- Research article
- Open Access
De novo assembly and characterization of bark transcriptome using Illumina sequencing and development of EST-SSR markers in rubber tree (Hevea brasiliensis Muell. Arg.)
© 2012 Li et al.; licensee BioMed Central Ltd. 2012
- Received: 30 November 2011
- Accepted: 3 May 2012
- Published: 18 May 2012
In rubber tree, bark is one of important agricultural and biological organs. However, the molecular mechanism involved in the bark formation and development in rubber tree remains largely unknown, which is at least partially due to lack of bark transcriptomic and genomic information. Therefore, it is necessary to carried out high-throughput transcriptome sequencing of rubber tree bark to generate enormous transcript sequences for the functional characterization and molecular marker development.
In this study, more than 30 million sequencing reads were generated using Illumina paired-end sequencing technology. In total, 22,756 unigenes with an average length of 485 bp were obtained with de novo assembly. The similarity search indicated that 16,520 and 12,558 unigenes showed significant similarities to known proteins from NCBI non-redundant and Swissprot protein databases, respectively. Among these annotated unigenes, 6,867 and 5,559 unigenes were separately assigned to Gene Ontology (GO) and Clusters of Orthologous Group (COG). When 22,756 unigenes searched against the Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) database, 12,097 unigenes were assigned to 5 main categories including 123 KEGG pathways. Among the main KEGG categories, metabolism was the biggest category (9,043, 74.75%), suggesting the active metabolic processes in rubber tree bark. In addition, a total of 39,257 EST-SSRs were identified from 22,756 unigenes, and the characterizations of EST-SSRs were further analyzed in rubber tree. 110 potential marker sites were randomly selected to validate the assembly quality and develop EST-SSR markers. Among 13 Hevea germplasms, PCR success rate and polymorphism rate of 110 markers were separately 96.36% and 55.45% in this study.
By assembling and analyzing de novo transcriptome sequencing data, we reported the comprehensive functional characterization of rubber tree bark. This research generated a substantial fraction of rubber tree transcriptome sequences, which were very useful resources for gene annotation and discovery, molecular markers development, genome assembly and annotation, and microarrays development in rubber tree. The EST-SSR markers identified and developed in this study will facilitate marker-assisted selection breeding in rubber tree. Moreover, this study also supported that transcriptome analysis based on Illumina paired-end sequencing is a powerful tool for transcriptome characterization and molecular marker development in non-model species, especially those with large and complex genomes.
- Rubber tree
- Illumina paired-end sequencing
- de novo assembly
Natural rubber is one of the most important raw materials for many industries, and it cannot be replaced by synthetic alternatives due to its unique properties, such as resilience, elasticity, impact and abrasion resistance, efficient heat dispersion and malleability at cold temperature [1, 2]. Among over 2,000 plant species recognized for producing rubber, Hevea brasiliensis Muell. Arg. is the only species cultivated commercially for natural rubber. H. brasiliensis is a cross-pollinated, diploid (2n = 2× = 36) and perennial plant with a large genome size (~2100 Mb) . Despite growing demand and high-yield potential, the production of natural rubber is relatively low, especially in China. Biotic and abiotic stresses, such as tapping panel dryness (TPD), powdery mildew, leaf blight, low temperature, strong wind and drought, are major yield-limiting factors on natural rubber production. The combination of conventional and modern breeding technologies will be helpful to increase the yield of rubber tree . However, very limited genomic resources are available for rubber tree, which restricted the development of modern breeding technologies.
Various genomic tools have facilitated the development of improved genotypes/varieties in several crop species [5, 6]. In rubber tree, expressed sequence tags (ESTs) and molecular markers have been developed, but the functional genomic studies are still in their infancy. Currently, there are only 37,745 rubber tree ESTs available in the national center for biotechnology information (NCBI) database (as of Dec 2011). Most of these ESTs were generated with the aim to identify the candidate genes involved in various abiotic and biotic stress responses and rubber biosynthesis [7–20], whereas only superoxide dismutase was further studied by using transgenic approaches [21–23]. Among these 37,745 ESTs, most were derived from the latex, and only a few from the bark and leaf. The recent transcriptome sequencing work from Xia et al.  and Triwitayakorn et al.  added millions of next-generation sequencing reads separately with both Illumina and Roche platforms. Other techniques such as microarray, serial analysis of gene expression (SAGE) and digital gene expression (DGE) have not been utilized so far in rubber tree. Molecular markers have been developed and employed for DNA fingerprinting in rubber tree [25–31], but the categories and numbers of molecular markers cannot keep up with the biological development of rubber tree.
During the last decade, a large number of genomic and transcriptomic sequences became available in model plants, such as Arabidopsis and rice, which has greatly improved our understanding of the growth and development in higher plants. For rubber tree, only limited genomic and transcriptomic sequences are available. The rubber tree genome is highly heterozygous because of its cross-pollination nature. Thus, transcriptome sequencing is an attractive alternative to whole-genome sequencing because transcriptome sequencing only focuses on the transcribed portions of the genome and avoids the non-coding and repetitive sequences that make up the majority of most eukaryotic genomes. The RNA-seq approach provides a cost-effective means for sequencing the transcriptome of an organism. Several transcriptome studies reported with RNA-seq techniques so far indicated that it was feasible for plant species to assemble and analyze the transcriptome with short-read sequence data [24, 32–39]. Using the mixed materials of leaf and latex, Xia et al.  recently reported de novo transcriptome assembly of rubber tree with RNA-seq approach and submitted 37,432 unigenes.
Besides important roles in protecting plants, transporting water and nutrients and storing proteins, rubber tree bark contains the laticifers where latex is synthesized and stored. Compared with other tissues, bark is more important agricultural and biological organ in rubber tree. However, there are very limited data available for understanding the transcriptome of rubber tree bark. In this study, the transcriptome from rubber tree bark was sequenced with Illumina paired-end sequencing technology, the sequencing data were assembled and annotated, and EST-SSR markers were developed in rubber tree. To our knowledge, this is the first systematic report on the transcriptome of rubber tree bark. The research is essential and helpful to understand the transcriptome characterization of rubber tree bark. The transcriptome data generated from our study are very useful resources for gene annotation and discovery, molecular markers development, genomic and transcriptomic assembly, and microarrays development in rubber tree. In addition, the EST-SSR markers predicted and developed in this study will enrich the number of molecular markers, and facilitate genes mapping, linkage map development, genetic diversity analysis, and marker-assisted selection breeding in rubber tree.
