1000, Nano-Drop Technologies Inc, USA).

Library construction and sequencing

Paired-end Illumina mRNA libraries were constructed according to the provider’s instructions as follows: First, poly(A) RNA was isolated from total RNA using Oligo (dT) magnetic beads and then purified by agarose gel electrophoresis. Second, the mRNA was cleaved into smaller fragments under the ultrasonic treatment, and these RNA fragments were used as a template to synthesize the first cDNA strand with the help of reverse transcriptase, random primers, dNTPs, and buffer, and then second-strand cDNA was synthesized using buffer, dNTPs, RNaseH, and DNA polymerase-I and the first cDNA template strand. Third, the synthesized double stranded DNA was purified using Qiagen MinElute Reaction Cleanup Kit (QIAGEN, Germany). Fourth, the double stranded DNA fragments were ligated with sequencing adapters and purified. After that, suitable fragments were selected as template for PCR amplification to create the final sequencing library. Finally, the sequencing library was used to generate raw data using Illumina HiSeq 2000 at Gene De novo Biology Technique Institute Co. Ltd (Guangzhou, China).

Data filtering and de novo assembly

The raw data (accessible through NCBI, PRJNA473924) were filtered out for the sequencing adaptors, empty reads, reads with more than 5% ambiguous bases, and low-quality reads (whose average quality was less than Q20). The remaining high-quality reads were assembled into unigenes by program Trinity with built in software package Bowtie (http://bowtie-bio.sourceforge.net/index.shtml). Unigenes with more than 200 bp were used for further analysis.

Functional annotation of unigenes

Unigenes were aligned (E value of 1E−5) against Nr database of Genbank (https://www.ncbi.nlm.nih.gov/genbank/) and Swiss-Port database (https://www.uniprot.org/uniprot/), and then executed by the Blast2GO program (Conesa et al. 2005). The function prediction and classification of unigene sequences were done by aligning to the COG database (Tatusov et al. 2003). The pathway assignments of unigenes were predictedusing BLASTx (with a cut off 1E10−5) against the KEGG database (Kanehisa and Goto 2000).

Detection and development of SSR markers

The program MIcroSAtellite (MISA, http://pgrc.ipk-gatersleben.de/misa/) was used to detect simple sequence repeat (SSR) in the unigenes with the following parameters: the motifs size ranged from two to six nucleotide repeat units (six for di-, five for tri- and tetra-, and four for penta- and hexa-nucleotides), and maximum interruption distance between two SSR loci was 100 bp. The program Premier 5.0 (PREMIER Biosoft International, Palo Alto, CA) was used to design SSR primers with following criteria: GC contents ranged from 40 to 65%, primer length varied from 18 to 24 bp, the expected product size was 100–350 bp with no secondary structures, and melting temperature ranged from 50 to 65 °C.

SNP calling

The program tophat v2.0.14 build in software package bowtie (http://bowtie-bio.sourceforge.net/index.shtml) was used to call the SNPs. To get rid of false-positive mutant loci, the following criteria were set: the sequencing quality of base of SNP loci reached to Q30, the read depth of each relative base of same SNP loci was more than three intra-samples and more than five inter-sample (otherwise the loci was considered as mono-morphism), minor allele frequency was more than 15%, and SNPs located only in the annotated unigenes were counted and analyzed. All SNP information is shown in Online Resource 1.

Results

Sequencing data and unigenes assembly

A total of 80,516,400 reads were obtained, including 31,388,290, 21,519,756, and 27,608,254 reads from sequencing libraries of G. elata BI. f. elata, G. elata Bl. f. glauca S. Chow, and G. elata Bl. f. Viridis, respectively. After filtering out the reads with only adaptor, N% and low quality, the number of high-quality clean reads reached to 30,823,984, 20,956,842, and 27,113,094 in three sub-species (Table 1). The high-quality reads from three samples were comprised of 3,852,998,000, 2,619,605,250, and 3,389,136,750 nucleotides. In total, 9,861,740,000 nucleotides with 51.27% GC contents were detected (Table 1). The high-quality clean data were assembled into 59,229, 51,638, and 63,853 transcripts by the Trinity software. The assembled transcripts of these tissues were clustered into 45,673, 40,201, and 55,603 unigenes, and a total of 82,860 transcripts and 56,884 unigenes were detected. The median contig length, average contig length, total assembled bases, and GC percentage of transcripts and unigenes that assembled from total data were 577 bp, 844.34 bp, 69,961,601 bp, and 45.52%, and 454 bp, 711.02 bp, 40,445,596 bp, and 46.47%, respectively (Table S1).

