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De novo transcriptomic analysis and development of EST-SSRs for Sorbus pohuashanensis (Hance) Hedl.

  • Congcong Liu ,

    Contributed equally to this work with: Congcong Liu, Ying Dou

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Ying Dou ,

    Contributed equally to this work with: Congcong Liu, Ying Dou

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Xuelian Guan,

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Qiang Fu,

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Ze Zhang,

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Zenghui Hu,

    Affiliation College of Landscape Architecture, Beijing University of Agriculture, Beijing, China

  • Jian Zheng ,

    Affiliations College of Landscape Architecture, Beijing University of Agriculture, Beijing, China, Beijing Collaborative Innovation Center for Eco-environmental Improvement with Forestry and Fruit Trees, Beijing, China, Beijing Engineering Research Center of rural landscape planning and design, Beijing, China

  • Yizeng Lu,

    Affiliation Shandong Provincial Center of Forest Tree Germplasm Resources, Jinan, Shandong Province, China

  • Wei Li

    Affiliation College of Landscape Architecture and Forestry, Qingdao Agricultural University, Qingdao, China

De novo transcriptomic analysis and development of EST-SSRs for Sorbus pohuashanensis (Hance) Hedl.

  • Congcong Liu, 
  • Ying Dou, 
  • Xuelian Guan, 
  • Qiang Fu, 
  • Ze Zhang, 
  • Zenghui Hu, 
  • Jian Zheng, 
  • Yizeng Lu, 
  • Wei Li


Sorbus pohuashanensis is a native tree species of northern China that is used for a variety of ecological purposes. The species is often grown as an ornamental landscape tree because of its beautiful form, silver flowers in early summer, attractive pinnate leaves in summer, and red leaves and fruits in autumn. However, development and further utilization of the species are hindered by the lack of comprehensive genetic information, which impedes research into its genetics and molecular biology. Recent advances in de novo transcriptome sequencing (RNA-seq) technology have provided an effective means to obtain genomic information from non-model species. Here, we applied RNA-seq for sequencing S. pohuashanensis leaves and obtained a total of 137,506 clean reads. After assembly, 96,213 unigenes with an average length of 770 bp were obtained. We found that 64.5% of the unigenes could be annotated using bioinformatics tools to analyze gene function and alignment with the NCBI database. Overall, 59,089 unigenes were annotated using the Nr database(non-redundant protein database), 35,225 unigenes were annotated using the GO (Gene Ontology categories) database, and 33,168 unigenes were annotated using COG (Cluster of Orthologous Groups). Analysis of the unigenes using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database indicated that 13,953 unigenes were involved in 322 metabolic pathways. Finally, simple sequence repeat (SSR) site detection identified 6,604 unigenes that included EST-SSRs and a total of 7,473 EST-SSRs in the unigene sequences. Fifteen polymorphic SSRs were screened and found to be of use for future genetic research. These unigene sequences will provide important genetic resources for genetic improvement and investigation of biochemical processes in S. pohuashanensis.

1. Introduction

Sorbus pohuashanensis (Hance) Hedl. is a species of small deciduous tree in the genus Sorbus (subfamily Maloideae, family Rosaceae) that is native to China [1]. It has a high ornamental value due to the appearance of its leaves, flowers, and fruit in different seasons (S1 Fig). The trees also attract bird life, increase biodiversity, and provide appreciable ecological benefits [2]. With the increasing emphasis on city greening, greater attention is being paid to S. pohuashanensis trees because of their ornamental value and practical characteristics. However, limited bioinformatics information of S. pohuashanensis is available; previous studies on rowans mainly concentrated on the geographic distribution and habitats of wild plants [3], breeding strategies [4, 5], and population genetic diversity [68].Few molecular biology studies have been reported.

Here, we studied the transcriptome in the leaves of S. pohuashanensis trees. The transcriptome is the set of all RNA molecules in one cell or a population of cells, and includes both mRNAs and non-coding RNAs. The development of next generation sequencing of the transcriptome (RNA-seq) has provided the advantages of high accuracy and throughput, great sensitivity, and low operating costs. Not only can de novo sequencing of the transcriptome provide genetic information of a species without the aid of a reference genome, but also can forecast probable non-coding RNAs [9]. RNA-seq has gradually replaced gene chip technology and become the preferred method for genomic study of gene expression in plants [10]. RNA-seq has been used to analyze the transcriptomes of Chimonanthus praecox [11], Eucommia ulmoides [12], Platycladus orientalis [13], Myrica rubra [14], Salix spp. [15], and Syringa oblata [16]. Here, we used RNA-seq to construct a transcriptome database for S. pohuashanensis. This information will be of value for future development and genetic modification of the germplasm of this species.

