Exploring the transcriptome of non-model oleaginous microalga Dunaliella tertiolecta through high-throughput sequencing and high performance computing

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 122
• Main Authors: Yao, Lina, Tan, Kenneth Wei Min, Tan, Tin Wee, Lee, Yuan Kun
• Format: Journal Article
• Language: English
• Published: London BioMed Central 22.02.2017; BioMed Central Ltd
• Subjects: Algae; Algorithms; Biofuels; Bioinformatics; Biomass energy; Biomedical and Life Sciences; Chlorophyta - genetics; Chlorophyta - growth & development; Chlorophyta - metabolism; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data processing; Databases, Genetic; Down-Regulation; Gene expression; Genetic aspects; Heterogeneity; High-Throughput Nucleotide Sequencing; Life Sciences; Microalgae; Microarrays; Molecular Sequence Annotation; Nitrogen - metabolism; Research Article; RNA sequencing; Sequence analysis (applications); Sequence Analysis, RNA; Software; Transcriptome; Triglycerides - biosynthesis; Up-Regulation; Microalgae; HPC; RNA-Seq; Transcriptome; Dunaliella tertiolecta
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1551-x

Background RNA-Seq technology has received a lot of attention in recent years for microalgal global transcriptomic profiling. It is widely used in transcriptome-wide analysis of gene expression., particularly for microalgal strains with potential as biofuel sources. However, insufficient genomic or transcriptomic information of non-model microalgae has limited the understanding of their regulatory mechanisms and hampered genetic manipulation to enhance biofuel production. As such, an optimal microalgal transcriptomic database construction is a subject of urgent investigation. Results Dunaliella tertiolecta , a non-model oleaginous microalgal species, was sequenced via Illumina MISEQ and HISEQ 4000 in RNA-Seq studies. The high quality high-throughout sequencing data were explored using high performance computing (HPC) in a petascale data center and subjected to de novo assembly and parallelized mpiBLASTX search with multiple species. As a result, a transcriptome database of 17,845 was constructed (~95% completeness). This enlarged database constructed fueled the RNA-Seq data analysis, which was validated by a nitrogen deprivation (ND) study that induces triacylglycerol (TAG) production. Conclusions The new paralleled assembly and annotation method under HPC presented here allows the solution of large-scale data processing problems in acceptable computation time. There is significant increase in the number of transcriptomic data achieved and observable heterogeneity in the performance to identify differentially expressed genes in the ND treatment paradigm. The results provide new insights as to how response to ND treatment in microalgae is regulated. ND analyses highlight the advantages of this database generated in this study that could also serve as a useful resource for future gene manipulation and transcriptome-wide analysis. We thus demonstrate the usefulness of exploring the transcriptome as an informative platform for functional studies and genetic manipulations in similar species.