Table 1.

Classification of sequencing raw data

Sub-species Raw reads Adaptor reads Low quality reads High quality clean reads Total nucleotides (nt) Q20 (%) GC (%)
Gastrodia elata f1 31,388,290 10,800 (0.03%) 553,506 (1.76%) 30,823,984 (98.20%) 3,852,998,000 97.22 51.49
Gastrodia elata f2 21,519,756 9408 (0.04%) 355,180 (1.67%) 20,956,842 (98.29%) 2,619,605,250 97.32 50.41
Gastrodia elata f3 27,608,254 10,914 (0.04%) 484,246 (1.75%) 27,113,094 (98.21%) 3,389,136,750 97.24 51.67
Total 80,516,300 31,122 (0.04%) 1,392,932 (1.73%) 78,893,920 (98%) 9,861,740,000 97.25 51.27
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f1, f2, and f3 represent G. elata B1. F. elata, G. elata Bl. f. glauca S. Chow and G. elata. G. elata Bl. f. Viridis

Functional annotation

Among the total 56,884 unigenes detected from the transcriptomes of three G. elata sub-species, 31,224 (54.89%) and 25,733 (45.24%) showed significant similarity to known proteins in the Nr and Swiss-Port databases, respectively. Furthermore, 22,629 and 11,856 unigenes could be annotated according to the Eukaryotic Ortholog Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively (Fig. 1a). In total, 32,530 (57.19%) unigenes were annotated in at least one of above four databases, and 10,470 (18.41%) unigenes were annotated by all four databases. 24,354 (42.81%) were not annotated by none of the above databases (Fig. 1a, b). A total of 19,715 unigenes, which accounted for 63.14% of total unigenes, annotated by Nr database matched to 15 top-hit species (Fig. 1c). The E value distribution of the hits showed that 41.96% of the corresponding annotated unigenes had highly significant homology (< 1E−50) to entries in the Swiss-Port database, and the E values of remaining 22.98% and 35.06% were between 1E−25 < 1E−50 and 1E−5 < 1E−25 (Fig. 1d). Based on the results of Nr annotation, 11,867 unigenes were assigned to 42 functional groups in Gene Ontology (GO) database, and most of the unigenes showed more than one functional group, and we totally detected 60,376 hits. Approximately, 28,367 (46.99%) hits fall into main functional category, namely, ‘biological process’, followed by 20,809 (34.47%) and 11,112 (18.40%) hits in the remaining two main functional categories, i.e., ‘cellular component category’ and molecular function category, respectively. The GO terms ‘metabolic process’ (5980; 9.90%) and ‘cellular process’ (4702; 7.79%), ‘cell’ (5609; 9.29%) and ‘cell part’ (5481; 9.08%), and ‘catalytic activity’ (5627; 9.34%) and ‘binding’ (4792; 7.94%) were the first and second largest groups among the main functional groups of ‘biological process’, ‘cellular component’, and ‘molecular function’, respectively (Fig. S1). A total of 22,629 unigenes that showed Nr hits and annotated by the KOG database were functionally classified into 25 molecular families. The orthology cluster described as ‘General function prediction only’ was the most frequent (6991 hits), followed by ‘Posttranslational modification, protein turnover, chaperones’ (4846 hits), and ‘Signal transduction mechanisms’ (3515 hits); whereas some other clusters were poorly characterized, such as ‘Extracellular structures’ (39 hits) and ‘Cell motility’ (87 hits) (Fig. S2). KEGG pathway enrichment analysis annotated a total of 11,856 unigenes and assigned them to five main categories. Among the 5 main categories, the largest category was ‘Metabolism’, which contained 11 sub-categories and 94 pathways, followed by ‘Genetic Information Processing’, which contained 3 sub-categories and 21 pathways, and then ‘Cellular Processes’, ‘Organismal Systems’, and ‘Environmental Information Processing’, which contained 1, 2, and 2 sub-categories, and 4, 4, and 3 pathways, respectively. Among these pathways, the unigenes numbers in ‘Metabolic pathways’ and ‘Biosynthesis of secondary metabolites’ were the highest compared to other pathways, and reached to 3224 and 1596 (Online Resource 2).