Simple sequence repeats (SSRs) are one of the most efficient genetic markers. By virtue of their reproducibility, multi-allelic nature, co-dominant inheritance, relative abundance, and good genome coverage, SSR markers have been widely applied in genetic diversity studies [17]. According to their locations in the genome, SSR markers are generally divided into genomic SSRs and EST-SSRs (expressed sequence tag SSRs). In comparison with genomic SSR markers, EST-SSR markers are derived from coding regions and are believed to have some practical advantages, such as low cost and relative ease of identification, applicability to assays of functional diversity in natural populations or germplasm collections, high transferability to related species, and utility as anchor markers for comparative mapping or evolutionary studies [18].

However, the development of EST-SSRs can be difficult because of the laborious methods required and high cost of identifying ESTs. As a consequence, EST-SSR marker development in S. pohuashanensis has not been reported. With the development of next-generation sequencing technology, creating transcriptome-level sequence collections has become much quicker and cheaper. As a result, several gene-based SSRs and other genetic markers depending on such resources have been identified and developed in various plant species such as Amentotaxus spp. [19], Dipteronia spp. [20], Taxodium ‘zhongshansa’ [21], E. ulmoides [12], Pinus koraiensis [22], and Cunninghamia lanceolata [23]; however, to date, they have not been reported in S. pohuashanensis. Therefore, The transcriptome database offers an attractive alternative to complement existing SSR collections. In this study, we reported the first development of EST-SSR markers in S. pohuashanensis, which could provide new opportunities for assessing molecular phylogeny and genetic diversity in this species and other species in the genus Sorbus.

2. Materials and methods

2.1 Plant materials

Five S. pohuashanensis plants with strong growth and no indication of pests or disease infections were selected from the forest germplasm resources nursery of Beijing University of Agriculture, of the National Forest Genetic Resources Platform (NFGR). During the summer, three leaves were picked from each of the five trees (biological replicates) and separately frozen in liquid nitrogen.

Eight 2-year-old S. pohuashanensis plants from Laoshan provenance were randomly chosen from the forest germplasm resources nursery of Beijing University of Agriculture, of the National Forest Genetic Resources Platform (NFGR) and used in the development of the EST-SSR markers in this study.

2.2 DNA extraction

Genomic DNAs were extracted from leaves of the2-year-old plants using a DNA extraction kit [Model: tgDP320-03, Tiangen Biotech (Beijing) CO. LTD] following the manufacturer’s protocol. DNA quality and quantity were checked in 1% agarose gels and Eppendorf BioSpectrometer (Eppendorf, Germany), respectively.

2.3 RNA extraction and purification

Total RNA was extracted using an RNAqueousTotal RNA Isolation kit (Ambion) following the manufacturer’s instructions. The integrity of the extracted RNA was checked using an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US). Total RNA was further purified using an RNeasyMicro kit (Qiagen, GmBH, Germany) and RNase-Free DNase Set (Qiagen).

2.4 Sequencing library construction

mRNA was isolated from the purified total RNA and fragmented. The following steps were performed as described in the Illumina workflow protocol: synthesis of first and second strand cDNAs, tail end repair, addition of A to the 3′-end, joint bonding, and enrichment [24]. Five cDNA libraries were constructed, and their concentrations and sizes were checked individually using a Qubit2.0 Fluorometer and Agilent 2100 Fluorometer. Cluster generation and first dimension primer hybridization were performed using an IlluminacBot matched to llluminaHiSeq, and paired end (2×125 nt multiplex) sequencing was performed with the paired-end program. Sequencing was performed by the Shanghai Biotechnology Corporation.

2.5 Sequence data analysis and transcript assembly

To obtain clean reads, the Fastax online software (version: 0.0.13, was used to screen out unqualified reads from the raw reads; this step removed joint sequences, reads with low overall quality (mainly reads with lengths less than 20bp), reads with base N(base with uncertain identity), reads with more than 20% of the bases possessing Q-values ≤10, and or low end quality. Clean reads from the five libraries were de novo assembled using CLC Genomics Workbench (version:6.0.4) according to the scaffolding contig algorithm (word-size = 45, minimum contig length = 300) [2527]. These various steps produced the primary unigenes. These were then assembled for a second time using CAP3 EST software [28] to acquire the final unigene sequence set. This unigene set was used for further exploration of the transcriptome.

2.6 Sequence annotation and functional classification

The final unigene set was compared against the NCBI non-redundant (Nr) database and Uniprot database using BLASTX [29], with an E-value<1e-5. Gene function was annotated through these comparisons. Unigene sequences annotated to the Nr database were then compared against GO (Gene Ontology) [30] using BLASTX (E-value<1e-5). GO function classification was obtained for the top five alignment results against the processes “molecular function”, “cellular component” and “biological process”. The annotated unigenes were compared against the CDD database [31] with an E-value ≤ 1e-5. COG function prediction [32] was performed on the top five results, which were classified and then mapped on each level of COG. Finally, a mapping analysis using KEGG (Kyoto Encyclopedia of Genes and Genomes) [33] was performed using the online pathway alignment analysis tool KEGG KASS.