Illumina sequencing and de novo assembly
Characteristics of assembled contigs, scaffolds and unigenes
Nucleotides length (bp)
Average length (bp)
Total nucleotides length (bp)
To further shorten the remaining gaps, we gathered the paired-end reads with one end mapped on the unique contigs and the other end located in the gap region, and performed local assembly with the sequences on unmapped end to fill in the gaps within the scaffolds. In addition, Phrap was used to reduce the redundancy of scaffolds and extend the lengths of scaffolds. Such sequences without redundancy, containing the least amount of Ns and not being extended on either end, were defined as unigenes. With the steps mentioned above, 23,583 unigenes were finally obtained in this research. Of 23,583 unigenes, 827 unigenes indicated high identities to non-plant sequences and their BLAST results did not contain plant sequences, suggesting that those unigenes (about 3.51%) might represent contaminated sequences from other organisms (such as bacteria, fungi, etc.). After the removal of the contaminated sequences, 22,756 unigenes with a total length of about 11.05 Mb were obtained in this research. The assembled unigenes were submitted to the NCBI Transcriptome Shotgun Assembly (TSA) database, and assigned the accession numbers from JR344291 to JR366936. In addition, the assembled unigenes not conforming to the TSA standards (less than 200 bp in length, more than 10% N’s or containing greater than 14 N’s in a row) were shown in Additional file 1. The N50 and average length of unigenes were 592 and 485 bp, respectively (Table 1). The length of assembled unigenes ranged from 200 to 4,402 bp, and 7,180 unigenes (31.55%) had the length over 500 bp (Table 1). Among the assembled unigenes, 22,564 unigenes (about 99.16%) did not contain gap region, whereas only 192 unigenes (about 0.84%) were filled with Ns. The gap length distribution within the assembled unigenes was shown in Figure1. Xia et al. assembled the latex and leaf transcriptome with similar method used in this research , so the assembled unigenes from these two researches were compared with each other using local BLASTn program of BioEdit . With E-value threshold of 1E-20, the number of unigenes specific to bark, latex and leaf were 5,162 and 27,027, respectively. Comparative analysis indicated that 21,741 unigenes from Xia et al. matched with 17,594 unigenes obtained from us, indicating that some unigenes obtained in our research have multiple hits against the unigenes reported by Xia et al. .
Functional annotation by searching against public databases
Summary of most abundant unigenes in the transcriptome of rubber tree bark
No. of reads
CHK1 checkpoint-like protein
heat shock protein 70
heat shock protein
Functional classification by GO and COG
Functional classification by KEGG
Unigenes of MVA and MEP pathways identified in this research
Accession number of matched genes*
isopentenyl-diphosphate Delta-isomerase (IDI)
acetyl-CoA C-acetyltransferase (AACT)
BAF98276.1 (2), ZP_08629444.1 (1), BAF98277.1 (1), AAL18924.1 (1)
hydroxymethylglutaryl-CoA synthase (HMGS)
hydroxymethylglutaryl-CoA reductase (HMGR)
P29057.1 (1), BAF98280.1 (1)
mevalonate kinase (MVK)
phosphomevalonate kinase (PMK)
BAF98284.1 (1), AAL18926.1 (1)
diphosphomevelonate decarboxylase (MVD)
1-deoxy-D-xylulose 5-phosphate synthase (DXS) 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR)
XP_002533688.1 (2), ABD92702.1 (1), XP_002514364.1 (2), ZP_08629200.1 (1) ABQ53937.1 (1), AAS94121.1 (1), ABD92702.1 (1)
2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (CMS)
4-diphosphocytidyl-2 C-methyl-D-erythritol kinase (CMK)
2-C-methyl-D-erythritol 2,4- cyclodiphosphate synthase (MCS)
4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (HDS)
4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR)
In addition to the genes involved in the metabolic pathways, 2,531 unigenes were divided into the genetic information processing including transcription, translation, folding, sorting and degradation, replication and repair. A total of 928 unigenes were classified into organismal systems containing plant-pathogen interaction, plant circadian rhythm and natural killer cell mediated cytotoxicity, and the gene numbers of three sub-pathways were 761, 125 and 42, respectively. In addition, the categories of cellular process and environmental information processing separately included 388 and 121 unigenes ( Additional file 2). The functional classification of KEGG provided a valuable resource for investigating specific processes, functions and pathways involved in bark transcriptome of rubber tree.
Development and characterization of EST-SSR markers
Summary of EST-SSRs identified in rubber tree transcriptome
Total number of sequences examined
Total size of examined sequences (bp)
Total number of identified EST-SSRs
Number of EST-SSRs containing sequences
Number of sequences containing more than one EST-SSRs
The distribution of EST-SSRs based on the number of repeat units
No. of repeat unit
Illumina paired end sequencing and assembly
Transcriptome sequencing is an effective method to obtain EST sequences that are essential for developing molecular markers and identifying novel genes. In the past decade, the development of various NGS technologies including Roche GS FLX and Solexa/Illumina platforms have made it possible to perform de novo transcriptome sequencing [52, 53]. Among these sequencing methods, Roche GS FLX was widely utilized for de novo transcriptome sequencing in many organisms [25, 43, 54–63]. Compared with Roche GS FLX, the Illumina platform was mainly utilized in the organisms with reference genomes [64–67]. In recent years, an array of novel assembly methods have been developed and made short read assembly to be cost-effective. Therefore, de novo sequencing and assembly of transcriptome or genome have been successfully used for model [68–72] and non-model organisms [24, 32–34, 36–39, 73–75]. Consistent with these reports, the results from this research also suggested that short reads from Illumina sequencing can be effectively assembled and used for gene identification and SSR marker development in non-model organisms. In this study, more than 26 million high-quality reads were used to assemble the transcriptome of rubber tree bark. This large dataset resulted in a relatively high sequencing depth, with an average of 46.33 folds. The assembly result indicated that the mean length of unigenes was 592 bp, which was longer than the results shown in previous studies [24, 33, 43, 55, 56, 58]. By using similar method, Xia et al. (2011) assembled and analyzed the latex and leaf transcriptome of rubber tree. Compared with their studies , the mean and N50 sizes of contigs, scaffolds and unigenes generated in this research were longer with the exception of the N50 sizes of the unigenes. These results suggested that the transcriptome sequencing data from rubber tree bark were effectively assembled, which was further validated by the high proportion of unigenes matched with known proteins and the high PCR success rate of EST-SSR markers developed from the assembled unigenes.
