De novo assembly and characterization of bark transcriptome using Illumina sequencing and development of EST-SSR markers in rubber tree (Hevea brasiliensis Muell. Arg.)

Abstract

Background: 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.

Results: 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.

Conclusion: 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.

Figure 1

Gap distribution of assembled scaffolds and unigenes. The gap distribution (%) represents the percentage of the number of N divided by the sequence length of assembled scaffold or unigene.

Figure 2

Assessment of assembled quality. The assembled quality of unigenes was assessed by the distribution of mapped reads within the assembled unigenes.

Figure 3

Characterization of searching the assembled unigenes against Nr and Swissprot protein databases. (A) E-value distribution of BLAST hits for the assembled unigenes with a cutoff of 1E-5 in Nr database. (B) E-value distribution of BLAST hits for the assembled unigenes with a cutoff of 1E-5 in Swissprot database. (C) Similarity distribution of the top BLAST hits for the assembled unigenes with a cutoff of 1E-5 in Nr database. (D) Similarity distribution of the top BLAST hits for the assembled unigenes with a cutoff of 1E-5 in Swissprot database.

Figure 4

Gene Ontology classifications of assembled unigenes. 6,867 unigenes with significant similarity in nr protein databases were assigned to gene ontology classifications.

Figure 5

Histogram presentation of COG classification. All unigenes were aligned to COG database to predict and classify possible functions. Out of 16,520 unigenes with nr hits, 5,559 were assigned to 24 COG classifications.

Figure 6

Frequency distribution of EST-SSRs based on motif types. Within the potential EST-SSRs, a total of 429 motif sequence types were identified. The frequency of main motif types was showed in this figure.