2.7 Identification of EST-SSRs loci

The unigene sequences were analyzed for SSRs using MISA software [34]. SSR loci were identified using the search criteria that the minimum repetitions of di-, tri-, tetra-, penta-, and hexa-nucleotides were 6, 5, 4, 4, and 4, respectively, and the flanking sequence length of the SSR loci was greater than 50 bp. As it is difficult to authenticate the presence and stability of single nucleotide repeats and compound nucleotide repeats, these two structures were not analyzed in this study.

2.8 Primer screening and designing of EST-SSR markers

Primer pairs flanking the SSRs were designed using Primer3 online design software ( following the core criteria: the optimum length of the primers was 20 bp, ranging from 18 to 22 bp; the annealing temperature was between 55°C and 62°C; the GC content ranged from 40–60%; and the optimum size of the PCR products was 100–300 bp. A total of 140 EST-SSRprimer pairs were designed with the criteria mentioned above (S1 File). To confirm their utility, all of the designed primers were screened using a sample from one 2-year-old plant, which was chosen at random, by 2% agarose gel electrophoresis. Then, primers producing a single clear band were screened on samples from four other plants by 3% agarose gel electrophoresis. Finally, the primers producing a single clear band in all the four samples were screened on samples from eight plants using an ABI 3730XL capillary electrophoresis analyzer, and the polymorphic SSR loci were identified.

Polymerase chain reaction (PCR)and polymorphic marker validation.

Genotyping PCR amplifications were performed in 25 μL reaction volumes as follows: 12.5 μL of 2×Taq MasterMix (Ruibiotech, Beijing, China), approximately 50 ng of DNA, 5 pmol forward primer with the 5′end labeled with a fluorescent dye (FAM; Ruibiotech, Beijing, China), 5 pmol reverse primer, and sterile double-distilled water added to 25 μL. The amplifications were performed using the following schedule: denaturation at 94°C for 5 min; 30 cycles of denaturation for 30 s at 94°C, annealing for 30 s at the optimal temperature, and then extension for 30 s at 72°C; a final extension at 72°C for 7 min. All the PCR amplifications were performed using the same thermal cycler (Applied Biosystems, Foster, CA, USA). The PCR products were separated by an ABI 3730XLcapillary electrophoresis analyzer (Applied Biosystems, Foster, CA, USA) with a GeneScan-500LIZ size standard. Fragments were genotyped for their presence/absence at each locus, and the allele sizes were scored using GeneMaker 2.2.0 software (Soft Genetics LIC, State College, PA, USA) and visually checked twice to reduce genotyping errors.

3. Results

3.1 De novo assembly

The preliminary assembly yielded 137,506 contigs, with an average length of 588 bp and an N50 value of 650 (Table 1). Using the CLC Genomics Workbench, 115,987 primary unigenes with an average length of 703 bp (N50 value: 768) were obtained. The primary unigenes were assembled using CAP3 EST software to produce a final unigene set of 96,213 unigenes with an average length of 700 bp and N50 value of 894.

Table 1. Summary of assembly statistics for Sorbus pohuashanensis leaf transcriptome.

The final unigene set contained 19,614 sequences (20.39%), with lengths less than 400 bp; 32,971 unigenes (34.27%), with lengths ranging from 401 to 600 bp; 14,962 unigenes (15.55%), with lengths ranging from 601 to 800 bp;8,582 unigenes (8.91%), with lengths ranging from 801 to 1000 bp; and 20,084 unigenes (20.87%), with lengths above 1000 bp (Fig 1).

Fig 1. Length distribution of assembled Sorbus pohuashanensis leaf unigenes.

Clean reads for each S. pohuashanensis leaf were combined and resulted in 96,213 unigenes. Horizontal and vertical axes show the size and number of unigenes, respectively.

3.2 Gene function annotation

The final unigene set was searched against the Nr, Uniprot, GO, COG and KEGG databases and a total of 62,053 unigenes were annotated (Table 2). From the species statistics on unigenes that were annotated in the Nr database, we found that 26,788 genes (45.34%) were annotated to Malus domestica, 22,512 genes (38.10%) to Pyrus × bretschneideri, 1,369 (2.32%) to Pyrus persica, and 1,270 (2.15%) to P. mume. This suggests that S. pohuashanensisis is closely related to M. domestica and Pyrus × bretschneideri, and less closely related to P. persica and P. mume. The details of the comparison are shown in Fig 2 and S2 File.

Fig 2. Species-based distribution of BLASTX matches for unigenes against NCBI Nr database.

We used all plant proteins in the NCBI Nr database in performing the homology search; for each unigene, we selected the closest match for analysis.

Table 2. Statistics of annotation results for Sorbus pohuashanensis unigenes.