Fig. 1.

Fig. 1

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Characterization of homology search for unigenes from whole transcriptome data of Gastrodia elata. a Summary of annotated unigenes by four databases; b venn diagram of BLAST hits for unigenes against protein databases (E value < 1.0E−05). Numbers in the circles indicate the number of unigenes annotated by single or multiple databases; c species distribution of the top BLAST hits for the assembled unigenes (E value < 1.0e−05); d E value distribution of BLAST hits against Swiss-Port database for each unique sequence (E value < 1.0E−05)

Statistics of SSRs

A total of 3766 SSRs were identified from 3294 unigenes. Of these unigenes, 399 (12.11%) were found to have more than one SSRs, and 235 (7.13%) SSRs were present in compound forms (Table 2). We further designed primer pairs for these SSRs loci and their sequences are listed in Table S2. Of the 3766 identified SSRs, the most abundant repeat motif types were tri-nucleotide (1800, 47.80%), which accounted for about half of total SSRs, followed by di-nucleotide repeat motifs, tetra-nucleotide, hexa-nucleotide, and penta-nucleotide repeat motifs, and the number and frequency of these motifs were 1563 and 41.50%, 247 and 6.56%, 99 and 2.63%, and 57 and 1.51%, respectively. Among these SSRs, 5 tandem repeats type were the most common (1042, 27.67%), followed by 6 tandem repeats (883, 23.45%), 7 tandem repeats (498, 13.22%), 8 tandem repeats (351, 9.32%), and 4 tandem repeats (282, 7.49%) (Table 3). The most common type of SSR motif was AG/CT (807, 21.43%), followed by AT/AT (614, 16.30%), AAG/CTT (509, 15.52%), AGC/CTG (322, 8.55%), and ATG/ATC (242, 6.43%) (Fig. 2).

Table 2.

Identification of SSR markers in three sub-species of G. elata by RNA-seq

G. elata f1 G. elata f2 G. elata f3 Total
Total number of sequences examined 45,673 40,201 55,603 56,884
Total size of examined sequences (bp) 29,786,721 30,287,685 37,292,158 40,445,596
Total number of identified SSRs 2980 2926 3416 3766
Number of SSR containing sequences 2592 2550 3015 3294
Number of sequences containing more than 1 SSR 325 318 343 399
Number of SSRs present in compound formation 212 198 191 235
Di-nucleotide 1256 1093 1258 1563
Tri-nucleotide 1404 1490 1820 1800
Tetra-nucleotide 197 205 207 247
Penta-nucleotide 50 52 39 57
Hexa-nucleotide 73 86 92 99
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f1, f2, and f3 represent G. elata B1. F. elata, G. elata Bl. f. glauca S. Chow and G elata. G. elata Bl. f. Viridis

Table 3.

The types of SSR markers detected in G. elata

Number of repeat units Di- Tri- Tetra- Penta- Hexa Total
4 0 0 159 44 79 282
5 0 966 60 10 6 1042
6 441 414 20 1 7 883
7 271 220 3 0 4 498
8 243 103 4 0 1 351
9 184 14 0 1 1 200
10 119 29 0 1 1 150
11 130 15 1 0 0 146
12 34 16 0 0 0 50
13 0 6 0 0 0 6
14 4 6 0 0 0 10
≥ 15 137 11 0 0 0 148
Total 1563 1800 247 57 99
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Fig. 2.

Fig. 2

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SSR motif kinds and corresponding frequency identified from unigenes sequences of three G. elata sub-species

Statistics of SNPs

In this study, we identified 128,921 SNPs from three sub-species of G. elata. Among them, the numbers of transition SNPs, T/C, and A/G, were 42,707 and 43,630, which accounted for 33.13% and 33.84%, respectively. The number of transition SNPs was far more than transversion SNPs (C/A, A/T, C/G, and G/T). Among three samples, 53,149 intra-SNPs were identified in G. elata Bl. f. Viridis, which were 3.38 and 3.76 times higher than that in G. elata BI. f. elata and G. elata Bl. f. Glauca S. Chow (Fig. 3). Although different numbers of SNPs were detected in three sub-species, the SNP density distribution patterns and peak values were almost similar in three transcripts (Fig. S3).

