Transcriptional landscapes analysis through direct RNA sequencing
About Intawat Nookaew
Intawat Nookaew, PhD is an Associate Professor in Department of Biomedical Informatics and Department of Physiology & Biophysics. He has experience in both experimental work and extensive computational analysis in broad domains of life. His research is focused on applied bioinformatics and systems biology. He has developed many robust frameworks and computational analysis packages to handle, analyze and integrate large-scale datasets such as multilevel omics data. He also has extensive experiences in genomics and transcriptomics aerea. He has applied the developed frameworks on several projects in diverse areas of clinical/biomedical and biotechnological research.
Obtaining a complete genome and transcriptional landscapes of eukaryote can be a difficult task, due to a combination of highly repetitive sequences along the chromosomes and short read lengths obtained from second-generation sequencing. With long reads were generated using Oxford Nanopore (ONT), we obtained complete genome through de novo assembly. Furthermore, we generated long reads using direct RNA sequencing with ONT to investigate transcriptional landscapes and quantification. Full-length transcripts were identified through a novel approach of direct RNA-seq. This method provides accurate identification of transcriptional landscapes, including untranslated regions as well as differential gene expression quantification. Direct RNA-seq identified many polyadenylated non-coding RNAs, including rRNAs, telomere-RNA and a long noncoding RNA.