3.3 GO classification for unigenes

A search of the GO database using BLAST2GO software and the final unigene set generated 35,225 unigenes that annotated to the GO database. GO classification was carried out on the annotated unigenes which showed they mainly involved three biological functions: 28,895 (82.03%) unigenes annotated to “biological process” (GO: 0008150); 12,495 unigenes (35.47%) annotated to “cellular component” (GO: 0005575); and 31,424 (89.21%) annotated to “molecular function” (GO: 0003674). The GO database annotation indicated that the 35,225 unigenes annotated to 2,939 GO terms; biological process included 2,888 GO terms and was the largest group (Table 3, S2 Fig).

Table 3. Statistics of GO functional annotation results for Sorbus pohuashanensis leaf unigenes.

Unigenes in the three biological function categories were divided into 52 biological function subgroups using GO function classification. In the biological process category, 23,859 (67.73%) were assigned to the metabolic process subgroup, 19,036 (54.04%) to the cellular process subgroup, and 12,604 (35.78%) to single-organism process genes.In the cellular component category, 8,001 (22.71%) were assigned to the cell and cell part expression, 5,772 (16.39%) to the membrane, 5,669 (16.09%) to the organelle. In the molecular function category, 21,323 (60.53%) were assigned to the binding function, 18,652 (52.95%) to the catalytic activity (Fig 3, S3 File).

Fig 3. Gene Ontology (GO) classification of Sorbus pohuashanensis leaf unigenes.

Left y-axis indicates the percentage of unigenes in subcategories of each main category. Right y-axis indicates the number of unigenes in each subcategory.

3.4 Protein classification and function predictions

The Clusters of Orthologous Groups (COG) of proteins includes data from bacteria, algae, and eukaryotes and is based on phylogenetic relationships. The final unigene set obtained here was searched against the COG database (E-value ≤ 1e-5), and prediction of COG function classification was performed on annotated unigenes. Of the 96,213 unigenes in the final set, 33,168 were annotated to the COG database and assigned to 25 function classifications (Table 3, Fig 4). The largest group of unigenes (16,748 members, 50.49%) was assigned to the T class (signal transduction mechanisms); R class (general function prediction) accounted for 9,449 unigenes (28.49%); O class (posttranslational modification, protein turnover, chaperones) accounted for 8,780 (26.47%); N class (cell motility) was the smallest, with only 22 unigenes (Fig 4, S4 File). We also found that 441 unigenes (1.33% of total) were involved in V class (defense mechanisms) (Fig 4). The identification of genes participating in signal transduction and defense mechanisms will be of value for future studies on abiotic stress mechanisms in S. pohuashanensis. Isolation and identification of related genes will also be of importance for future work.

Fig 4. COG functional classification of Sorbus pohuashanensis unigenes.

From the final set of 96,213 unigenes, 33,225 were annotated with significant homology in the COG database (E-value≤1.0 E–5) and were classified into 25 KOG categories.

3.5 Metabolic pathway analysis of unigenes

KEGG enables assignment of unigenes to intracellular metabolic pathways; thus, the function of gene products can be analyzed systematically and complex biological behaviors can be explored. We performed a KEGG mapping analysis of the final unigenes set and found that 13,953 unigenes were assigned to 322 metabolic pathways (S5 File). Of these 13,953 unigenes, 980 (7.02%) were involved in metabolic pathways with large numbers of genes; 435 (3.12%) were involved in biosynthesis of secondary metabolites 217 (1.56%) were involved in biosynthesis of antibiotics and 179 (1.28%) in microbial metabolism in diverse environments. Only a few unigenes (0.3%) were involved in the signaling pathway, such as AMPK, MAPK, and P53. Undoubtedly, these data will be useful for future metabolic research in S. pohuashanensis.

3.6 EST-SSR marker analysis of S. pohuashanensis

We identified 7,473 SSR sites in 6,604 unigenes (7.7%) of the annotated 96,213 unigenes (Table 4). Among these 6,604 unigenes, one SSR site was present on average every 9.1 kb sequence; 5,839 unigenes contained only one site, while 765 unigenes contained two or more sites (Table 4, S6 File).Di-nucleotide repeats accounted for the majority (66.49%)of the identified SSRs; tri-nucleotide repeats accounted for 31.02% of the total, whereas, tetra-nucleotide, penta-nucleotide and hexa-nucleotide repeats were rare and accounted for 2.49% (Table 4). The frequencies of AG/CT and GA/TC were highest in di-nucleotide repeats, accounting for 39.98% and 31.62% of di-nucleotide SSRs, respectively. AT and TA repeats accounted for 9.40% and 7.97% of di-nucleotide SSRs respectively. We also found a few CG/GC repeats (0.2%) in the di-nucleotide repeats (Fig 5).We identified 583 different repeat unit types in the 7,473 SSR sites; overall, we found 179 di-nucleotide, 262 tri-nucleotide, 92 tetra-nucleotide, 32 penta-nucleotide and 18 hexa-nucleotide type repeats (S5 File).