Nevertheless, only about 38.63% reads were assembled into unigenes, which is lesser than the results reported by other research groups [33, 43, 55, 58]. We propose that the high percentage of unassembled reads might have resulted from the following reasons such as relatively short reads generated by Illumina Genome analyzer, relative strict selection of assembly parameters (e.g., the K-mer size), low-abundant transcripts, simple repeat regions, alternative splicing, high heterozygous nature of rubber tree, etc. Although the high percentage of unassembled reads existed in this study, these unassembled reads were still an important resource for rubber tree research. To obtain better assembly results, other sequencing technologies (FLX-454, Sanger or other NGS technologies) should be utilized in combination with Illumina platform.
When all the usable sequencing reads were realigned to the assembled unigenes, an average sequencing depth of 46.33 folds was obtained in this research. However, of the 22,756 unigenes, more than 0.8% unigenes had a coverage depth of less than 1, which was partly due to the drawback to the de Bruijn graph approach used in SOAPdenovo program . In de Bruijn approach, the reads were decomposed into k-mers, and then the sequence assembly was carried out. Therefore, the decomposing process likely causes the loss of information. In a few cases, only partial K-mers from the reads are utilized for the sequence assembly, which results in the assembled sequences not supported by the underlying reads. Moreover, the bubbles with high similarity are likely merged into one contig because they cannot be well distinguished due to the short read length and the lack of reference genome.
Functional annotation of unigenes
For the transcriptome sequencing projects, the number of genes and the level of transcript coverage are usually important issues, but it is very difficult to estimate them due to the lack of a reference genome in this research. Using blast algorithm, we indirectly evaluated the transcriptome coverage breadth by estimating the number of unique genes. A large number of unigenes could be matched with unique known proteins in public databases, which implied that the Illumina sequencing project yielded a substantial fraction of unique genes from rubber tree. With the method reported by previous studies [33, 43, 57], if the number of genes in rubber tree was assumed to be commensurate with that of Arabidopsis (25,000), the annotated unigenes (13,115 unique protein accessions) would likely represent more than 52% of genes in rubber tree genome. The unigene number was less than that reported by Xia et al.  and Triwitayakorn et al.  separately with latex, leaf and shoot apical meristem as sequencing materials, but more than that reported by Chow et al. with latex as sequencing material . In our research, the transcripts mostly expressed in bark were mainly associated with stress/defense response and secondary metabolism, whereas the most highly represented unique transcripts in latex and shoot apical meristem were involved in rubber biosynthesis, stress or defence responses and cyanogenic metabolism, respectively. These results indicated that the nature of abundant transcripts were distinct in different tissues, reflecting the unique transcriptomic signatures of different tissues.
In this study, a large number of unigenes were assigned to a wide range of GO categories and COG classifications (Figures 4 and 5), suggesting that the assembled unigenes represented a wide diversity of transcripts in rubber tree genome. Among three GO categories, cell and binding activity were the most abundant classes in cellular component and molecular function, respectively, which was consistent with the report from Xia et al. . Triwitayakorn et al. also indicated that the majority category fell into binding activity among molecular function terms . As for biological process, metabolic process was the largest group in our and Triwitayakorn’s studies , whereas cellular process in Xia’s work . Among COG classifications, the second and third largest classifications unearthed in our work were separately posttranslational modification, protein turnover, chaperones and transcription, which was different from the report by Xia et al. . In addition, the unigenes number in some COG classifications such as defense mechanisms, extracellular structures, RNA processing and modification, lipid transport and metabolism, translation, ribosomal structure and biogenesis, etc. were obviously different from the results of Xia et al. . These results further confirmed that the bark transcriptome sequencing data unearthed new genes that were not identified by Xia et al., and vice versa. Therefore, assembling and analyzing the data from the transcriptome sequencing of various tissues would obtain more comprehensive and integrated set of transcriptome in rubber tree.
Among the KEGG pathways, the well represented pathways discovered in our study were spliceosome, plant-pathogen interaction, biosynthesis of plant hormones, biosynthesis of phenylpropanoids and ribosome, which was different from the results of Xia et al. . Compared with the transcriptome of latex and leaves, there existed different inner-cell metabolic pathways in the transcriptome of rubber tree bark. Furthermore, lots of unigenes without hits in BLAST analyses likely corresponded to the untranslated regions, short sequences not containing a known domain, non-coding RNAs, or the potential rubber tree-specific genes. Generally speaking, such de novo transcriptome sequencing data can provide sufficient transcriptomic sequence information for identifying novel genes in rubber tree, which also confirm that high-throughput Illumina sequencing is an efficient, inexpensive and reliable tool for transcriptome characterization and gene discovery in non-model species.
EST-SSR marker identification and characterization
It is well-known that EST-SSR marker is very important for the researches such as the assessment of genetic diversity, the development of genetic maps, comparative genomics, marker assisted selection breeding, etc. Only about several hundred EST-SSR markers have been developed until now [25, 29, 30], which limited the application of EST-SSR markers in rubber tree. The transcriptome sequencing provided plenty of sequences for developing numerous EST-SSR markers in rubber tree. In total, 39,257 potential EST-SSRs were identified in 16,208 unigenes. Although the selection criterions for developing EST-SSR markers in this study were different from the previous works, some identical results were obtained. If mono-nucleotide repeats were excluded, di-nucleotide repeats were the most abundant type, followed by tri- nucleotide repeats, which was consistent with previous reports [25, 29, 30]. The most abundant di- and tri-nucleotide motifs were AG/TC and AAG/TTC, respectively. These results were also coincident with previous reports except that the most abundant tri-nucleotide motifs was CTT/GAA in An’s research [25, 29, 30]. Of 110 pair primers randomly selected for PCR validation, 106 produced clear bands. The PCR success rate was higher than the results from Triwitayakorn et al. , An et al.  and Feng et al.  in rubber tree, but similar to Wang et al.  in sweet potato. Therefore, the 39,257 potential EST-SSRs identified in this research will provide a wealth of resource for developing EST-SSRs in rubber tree.