Fig. 3.

Fig. 3

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SNPs mutation patterns and corresponding numbers in three G. elata sub-species. SNPs mutation patterns are presented in each species individually and total in three sub-species

Discussion

Medicinal plants contribute special significance to the people’s lives in terms of financial income, healthcare, livelihood security, and cultural identity (Hamilton 2004). Up to 80% of population depends primarily on herbal medicine for basic treatment of diseases in developing countries (Hamilton 2004; Pourmohammad 2013). To meet the increased demand of medicinal plants for pharmaceuticals, it is needed to take a variety of measures such as conservation of wild resources by adopting appropriate strategies, developing new high-yielding varieties or improving the existing cultivars of medicinal plants using the molecular marker-based techniques (Chen et al. 2016).

Wild resource conservation, including in situ and ex situ conservation, required to determine the protection unit that is based on the estimation of genetic diversity of geographical pattern (population genetics), and the molecular makers especially SSR and SNP markers are an indispensable tool to fulfil the requirements. About 10% of 50,000 different herbal plant species are now commercially cultivated to satisfy the demands of people (Pourmohammad 2013). In fact, the cultivation practices could improve yields of target products after providing optimal levels of water, nutrients, and suitable environmental factors (Wong et al. 2014). However, cultivation may also encounter the problems such as toxic components, pesticide contamination, and low contents of active ingredients, but cultivation also provides the opportunity to use new techniques to solve the problems. For example, the use of DNA fingerprinting technology-based molecular markers offer authentication of medicinal plants and species taxonomy (Um et al. 2001; Oleszek et al. 2002; Li et al. 2003). Molecular breeding technology based on marker-assisted selection could identify the desirable chemotypes (Fico et al. 2003). Moreover, molecular makers, especially SSR and SNP makers, have significant impact in the research field of traditional herbal medicinal plants. High-throughput sequencing has been used frequently to exploit SSR and SNP markers for herbal medicinal plants on a large scale (Yun et al. 2012; Liu et al. 2015; Su et al. 2016; Wei et al. 2016; Otto et al. 2017; Wang et al. 2019).

G. elata has been studied widely in multiplex fields such as cultivation (Xu, 2013), phytochemistry (Jang et al. 2015), and genetic diversity (Li et al. 2011; Chen et al. 2015), but SSR markers were exploited on a small scale (Xu et al. 2006). In spite of the genome information availability of G. elata (Tsai et al. 2016; Zeng et al. 2017), little is known about the SNP markers exploitation by high-throughput sequencing in G. elata. As a famous traditional herbal medicine, wild G. elata resources have always been used in large quantities, which resulted in the severe decline of wild G. elata resources. Recently, the artificial cultivation of G. elata has replaced the wild excavation and has become main source of market. However, wild G. elata is the germplasm resource of cultivated G. elata, and preserving the genetic diversity and the estimation of geographical distribution pattern of wild resources is always the most basic and important issue. For this, we need to select a reasonable protection unit and design a proper protection strategy for the conservation of wild G. elata germplasm, which primarily depends on the molecular tools such as SSR and SNP. Here, we have identified a total of 3766 EST-SSR and 128,921 SNP markers from tuber transcriptomes of three G. elata sub-species using high-throughput sequencing, which will be very helpful to assess the genetic diversity and conservation of this important herbal plant.

The detection of false-positive SNPs is a common problem in the high-through transcriptome sequencing (Canovas et al. 2010; Cirulli et al. 2010). Different SNP calling programs could generate inconsistent SNP dataset, along with the errors in data (Clevenger et al. 2015; He et al. 2017). Three common programs namely, Bowtie 2 built in SAMtools, Freebayes, and GATK Unified Genotyper, are used to call SNPs. GATK is the most conservative, and SAMtools is the most aggressive program. Freebayes and SAMtools are much more consistent with each other, and shared 104,981 SNPs (Clevenger et al. 2015). To remove the false-positive SNPs, different methods were used by researchers, such as by removing all polymorphisms associated with genes that have paralogs (Tang et al. 2006; Barbazuk et al. 2007), by setting some parameters such as the read depth (Li et al. 2012), minimum allele frequency (Byers et al. 2012), genotype score (Peace et al. 2012), base quality (Allen 2013), and mapping quality (Uitdewilligen et al. 2013). Of these, read depth and minimum allele frequency are the most commonly used parameters (Clevenger et al. 2015). Another study used window approach to eliminate false-positive SNPs, i.e., potential alleles differed by 10% or more in the window surrounding the SNPs (Han et al. 2011). In this study, we detected the SNPs with RD ≥ 5, MAF ≥ 0.15, BQ ≥ 30, which could filter most of the false-positive SNPs, and generate high-quality SNPs.