Table 4. Distribution of the SSR motifs in the Sorbus pohuashanensis transcriptome.

3.8 Validation of EST-SSRs

We selected 24 from the 140 SSR primer pairs that provided a clear and consistent amplification band after PCR amplification (S1 File). Using an ABI3700 DNA analyzer, the selected 24 pairs of primers (the forward primer was 5′end labeled with FAM fluorescent dye) were tested and analyzed; 15 SSRs were found to be polymorphic. The primer sequence, motif repeats, and allele number of each SSR marker are given in Table 5, and the genotype profiles of the eight 2-year-old plants are shown in the S7 File.

4. Discussion

4.1 Transcriptome sequencing, assembly, and functional annotation of unigenes

S. pohuashanensis is a native tree species in China with considerable ornamental value in urban landscapes. However, the dearth of information on its genetics has limited breeding and exploitation of the species. Here, we used an RNA-seq approach to sequence the transcriptome of leaves and used these sequences to identity and characterize the genes and proteins of the S. pohuashanensis leaves. The average length of the unigenes obtained here (770 bp) was greater than reported for P. orientalis (475 bp) [13], M. rubra (531 bp) [14], bamboo (736 bp) [35], willow (652 bp), and Salix suchowensis (723bp) [15], although less than for S. oblata (853 bp) [16] and C. praecox (1190 bp) [11]. A total of 62,053 unigenes were identified in this study; 35.50% of these had no homologs in the NCBI database. However, the annotated unigenes showed greater similarity to sequences from M. domestica and Pyrus × bretschneideri (homology matching rate of 83.44%) than to other species. This result is in good agreement with taxonomic studies and confirms that S. pohuashanensisis more closely related to M. domestica and Pyrus × bretschneideri [1].

Annotation of the unigenes showed that the largest group annotated to the molecular function group (89.21%) (Table 2, S2 Fig). This result is different to those reported for S. oblata and C. praecox, probably because of use of different tissue samples; the transcriptomes analyzed in S. oblata and C. praecox were obtained from flowers [11, 16]. Our KEGG function annotation analysis showed that 13,953 unigenes participated in 322 metabolic pathways. The largest number of unigenes were involved in metabolic pathways, while those involved in biosynthesis of secondary metabolites were next most frequent. These data will be of value for future metabolic research in S. pohuashanensis. Overall, our sequencing of the S. pohuashanensis transcriptome has provided considerable information that will be of use to future studies on a range of practical applications of this species.

4.2 EST-SSR markers analysis of S. pohuashanensis

Based on their high rate of polymorphism, good reproducibility, and codominance, SSRs have been widely used for plant DNA fingerprinting, analysis of genetic diversity, gene mapping, and molecular marker-assisted breeding. However, the development of SSR marker primers is essential for their use [36, 37]. Simple sequence repeat markers for expressed sequence tags (ESTs) identify coding sequences and can be used to directly obtain information on gene expression as they are closely linked to functional genes. Additionally, the primers for SSRs have high transferability among related species. A large number of EST sequences have been identified from transcriptome data using high-throughput sequencing [38]. To improve research into genetic diversity and marker assisted selection, we can use transcriptome sequences to develop EST-SSR markers. In this study, we found 7,473 SSR sites in 6,604 of the 96,213 annotated unigenes, i.e., 7.77% of the unigenes, contained SSR sites (Table 4). This rate of SSR sites is higher than in E. ulmoides (6.54%) [12], Ginkgo biloba (5.97%) [39], S. suchowensis (5.4%) [15], and Jatropha carcas (6.51%) [40]. We also found that 5,839 unigenes contained only one SSR site, while 765 contained two or more sites (S6 File). On average, there was one SSR site every 9.1 kb DNA sequence (Table 4). These results suggested that the frequency of EST-SSRs in S. pohuashanensis was higher than E. ulmoides (one every 11.61 kb) [12], Populus spp. (one every 14 kb), and G. biloba (one every 12.02 kb) [39], but lower than Camellia sinensis (one every 3.68 kb) [41], Liriodindron tulipifera (one every 8.5 kb) [42], and Heveabra siliensis (one every 3.93 kb) [43].

Di-nucleotide repeats were the most common type (Table 4). The frequencies of AG/CT and GA/TC di-nucleotides (39.98% and 31.62%, respectively), were similar to those reported for E. ulmoides [12], Salvia splendens [44], Saccharum officinarum [45], and Sorghum bicolor [46]. Simultaneously, we also found a few CG/GC repeats, accounting for 0.20% of di-nucleotide SSRs (Fig 5). In addition, CTT/GAA, AAG/TTC, CTC/GAG, AGA/TCT, CCT/GGA, AGG/TCC, CCA/GGT, AAC/TTG, CAC/GTG, and ACC/TCC sequences accounted for 59.71% of the total tri-nucleotides, and were mainly tri-nucleotide repeat motif types (S6 File). This result was similar to G. biloba [39], C. sinensis [41], and L. tulipifera [42]. We believe that our analysis provides a solid foundation for the development and application of S. pohuashanensis EST-SSRs.