In this work, we reported the transcriptome characterizations of rubber tree bark and provided valuable resources for new genes discovery and EST-SSR markers development, which will certainly accelerate the research progress in molecular biology of rubber tree. To our knowledge, this is the first attempt to assemble and characterize the transcriptome of rubber tree bark using Illumina paired-end sequencing method. Based on the transcriptome assembly, EST-SSRs were predicted and their characterizations were further analyzed. The 39,257 EST-SSRs predicted in this study laid a solid foundation for molecular marker development in rubber tree. These results fully demonstrate that Illumina paired-end sequencing is a fast and cost-effective approach for new genes discovery and molecular markers development in non-model organism, especially those with large genome.
Plant material, DNA and RNA extraction
The RY7-33-97, a high-yielding clone, was planted at the experimental farm of Chinese Academy of Tropical Agricultural Sciences in 1992. During the past 11 years, the plants were tapped once every 4 days for latex harvest, and 1.5% ethephon was applied to stimulate latex yield two days before tapping with once every three tappings. The bark samples were collected from healthy rubber trees, and then washed with diethyl pyrocarbonate treated water to remove the latex, and frozen in liquid nitrogen for RNA extraction. For Illumina sequencing, the total RNA was isolated from the bark tissues according to the method reported by Venkatachalam et al. . RNA quality was detected with a 2100 Bioanalyzer (Agilent Technologies). The beads with oligo(dT) were used to isolate poly(A) mRNA from total RNA (Qiagen GmbH, Hilden, Germany).
To examine the polymorphism of EST-SSR markers, the leaves from thirteen Hevea germplasms, SCATC93-114, LCB1320, RRII118, PB86, RY-7-33-97, AC/F, RO/OP, MT/IT, MT/VB, H. pauciflora Muellet-Argoviensis, H. spruceana Mueller-Argoviensis, H. benthamiana Mueller-Argoviensis and H. nitida Mart var. toxicodendroides, were rinsed in deionized water and stored in −80°C freezer until DNA extraction. Two grams of leaves was ground in liquid nitrogen and genomic DNA was extracted using the CTAB method described by Doyle and Doyle . DNA quantification was detected with a 2100 Bioanalyzer (Agilent Technologies) and gel electrophoresis analysis.
cDNA library construction and sequencing
Illumina sequencing was performed at Beijing Genomics Institute (BGI)-Shenzhen, China according to the manufacturer’s instructions (Illumina, San Diego, CA). Firstly, mRNA with poly(A) tail was isolated from 20 μg total RNA using Sera-mag magnetic oligo (dT) beads (Illumina). To avoid priming bias, the purified mRNA was firstly fragmented into small pieces (100–400 bp) using divalent cations at 94°C for 5 minutes. With random hexamer primers (Illumina), the double-stranded cDNA was synthesized using the SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA). The synthesized cDNA was subjected to end-repair and phosphorylation, and then the repaired cDNA fragments were 3′ adenylated with Klenow Exo- (3′ to 5′ exo minus, Illumina). Illumina paired-end adapters were ligated to the ends of these 3′-adenylated cDNA fragments. To select the proper templates for downstream enrichment, the products of ligation reaction were purified on 2% agarose gel. The cDNA fragments (about 200 bp) were excised from the gel. Fifteen rounds of PCR amplification were carried out to enrich the purified cDNA template using PCR primer PE 1.0 and 2.0 (Illumina) with phusion DNA polymerase. Finally, the cDNA library was constructed with 200 bp insertion fragment. After validating on an Agilent Technologies 2100 Bioanalyzer, the library was sequenced using Illumina HiSeqTM 2000 (Illumina Inc., San Diego, CA, USA), and the workflow was as following: template hybridization, isothermal amplification, linearization, blocking, sequencing primer hybridization, and sequencing on the sequencer for read 1. After completion of the first read, the templates can be regenerated in situ to enable a second read from the opposite end of the fragments. Once the original templates are cleaved and removed, the reverse strands undergo sequencing-by-synthesis.
Data filtering and de novo assembly
Before the transcriptome assembly, we carried out a stringent filtering process of raw sequencing reads. The reads with more than 10% of bases with a quality score of Q < 20, non-coding RNA (such as rRNA, tRNA and miRNA), ambiguous sequences represented as “N” and adaptor contamination were removed; moreover, we also discarded the reads that do not pass the Illumina failed-chastity filter according to the relation “failed-chastity ≤ 1”, with a chastity threshold of 0.6 on the first 25 cycles.
De novo transcriptome assembly was performed by de Bruijn graph and SOAPdenovo with the default settings except K-mer value . The high-quality reads were loaded into the computer, and then de Bruijn graph data structure was used to represent the overlap among the reads. In this step, we firstly broke down all reads into 29 mers which were used as nodes to construct the de Bruijn graph and two reads overlapping 28 bp were connected. To reduce the graph complexity, the tiny repetitive sequences shorter than the read length in the graph were resolved by read paths; the short tips with the lengths less than 2 K (58 bp) and lower frequency than other alternative paths were clipped in the graph; the low-coverage links that appeared only once along with their related edges were also filtered; the detected bubbles were merged into a single path if the sequences of the parallel paths were a single base pair difference or had fewer than four base pairs difference with >90% identity. After these steps, the connections on simplified graph were broke at repeat boundaries, and then the unambiguous fragments produced from the graph were defined as contigs.
After obtaining the contig sequences, we realigned the short reads onto the contigs and utilized the paired-end information to estimate the order and interval distance of two contigs. The repeat contigs with multiple and conflicting connections to the unique contig were masked, and the remaining contigs with compatible connections were assembled with Ns representing unknown nucleotides according to the pair-end linkage and insert size information. Following the above processes, the contigs with compatible connections to each other were constructed into scaffolds. Finally, paired-end information was used to retrieve the read pairs with one read well aligned on the contig and another read located in the gap region, and then a local assembly for the collected reads was carried out to make the scaffolds with least Ns. The scaffolds were further assembled to reduce the redundancy of scaffolds and extend the lengths of scaffolds using Phrap (http://bozeman.mbt.washington.edu/phrap.docs/phrap.html). After the steps mentioned above, the sequences obtained without redundancy, containing the least amount of Ns, and not being extended on either end were defined as unigenes. To evaluate the depth of coverage, all usable reads were realigned to the unigenes using SOAPaligner with the default settings .