We have assembled 59,229, 51,638, and 63,853 transcripts from the sequencing data of three sub-species of G. elata, besides the detection of molecular makers. A total of 56,884 unigenes and 82,860 transcripts were obtained, and these results would be very helpful for further studies on the molecular mechanism of growth, development, and metabolism of G. elata. Interestingly, the number of assembled transcripts was lower in G. elata compared to other previously sequenced plants (Chen et al. 2014a, b; Zhang et al. 2015; Wang et al. 2016), and this phenomenon could be explained by lower number of genes in G. elata genome than other plant species (Yuan et al. 2018). Tsai et al. (2016) found many putative unigenes that encoded key enzymes (monooxygenases and glycosyltransferases) responsible for the biosynthesis of gastrodin by transcriptome analysis of juvenile tubers and vegetative propagation corms. They concluded that the enzymes (ID in KEGG: 1.14.13 and 2.4.1.12.-) involved into “Toluene degradation” pathway (KEGG: ko00623), and “Starch and sucrose metabolism” pathway (KEGG: ko00500) in KEGG dataset are responsible for the biosynthesis of gastrodin. Here, we did not find any unigene associated with the “Toluene degradation” pathway, and we also did not detect the unigenes related to glycosyltransferase (ID in KEGG: 2.4.1.12) in Ko00500. However, we identified the unigenes associated with the key enzymes (ID in KEGG: 1.12.13.-) involved into “Flavonoid biosynthesis” pathway (KEGG: Ko00941) (Fig. S4), so we speculate that the biosynthetic pathway of gastrodin is not the only single pathway associated with the medicinal effects of herbal plants. Moreover, we identified many unigenes encoding different enzymes that take part in the metabolic pathways such as “Flavonoid biosynthesis”, “Flavone and flavonol biosynthesis”, “Caffeine metabolism” “N-Glycan biosynthesis”, and different bioactive components such as caffeine, chlorogenic acid, polysaccharide, which are also responsible for healthful functions. One of the most important targets of “Tianma” is the breeding of high-healthful lines by marker-assisted breeding. Therefore, SSRs and SNPs that are located in the corresponding unigenes should be given priority for molecular breeding, because they are highly correlated with the quality of G. elata that was estimated by the contents of active ingredients.

Conclusion

We generated more than 9.8G transcriptome sequence data from tuber tissues of three Gastrodia sub-species as 125 bp paired-end reads, and assembled into 56,884 unigenes. The results of the present study would increase the knowledge about the functional genes and healthful effects of Gastrodia tubers. We also provided a valuable resource of more than 3766 SSR and 128,921 SNP markers for population genetics, molecular breeding, and association studies of G. elata.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2220 kb) (2.2MB, docx)
Supplementary material 2 (DOCX 9391 kb) (9.2MB, docx)
Supplementary material 3 (PDF 585 kb) (585.1KB, pdf)

Acknowledgements

This work was supported by Guizhou Provincial Science and Technology Foundation [LH(2015)7754].

Author contributions

YW conceived and designed the experiments; YW performed the experiment. YW, MQS, FG, SE, and FSB wrote and revised the paper. YW, MQS, and FG contributed to data analysis. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Contributor Information

Yunsheng Wang, Phone: 0086-855-8558300, Email: wys3269@126.com.

Muhammad Qasim Shahid, Email: shahidmq@gmail.com.

Fozia Ghouri, Email: fauzia@stu.scau.edu.cn.

Sezai Ercişli, Email: sercisli@gmail.com.

Faheem Shehzad Baloch, Email: balochfaheem13@gmail.com.

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