Finally, we designed a total of 140 pairs of EST-SSR primers and validated their accuracy using samples from eight 2-year-old S. pohuashanensis plants. We found that 130 primer pairs produced amplification products (data not shown), i.e., an effective amplification rate of 92.86%.This rate is higher than that reported for walnut (90.9%) [47], pear (64.6%) [48], and apple (63.9%) [49]. Some primer pairs did not amplify products, probably because the EST-SSR location of the primers contained introns in the genome sequences. However, only 15 EST-SSR primer pairs were polymorphic, accounting for 11.54% of primer pairs that successfully amplified products. This rate is significantly lower than in apples[49], pears[48], Amentotaxus spp. [19], and C. lanceolata[23]. The polymorphism levels might be related to quantity differences in the sample materials. The polymorphism results indicate that it will be feasible to use the 15 EST-SSRs for genetic research on S. pohuashanensis in the future.

5. Conclusions

We used RNA-seq to perform de novo transcriptome sequencing of S. pohuashanensis. Additionally, we also obtained information on EST-SSRs. To the best of our knowledge, this is the first comprehensive report on an S. pohuashanensis transcriptome. The unigenes were assembled without use of a reference genome. More than 80 Gbp raw reads and 96,213 unigenes (total length 74.06 Gbp) were acquired by splice and assembly; 62,053 unigenes were annotated in the Nr, Uniprot, GO, COG, and KEGG databases. In addition, we identified 7,743 EST-SSRs and discovered 15 EST-SSRs with polymorphism through experimental verification. These findings will provide a valuable information resource for use in research on genetic improvement, genetic engineering, and other biochemical studies in S. pohuashanensis.

Supporting information

S1 Fig. Appearance of Sorbus pohuashanensis.

a: Spring; b: Summer; c: early Autumn; d: middle or late Autumn.


S2 Fig. Distribution of GO functional level.


S1 File. The repeat motif types and the primer sequences of the 140 SSRs for Sorbus pohuashanensis.


S2 File. Species-based distribution of BLASTX matches for unigenes against NCBI Nr database.


S3 File. Summary of GO term assignment for the Sorbus pohuashanensis leaf transcriptome.



S4 File. COG annotation of Sorbus pohuashanensis leaf unigenes.


S5 File. Summary of KEGG pathways involved in the Sorbus pohuashanensis leaf transcriptome.


S6 File. Summary of the simple sequence repeats (SSRs) of Sorbus pohuashanensis.


S7 File. Genotype profiles of 15 SSR loci in eight 2-year-old Sorbus pohuashanensis plants.



We thank the National Forest Genetic Resources Platform (NFGRP) for providing the Sorbus pohuashanensis resources. We also thank Professor Yongxiu Liu (Institute of Botany, Chinese Academy of Sciences) for his excellent advice on this paper and thank “Editage English Editing company” for editing this manuscript.

Author Contributions

  1. Conceptualization: JZ.
  2. Data curation: CL XG ZH YL WL.
  3. Formal analysis: CL YD JZ.
  4. Funding acquisition: JZ.
  5. Investigation: JZ.
  6. Methodology: CL YD QF ZZ.
  7. Project administration: JZ.
  8. Resources: JZ.
  9. Software: XG ZH YL WL.
  10. Supervision: JZ.
  11. Validation: JZ.
  12. Writing – original draft: CL YD.
  13. Writing – review & editing: JZ.