Gene annotation and analysis
All unigenes were utilized for homology searches against various protein databases such as NCBI nr (http://www.ncbi.nlm.nih.gov/), Swissprot (http://www.expasy.ch/sprot/), the COG (http://www.ncbi.nlm.nih.gov/cog/), and the KEGG pathway (http://www.genome.jp/kegg/) with BLAST program (E-value < 1E-5), and the best aligning results were selected to annotate the unigenes. If the aligning results from different databases are in conflict with each other, the results from nr database were preferentially selected, followed by Swissprot, KEGG and COG database.
To further annotate the unigenes in this research, the Blast2GO program  was used to get GO annotation according to molecular function, biological process and cellular component ontologies (http://www.geneontology.org/). The unigene sequences were also aligned to the COG database to predict and classify possible functions. Pathway assignments were performed according to KEGG pathway database .
Development and detection of EST-SSR markers
The SSRIT was used to identify microsatellites in the unigenes. In this research, EST-SSRs were considered to contain motifs with two to nine nucleotides in size and a minimum of 3 contiguous repeat units. The frequency of EST-SSR refers to the cDNA sequences length containing one SSR. The primer premier 6.0 was used to design PCR primers. In total, 110 pairs of primers were designed ( Additional file 3) and validated by PCR reactions. The PCR amplification was carried out as follows: PCR mixtures were 94°C for 4 min, followed by 35–40 cycles of 94°C for 30 s, 55-60°C for 30 s and 72°C for 2 min. The final extension was performed at 72°C for 10 min. The PCR products were analyzed by electrophoresis on 8.0% non-denaturing polyacrylamide gels and silver stained . The band sizes were determined against DNA ladder.
This research was supported by the earmarked funds from National Natural Science Foundation of China (30960310), National Program on Key Basic Research Project of China (2012CB723005), Modern Agro-industry Technology Research System (CARS-34-GW1), and Rubber Research Institute of Chinese Academy of Tropical Agricultural Sciences (1630022011024 and RC201204). We thank Beijing Genomics Institute (Shenzhen) for the assistance in raw data processing and bioinformatics analyses. In addition, we thank Dr. Frank M You (Department of Plant Sciences, University of California, USA) for his critical reading and revision of the manuscript.
- Cataldo F: Guayule rubber: a new possible world scenario for the production of natural rubber. Prog Rubber Plastics Technology. 2000, 16: 31-59.Google Scholar
- Cornish K: Similarities and differences in rubber biochemistry among plant species. Phytochemistry. 2001, 57: 1123-1134. 10.1016/S0031-9422(01)00097-8.View ArticlePubMedGoogle Scholar
- Leitch AR, Lim KY, Leitch IJ, O’Neill M, Chye ML, Low FC: Molecular cytogenetic studies in rubber, Hevea brasiliensis Muell. Arg. (Euphorbiaceae). Genome. 1998, 41: 464-467.View ArticleGoogle Scholar
- Venkatachalam P, Kumari JP, Sushmakumari S, Jayashree R, Rekha K, Sobha S, Priya P, Kala RG, Thulaseedharan A: Current Perspectives on Application of Biotechnology to Assist the Genetic Improvement of Rubber Tree (Hevea brasiliensis Muell. Arg.): An Overview. Func Plant Sci Biotechnol. 2007, 1 (1): 1-17.Google Scholar
- Varshney RK, Graner A, Sorrells ME: Genomics-assisted breeding for crop improvement. Trends Plant Sci. 2005, 10: 621-630. 10.1016/j.tplants.2005.10.004.View ArticlePubMedGoogle Scholar
- Varshney RK, Hoisington DA, Tyagi AK: Advances in cereal genomics and applications in crop breeding. Trends Biotechnol. 2006, 24: 490-499. 10.1016/j.tibtech.2006.08.006.View ArticlePubMedGoogle Scholar
- Chen SC, Peng SQ, Huang GX, Wu KX, Fu XH, Chen ZQ: Association of decreased expression of a Myb transcription factor with the TPD (tapping panel dryness) syndrome in Hevea brasiliensis. Plant Mol Biol. 2002, 51: 51-58.View ArticleGoogle Scholar
- Ko JH, Chow KS, Han KH: Transcriptome analysis reveals novel features of the molecular events occurring in the laticifers of Hevea brasiliensis (para rubber tree). Plant Mol Biol. 2003, 53 (4): 479-492.View ArticlePubMedGoogle Scholar
- Chow KS, Wan KL, Isa MNM, Bahari A, Tan SH, Harikrishna K, Yeang HY: Insights into rubber biosynthesis from transcriptome analysis of Hevea brasiliensis latex. J Exp Bot. 2007, 58 (10): 2429-2440. 10.1093/jxb/erm093.View ArticlePubMedGoogle Scholar
- Venkatachalam P, Thulaseedharan A, Raghothama KG: Identification of expression profiles of tapping panel dryness (TPD) associated genes from the latex of rubber tree (Hevea brasiliensis Muell. Arg). Planta. 2007, 226: 499-515. 10.1007/s00425-007-0500-8.View ArticlePubMedGoogle Scholar
- Sando T, Takaoka C, Mukai Y, Yamashita A, Hattori M, Ogasawara N, Fukusaki E, Kobayashi A: Cloning and characterization of mevalonate pathway genes in a natural rubber producing plant, Hevea brasiliensis. Biosci Biotechnol Biochem. 2008, 72 (8): 2049-2060. 10.1271/bbb.80165.View ArticlePubMedGoogle Scholar
- Sando T, Takeno S, Watanabe N, Okumoto H, Kuzuyama T, Yamashita A, Hattori M, Ogasawara N, Fukusaki E, Kobayashi A: Cloning and characterization of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway genes of a natural-rubber producing plant, Hevea brasiliensis. Biosci Biotechnol Biochem. 2008, 72 (11): 2903-2917. 10.1271/bbb.80387.View ArticlePubMedGoogle Scholar
