Characterization of Gene Isoforms Related to Cellulose and Lignin Biosynthesis in Kenaf (Hibiscus cannabinus L.) Mutant.
Jae Il LyuRahul RamekarDong-Gun KimJung Min KimMin-Kyu LeeNguyen Ngoc HungJin-Baek KimJoon-Woo AhnSi-Yong KangIk Young ChoiKyoung-Cheul ParkSoon-Jae KwonPublished in: Plants (Basel, Switzerland) (2020)
Kenaf is a source of fiber and a bioenergy crop that is considered to be a third world crop. Recently, a new kenaf cultivar, "Jangdae," was developed by gamma irradiation. It exhibited distinguishable characteristics such as higher biomass, higher seed yield, and earlier flowering than the wild type. We sequenced and analyzed the transcriptome of apical leaf and stem using Pacific Biosciences single-molecule long-read isoform sequencing platform. De novo assembly yielded 26,822 full-length transcripts with a total length of 59 Mbp. Sequence similarity against protein sequence allowed the functional annotation of 11,370 unigenes. Among them, 10,100 unigenes were assigned gene ontology terms, the majority of which were associated with the metabolic and cellular process. The Kyoto encyclopedia of genes and genomes (KEGG) analysis mapped 8875 of the annotated unigenes to 149 metabolic pathways. We also identified the majority of putative genes involved in cellulose and lignin-biosynthesis. We further evaluated the expression pattern in eight gene families involved in lignin-biosynthesis at different growth stages. In this study, appropriate biotechnological approaches using the information obtained for these putative genes will help to modify the desirable content traits in mutants. The transcriptome data can be used as a reference dataset and provide a resource for molecular genetic studies in kenaf.
Keyphrases
- genome wide
- single molecule
- wild type
- ionic liquid
- dna methylation
- copy number
- genome wide identification
- single cell
- climate change
- gene expression
- cell wall
- rna seq
- living cells
- atomic force microscopy
- poor prognosis
- wastewater treatment
- high throughput
- amino acid
- genome wide analysis
- healthcare
- health information
- deep learning
- machine learning