  1. 1. Zheng Wanjun. SylvaSinica. Beijing: ChinaForestry Publishing House; 1985. 1009 p.
  2. 2. Labuda R, Krivánek L, Tančinová D, Mátéová S, Hrubcová S. Mycological survey of ripped service tree fruits (Sorbus domestica L.) with an emphasis on toxinogenic fungi. International Journal of Food Microbiology. 2005;99(2):215–23. pmid:15734569
  3. 3. Zheng J, Zheng Y, Wu C, Zhang C, Zong Y, Li B, et al. Geographical distribution and patterns of natural regeneration of Sorbus pohuashanensis. Scientia Silvae Sinicae. 2007;43(12):86–93.
  4. 4. Zheng J, Zheng Y, Wu C. Propagation of Sorbus pohuashanensis by softwood cuttings. Forest Research. 2009;22(1):91–7.
  5. 5. Hailong Shen, Yang L, Zhang J, Feng D, Fan S. Influencing factors to seed dormancy and germination characteristics of Sorbus pohuashanensis. Scientia Silvae Sinicae. 2006;42(10):133–8.
  6. 6. Zheng J, Zheng Y, Zong Y, Zhang C, Li B, Wu C. Genetic diversity in natural populations of Sorbus pohuashanensis. Biodiversity Science. 2008;16(6):562–9.
  7. 7. Zheng J, Zheng Y, Zhang C, Zong Y, Li B. Outcrossing rates in 6 natural populations of Sorbus pohuashanensis. Scientia Silvae Sinicae. 2009;45(11):44–9.
  8. 8. Zheng J, Hu Z, Zheng Y, Xie Y. Analysis on geographic variation of phenotypic traits of Sorbus pohuashanensis among different provenances. Journal of Plant Resources and Environment. 2012;21(3):50–6.
  9. 9. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009;10(1):57–63. pmid:19015660
  10. 10. 'tHoen PAC, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RHAM, Menezes de RX, et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Research. 2008;36(21):e141. pmid:18927111
  11. 11. Liu DF, Sui SZ, Ma J, Li ZN, Guo YL, Luo DP, et al. Transcriptomic Analysis of Flower Development in Wintersweet (Chimonanthus praecox). PLoS ONE. 2013;9(1):e86976. pmid:24489818
  12. 12. Huang HY, Du HY, Wuyun T, Liu PF. Development of SSR Molecular Markers Based on Transcriptome Sequencing of Eucommia ulmoides. Sientia Silvae Sinicae. 2013;49(5):176–81.
  13. 13. Chang E, Shi S, Liu J, Cheng T, Xue L, Yang X, et al. Selection of Reference Genes for Quantitative Gene Expression Studies in Platycladus orientalis (Cupressaceae) Using Real-Time PCR. PLoS ONE. 2012;7(3):e33278. pmid:22479379
  14. 14. Feng C, Chen M, Xu CJ, Bai L, Yin XR, Li X, et al. Transcriptomic analysis of Chinese bayberry (Myrica rubra) fruit development and ripening using RNA-Seq. BMC Genomics. 2012;13:19. pmid:22244270
  15. 15. Zheng JW. Research on transcriptome sequencing and SSR markers development of salix. Nanjing: Nanjing Forestry University; 2013.
  16. 16. Zheng J, Hu Z, Guan X, Dou D, Bai G, Wang Yu, et al. Transcriptome analysis of Syringa oblata Lindl. inflorescence identifies genes associated with pigment biosynthesis and scent metabolism. PLoS ONE. 2015;10(11):e0142542. pmid:26587670
  17. 17. Kalia RK, Rai MK, Kalia S, Singh R, Dhawan AK. Microsatellite markers: an overview of the recent progress in plants. Euphytica. 2011;177(3):309–34.
  18. 18. Najafzadeh R, Arzani K, Bouzari N, Saei A. Genetic variation and identification of promising sour cherries inferred from microsatellite markers. Russian Journal of Genetics. 2016;52(1):64–73.
  19. 19. Li C, Chiang T, Chiang Y, Hsu H, Ge X, Huang C, et al. Cross-Species, amplifiable EST-SSR markers for Amentotaxus Species obtained by next-generation sequencing. Molecules. 2016;21(1):67. pmid:26751439
  20. 20. Zhou T, Li Z, Bai G, Feng L, Chen C, Wei Y, et al. Transcriptome Sequencing and development of genic SSR Markers of an endangered Chinese endemic genus dipteronia Oliver (Aceraceae). Molecules. 2016;21(3):166. pmid:26907245
  21. 21. Cheng Y, Yang Y, Wang Z, Qi B, Yin Y, Li H. Development and Characterization of EST-SSR Markers in Taxodium 'zhongshansa'. Plant Molecular Biology Reporter. 2015;33(6):1804–14.
  22. 22. Zhang Z, Zhang H, Mo C, Zhang L. Transcriptome sequencing analysis and development of EST-SSR markers for Pinus koraiensis. Scientia Silvae Sinicae. 2015;51(8):114–20.
  23. 23. Wen Y, Han W, Zhou H, Xu G. SSR mining and development of EST-SSR markers for Cunninghamia lanceolata based on transcriptome sequences. Scientia Silvae Sinicae. 2015;51(11):40–9.
  24. 24. Mao XZ, Cai T, Olyarchuk JG, Wei LP. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005;21(19):3787–93. pmid:15817693
  25. 25. Garg R, Patel RK, Tyagi AK, Jain M. De Novo Assembly of Chickpea Transcriptome Using Short Reads for Gene Discovery and Marker Identification. DNA Research. 2011;18(1):53–63. pmid:21217129