- Tungngoen K, Kongsawadworakul P, Viboonjun U, Katsuhara M, Brunel N, Sakr S, Narangajavana J, Chrestin H: Involvement of HbPIP2;1 and HbTIP1;1 aquaporins in ethylene stimulation of latex yield through regulation of water exchanges between inner liber and latex cells in Hevea brasiliensis. Plant Physiol. 2009, 151 (2): 843-856. 10.1104/pp.109.140228.PubMed CentralView ArticlePubMedGoogle Scholar
- Venkatachalam P, Thulaseedharan A, Raghothama K: Molecular identification and characterization of a gene associated with the onset of tapping panel dryness (TPD) syndrome in rubber tree (Hevea brasiliensis Muell.) by mRNA differential display. Mol Biotechnol. 2009, 41 (1): 42-52. 10.1007/s12033-008-9095-y.View ArticlePubMedGoogle Scholar
- Zhang ZL, Zhu JH, Zhang QQ, Cai YB: Molecular characterization of an ethephon-induced Hsp70 involved in high and low-temperature responses in Hevea brasiliensis. Plant Physiol Biochem. 2009, 47 (10): 954-959. 10.1016/j.plaphy.2009.06.003.View ArticlePubMedGoogle Scholar
- Deng Z, Liu XH, Chen CL, Tian WM, Xia ZH, Li DJ: Molecular cloning and characterization of an actin-depolymerizing factor gene in Hevea brasiliensis. Afr J Biotechnol. 2010, 9 (45): 7603-7610.Google Scholar
- Dusotoit-Coucaud A, Kongsawadworakul P, Maurousset L, Viboonjun U, Brunel N, Pujade-Renaud V, Chrestin H, Sakr S: Ethylene stimulation of latex yield depends on the expression of a sucrose transporter (HbSUT1B) in rubber tree (Hevea brasiliensis). Tree Physiol. 2010, 30 (12): 1586-1598. 10.1093/treephys/tpq088.View ArticlePubMedGoogle Scholar
- Li DJ, Deng Z, Chen CL, Xia ZH, Wu M, He P, Chen SC: Identification and characterization of genes associated with tapping panel dryness from Hevea brasiliensis latex using suppression subtractive hybridization. BMC Plant Biol. 2010, 10: 140-10.1186/1471-2229-10-140.PubMed CentralView ArticlePubMedGoogle Scholar
- Tang C, Huang D, Yang J, Liu S, Sakr S, Li H, Zhou Y, Qin Y: The sucrose transporter HbSUT3 plays an active role in sucrose loading to laticifer and rubber productivity in exploited trees of Hevea brasiliensis (para rubber tree). Plant Cell Environ. 2010, 33 (10): 1708-1720. 10.1111/j.1365-3040.2010.02175.x.View ArticlePubMedGoogle Scholar
- Zhao Y, Zhou LM, Chen YY, Yang SG, Tian WM: MYC genes with differential responses to tapping, mechanical wounding, ethrel and methyl jasmonate in laticifers of rubber tree (Hevea brasiliensis Muell. Arg.). J Plant Physiol. 2011, 168 (14): 1649-1658. 10.1016/j.jplph.2011.02.010.View ArticlePubMedGoogle Scholar
- Jayashree R, Rekha K, Venkatachalam P, Uratsu SL, Dendekar AM, Kumari Jayasree P, Kala RG, Priya P, Sushamakumari S, Sobha S, Asokan MP, Sethuraj MR, Thulaseedharan A: Genetic transformation and regeneration of rubber tree (Hevea brasiliensis Muell. Arg.) transgenic plants with a constitutive version of an anti-oxidative stress superoxide dismutase gene. Plant Cell Reports. 2003, 22: 201-209. 10.1007/s00299-003-0666-x.View ArticlePubMedGoogle Scholar
- Sobha S, Sushamakumari S, Thanseem I, Kumari Jayasree P, Rekha K, Jayashree R, Kala RG, Asokan MP, Sethuraj MR, Dandekar AM, Thulaseedharan A: Genetic transformation of Hevea brasiliensis with the gene coding for superoxide dismutase with FMV 34 S promoter. Curr Sci. 2003, 85: 1767-1773.Google Scholar
- Sobha S, Sushamakumari S, Thanseem I, Rekha K, Jayashree R, Kala RG, Kumari Jayasree P, Asokan MP, Sethuraj MR, Dandekar AM, Thulaseedharan A: Abiotic stress induced over-expression of superoxide dismutase enzyme in transgenic Hevea brasiliensis. Indian J Nat Rubber Res. 2003, 16: 45-52.Google Scholar
- Xia ZH, Xu HM, Zhai JL, Li DJ, He CZ, Huang X: RNA-Seq analysis and de novo transcriptome assembly of Hevea brasiliensis. Plant Mol Biol. 2011, 77: 299-308. 10.1007/s11103-011-9811-z.View ArticlePubMedGoogle Scholar
- Triwitayakorn K, Chatkulkawin P, Kanjanawattanawong S, Sraphet S, Yoocha T, Sangsrakru D, Chanprasert J, Ngamphiw C, Jomchai N, Therawattanasuk K, Tangphatsornruang S: Transcriptome sequencing of Hevea brasiliensis for development of microsatellite markers and construction of a genetic linkage map. DNA Res. 2011, 18: 471-482. 10.1093/dnares/dsr034.PubMed CentralView ArticlePubMedGoogle Scholar
- Low FC, Atan S, Jaafar H, Tan H: Recent advances in the development of molecular markers for Hevea studies. J Nat Rubber Res. 1996, 11: 32-44.Google Scholar
- Varghese YA, Knaak C, Sethuraj MR, Ecke W: Evaluation of random amplified polymorphic DNA (RAPD) markers in Hevea brasilinesis. Plant Breeding. 1997, 116: 47-52. 10.1111/j.1439-0523.1997.tb00973.x.View ArticleGoogle Scholar
- Lespinasse D, Rodier-Goud M, Grivet L, Leconte A, Legnaté H, Seguin M: A saturated genetic linkage map of rubber tree (Hevea spp.) based on RFLP, AFLP, microsatellite and isozyme markers. Theor Appl Genet. 2000, 100: 127-138. 10.1007/s001220050018.View ArticleGoogle Scholar
- An ZW, Zhao YH, Cheng H, Li WG, Huang HS: Development and application of EST-SSR markers in Hevea brasiliensis Muell. Arg Hereditas. 2009, 31 (3): 311-319.PubMedGoogle Scholar
- Feng SP, Li WG, Huang HS, Wang JY, Wu YT: Development, characterization and cross-species/genera transferability of EST-SSR markers for rubber tree (Hevea brasiliensis). Mol Breed. 2009, 23: 85-97. 10.1007/s11032-008-9216-0.View ArticleGoogle Scholar
- Li DJ, Xia ZH, Deng Z, Liu XH, Dong JM, Feng FY: Development and characterization of intron-flanking EST-PCR markers in rubber tree (Hevea brasiliensis Muell. Arg.). Mol Biotechnol. 2012, 51: 148-159. 10.1007/s12033-011-9449-8.View ArticlePubMedGoogle Scholar
- Wang QQ, Liu F, Chen XS, Ma XJ, Zeng HQ, Yang ZM: Transcriptome profiling of early developing cotton fiber by deep-sequencing reveals significantly differential expression of genes in a fuzzless/lintless mutant. Genomics. 2010, 96 (6): 369-376. 10.1016/j.ygeno.2010.08.009.View ArticlePubMedGoogle Scholar
- Wang Z, Fang B, Chen J, Zhang X, Luo Z, Huang L, Chen X, Li Y: De novo assembly and characterization of root transcriptome using Illumina paired-end sequencing and development of cSSR markers in sweet potato (Ipomoea batatas). BMC Genomics. 2010, 11: 726-10.1186/1471-2164-11-726.PubMed CentralView ArticlePubMedGoogle Scholar
- Zenoni S, Ferrarini A, Giacomelli E, Xumerle L, Fasoli M, Malerba G, Bellin D, Pezzotti M, Delledonne M: Characterization of transcriptional complexity during berry development in Vitis vinifera using RNA-Seq. Plant Physiol. 2010, 152: 1787-1795. 10.1104/pp.109.149716.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang G, Guo G, Hu X, Zhang Y, Li Q, Li R, Zhuang R, Lu Z, He Z, Fang X, Chen L, Tian W, Tao Y, Kristiansen K, Zhang X, Li S, Yang H, Wang J, Wang J: Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome. Genome Res. 2010, 20 (5): 646-654. 10.1101/gr.100677.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Garg R, Patel RK, Tyagi AK, Jain M: De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res. 2011, 18 (1): 53-63. 10.1093/dnares/dsq028.PubMed CentralView ArticlePubMedGoogle Scholar
- Iorizzo M, Senalik DA, Grzebelus D, Bowman M, Cavagnaro PF, Matvienko M, Ashrafi H, Van Deynze A, Simon PW: De novo assembly and characterization of the carrot transcriptome reveals novel genes, new markers, and genetic diversity. BMC Genomics. 2011, 12 (1): 389-10.1186/1471-2164-12-389.PubMed CentralView ArticlePubMedGoogle Scholar
- Shi CY, Yang H, Wei CL, Yu O, Zhang ZZ, Jiang CJ, Sun J, Li YY, Chen Q, Xia T, Wan XC: Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds. BMC Genomics. 2011, 12: 131-10.1186/1471-2164-12-131.PubMed CentralView ArticlePubMedGoogle Scholar
- Wong MM, Cannon CH, Wickneswari R: Identification of lignin genes and regulatory sequences involved in secondary cell wall formation in Acacia auriculiformis and Acacia mangium via de novo transcriptome sequencing. BMC Genomics. 2011, 12: 342-10.1186/1471-2164-12-342.PubMed CentralView ArticlePubMedGoogle Scholar
- Li RQ, Zhu HM, Ruan J, Qian W, Fang XD, Shi ZB, Li YR, Li ST, Shan G, Kristiansen K, Li SG, Yang HM, Wang J, Wang J: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010, 20 (2): 265-272. 10.1101/gr.097261.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser. 1999, 41: 95-98.Google Scholar
- Li R, Li Y, Kristiansen K, Wang J: SOAP: short oligonucleotide alignment program. Bioinformatics. 2008, 24: 713-714. 10.1093/bioinformatics/btn025.View ArticlePubMedGoogle Scholar
- Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. BMC Genomics. 2010, 11: 180-10.1186/1471-2164-11-180.PubMed CentralView ArticlePubMedGoogle Scholar
- Mittler R, Vanderauwera S, Gollery M, Breusegem FV: Reactive oxygen gene network of plants. Trends Plant Sci. 2004, 9: 490-498. 10.1016/j.tplants.2004.08.009.View ArticlePubMedGoogle Scholar
- Sung DY, Kaplan F, Guy CL: Plant HSP70 molecular chaperones: protein structure, gene family, expression and function. Plant Physiol. 2001, 113: 443-451. 10.1034/j.1399-3054.2001.1130402.x.View ArticleGoogle Scholar
- Lu SC: S-adenosylmethionine. Int J Biochem Cell Biol. 2000, 32: 391-395. 10.1016/S1357-2725(99)00139-9.View ArticlePubMedGoogle Scholar
- Shirley BW: Flavonoid biosynthesis: “new” functions for an “old” pathway. Trends Plant Sci. 1996, 1: 377-381.Google Scholar
- Li W, Ye YH: Polyubiquitin chains: functions, structures, and mechanisms. Cell Mol Life Sci. 2008, 65 (15): 2397-2406. 10.1007/s00018-008-8090-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Rhind N, Russell P: Chk1 and Cds1: linchpins of the DNA damage and replication checkpoint pathways. J Cell Sci. 2000, 113: 3889-3896.PubMed CentralPubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21: 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, Wang J, Li S, Li R, Bolund L: WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006, 34: W293-W297. 10.1093/nar/gkl031.PubMed CentralView ArticlePubMedGoogle Scholar
- Hudson HE: Sequencing breakthroughs for genomic ecology and evolutionary biology. Mol Ecol Resour. 2008, 8: 3-17. 10.1111/j.1471-8286.2007.02019.x.View ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.PubMed CentralView ArticlePubMedGoogle Scholar
- Alagna F, D’Agostino N, Torchia L, Servili M, Rao R, Pietrella M, Giuliano G, Chiusano ML, Baldoni L, Perrotta G: Comparative 454 pyrosequencing of transcripts from two olive genotypes during fruit development. BMC Genomics. 2009, 10: 399-10.1186/1471-2164-10-399.PubMed CentralView ArticlePubMedGoogle Scholar
- Novaes E, Drost DR, Farmerie WG, Pappas GJ, Grattapaglia D, Sederoff RR, Kirst M: High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics. 2008, 9: 312-10.1186/1471-2164-9-312.PubMed CentralView ArticlePubMedGoogle Scholar
- Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, Marden JH: Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol Ecol. 2008, 17: 1636-1647. 10.1111/j.1365-294X.2008.03666.x.View ArticlePubMedGoogle Scholar