  26. 26. Bräutigam A, Mullick T, Schliesky S, Weber APM. Critical assessment of assembly strategies for non-model species mRNA-Seq data and application of next-generation sequencing to the comparison of C3 and C4 s pecies. Journal of Experimental Botany. 2011;62(9):3093–12. pmid:21398430
  27. 27. Su CL, Chao YT, Alex-Chang YC, Chen WC, Chen CY, Lee AY, et al. De Novo Assembly of Expressed Transcripts and Global Analysis of the Phalaenopsis aphrodite Transcriptome. Plant Cell Physiology. 2011;52 (9):1501–14. pmid:21771864
  28. 28. Huang XQ, Madan A. CAP3: A DNA sequence assembly program. Genome Research. 1999;9:868–77. pmid:10508846
  29. 29. Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research. 2007;35(suppl. 1):D61–D5. pmid:17130148
  30. 30. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000;25(1):25–34. pmid:10802651
  31. 31. Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, et al. CDD: NCBI's conserved domain database. Nucleic Acids Research. 2015;43:D222–D6. pmid:25414356
  32. 32. Tatusov RL, author c, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003;4:41. pmid:12969510
  33. 33. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Research. 2007;35(suppl. 2):182–5. pmid:17526522
  34. 34. Wei WL, Qi XQ, Wang LH, Zhang YX, Hua W, Li DH, 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
  35. 35. Liu MY, Qiao GR, Jiang J, Yang HQ, Xie LH, Xie JZ, et al. Transcriptome sequencing and de novo analysis for Ma Bamboo (Dendrocalamus latiflorus Munro) using the Illumina platform. PLoS ONE. 2012;7(10):e46766. pmid:23056442
  36. 36. Varshney RK, Sigmund R, Brner A, Korzun V, Stein N, Sorrells ME, et al. Interspecific transferability and comparative mapping of barley EST-SSR markers in wheat, rye and rice. Plant Science. 2005;168:195–202.
  37. 37. Marconi TG, Costa EA, Miranda HR, Mancini MC, Cardoso-Silva CB, Oliveira KM, et al. Functional markers for gene mapping and genetic diversity studies in sugarcane. BMC Research Notes. 2011;4:264. pmid:21798036
  38. 38. Simon SA, Zhai J, Nandety RS, McCormick KP, Zeng J, Mejia D, et al. Short-Read sequencing technologies for transcriptional analyses. Annual Review of Plant Biology. 2009;60:305–33. pmid:19575585
  39. 39. Fan HH, Li TC, Li ZP, Lin Y, Cai YP. Characteristics of EST-SSR distribution in Ginkgo ESTs. Genomics and Applied Biology. 2009;28(5):869–73.
  40. 40. Yadav HK, Ranjan A, Asif MH, Mantri S, Sawant SV, Tuli R. EST-derived SSR markers in Jatropha curcas L. development, characterization, polymorphism, and transferability across the species/genera. Tree Genetics & Genomes. 2011;7(1):207–19.
  41. 41. Yang H, Chen Q, Wei CL, Shi CY, Fang CB, Wan XC. Analysis on SSR information in Camellia sinensis transcriptome. Journal of Anhui Agricultural University. 2011;38(6):882–6.
  42. 42. Xu M, Sun YG, Li HG. EST-SSRs development and paternity analysis for Liriodendron spp. New Forests. 2010;40(3):361–82.
  43. 43. An ARoPBSJoGSS, Zhao Y, Cheng H, Li W, Huang H. Development and application of EST-SSR markers in Hevea brasiliensis Muell. Hereditas. 2009;31(3):311–9. pmid:19273446
  44. 44. Ge XX, Chen HW, Wang HL, Shi AP, Liu KF. De Novo assembly and annotation of salvia splendens transcriptome using the Illumina platform. PLoS ONE. 2014;9(3):e87693. pmid:24622329
  45. 45. Cardoso-Silva CB, Costa EA, Mancini MC, Balsalobre TWA, Canesin LEC, Pinto LR, et al. De novo assembly and transcriptome analysis of contrasting sugarcane varieties. PLoS ONE. 2014;9(2):e88462. pmid:24523899
  46. 46. Ramu P, Kassahun B, Senthilvel S, Kumar CA, Jayashree B, Folkertsma RT, et al. Exploiting rice-sorghum synteny for targeted development of EST-SSRs to enrich the sorghum genetic linkage map. Theoretical and Applied Genetics. 2009;119(7):1193–204. pmid:19669123
  47. 47. Zhu Y, Hao Y, Wang K, Wu C, Wang W, Qi J, et al. Analysis of SSRs information and development of SSR markers from walnut Ests. Journal of Fruit Science. 2009;26(394–398).
  48. 48. Wang X, Jiang s, Shangguang L, Cao Y, Qiao Y, Zhang Z, et al. Development of EST-derived SSR markers for pear and evaluation of their application in pear genetic diversity analysis. Scientia Agricultura Sinica. 2010;43(24):5079–87.
  49. 49. Yao L, Zheng X, Cai D, Gao Y, Wang K, Cao Y, et al. Exploitation of Malus EST-SSRs and the utility in evaluation of genetic diversity in Malus and Pyrus. Genetic Resources and Crop Evolution. 2010;57(6):841–51.