- Barakat A, DiLoreto DS, Zhang Y, Smith C, Baier K, Powell WA, Wheeler N, Sederoff R, Carlson JE: Comparison of the transcriptomes of American chestnut (Castanea dentata) and Chinese chestnut (Castanea mollissima) in response to the chestnut blight infection. BMC Plant Biol. 2009, 9: 51-10.1186/1471-2229-9-51.PubMed CentralView ArticlePubMedGoogle Scholar
- Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics. 2009, 10: 219-10.1186/1471-2164-10-219.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang W, Wang Y, Zhang Q, Qi Y, Guo D: Global characterization of Artemisia annua glandular trichome transcriptome using 454 pyrosequencing. BMC Genomics. 2009, 10: 465-10.1186/1471-2164-10-465.PubMed CentralView ArticlePubMedGoogle Scholar
- Zagrobelny M, Scheibye-Alsing K, Jensen NB, Moller BL, Gorodkin J, Bak S: 454 pyrosequencing based transcriptome analysis of Zygaena filipendulae with focus on genes involved in biosynthesis of cyanogenic glucosides. BMC Genomics. 2009, 10: 574-10.1186/1471-2164-10-574.PubMed CentralView ArticlePubMedGoogle Scholar
- Sun C, Li Y, Wu Q, Luo H, Sun Y, Song J, Lui EM, Chen S: De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis. BMC Genomics. 2010, 11: 262-10.1186/1471-2164-11-262.PubMed CentralView ArticlePubMedGoogle Scholar
- Hou R, Bao ZM, Wang S, Su HL, Li Y, Du HX, Hu JJ, Wang S, Hu XL: Transcriptome sequencing and de novo analysis for Yesso scallop (Patinopecten yessoensis) using 454 GS FLX. PLoS One. 2011, 6: e21560-10.1371/journal.pone.0021560.PubMed CentralView ArticlePubMedGoogle Scholar
- Natarjan P, Parani M: De novo assembly and transcriptome analysis of five major tissues of Jatropha curcas L. using GS FLX titanium platform of 454 pyrosequencing. BMC Genomics. 2011, 12: 191-10.1186/1471-2164-12-191.View ArticleGoogle Scholar
- Wang J, Wang W, Li R, Li Y, Tian G, Goodman L, Fan W, Zhang J, Li J, Guo Y: The diploid genome sequence of an Asian individual. Nature. 2008, 456: 60-65. 10.1038/nature07484.PubMed CentralView ArticlePubMedGoogle Scholar
- Rosenkranz R, Borodina T, Lehrach H, Himmelbauer H: Characterizing the mouse ES cell transcriptome with Illumina sequencing. Genomics. 2008, 92: 187-194. 10.1016/j.ygeno.2008.05.011.View ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628. 10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
- Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M: The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008, 320: 1344-1349. 10.1126/science.1158441.PubMed CentralView ArticlePubMedGoogle Scholar
- Trick M, Long Y, Meng J, Bancroft I: Single nucleotide polymorphism (SNP) discovery in the polyploid Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J. 2009, 7: 334-346. 10.1111/j.1467-7652.2008.00396.x.View ArticlePubMedGoogle Scholar
- Hegedus Z, Zakrzewska A, Agoston VC, Ordas A, Racz P, Mink M, Spaink HP, Meijer AH: Deep sequencing of the zebrafish transcriptome response to mycobacterium infection. Mol Immunol. 2009, 46: 2918-2930. 10.1016/j.molimm.2009.07.002.View ArticlePubMedGoogle Scholar
- Berger MF, Levin JZ, Vijayendran K, Sivachenko A, Adiconis X, Maguire J, Johnson LA, Robinson J, Verhaak RG, Sougnez C: Integrative analysis of the melanoma transcriptome. Genome Res. 2010, 20: 413-427. 10.1101/gr.103697.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Jacob NM, Kantardjieff A, Yusufi FN, Retzel EF, Mulukutla BC, Chuah SH, Yap M, Hu WS: Reaching the depth of the Chinese hamster ovary cell transcriptome. Biotechnol Bioeng. 2010, 105: 1002-1009.PubMedGoogle Scholar
- Wang B, Guo G, Wang C, Lin Y, Wang X, Zhao M, Guo Y, He M, Zhang Y, Pan L: Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucleic Acids Res. 2010, 38: 5075-5087. 10.1093/nar/gkq256.PubMed CentralView ArticlePubMedGoogle Scholar
- Collins LJ, Biggs PJ, Voelckel C, Joly S: An approach to transcriptome analysis of non-model organisms using short-read sequences. Genome Inform. 2008, 21: 3-14.PubMedGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, Huang Q, Cai Q, Li B, Bai Y: The sequence and de novo assembly of the giant panda genome. Nature. 2010, 463: 311-317. 10.1038/nature08696.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang XW, Luan JB, Li JM, Bao YY, Zhang CX, Liu SS: De novo characterization of a whitefly transcriptome and analysis of its gene expression during development. BMC Genomics. 2010, 11: 400-10.1186/1471-2164-11-400.PubMed CentralView ArticlePubMedGoogle Scholar
- Pevzner PA, Tang H, Waterman MS: An Eulerian path approach to DNA fragment assembly. Proc Natl Acad Sci USA. 2001, 98: 9748-9753. 10.1073/pnas.171285098.PubMed CentralView ArticlePubMedGoogle Scholar
- Venkatachalam P, Thanseem I, Thulaseedharan A: A rapid and efficient method for isolation of RNA from bark tissues of Hevea brasiliensis. Curr Sci. 1999, 77: 101-103.Google Scholar
- Doyle JJ, Doyle JL: Isolation of plant DNA from fresh tissue. Focus. 1990, 12: 13-15.Google Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28: 27-30. 10.1093/nar/28.1.27.PubMed CentralView ArticlePubMedGoogle Scholar
- Creste S, Neto AT, Figueira A: Detection of single sequence repeat polymorphism in denaturating polyacrylamide sequencing gels by silver staining. Plant Mol Biol Rep. 2001, 19: 299-306. 10.1007/BF02772828.View ArticleGoogle Scholar
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