Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158914
Title: RNA structure probing using long-read nanopore sequencing technology
Authors: Aw, Ashley Jong Ghut
Keywords: Science::Biological sciences
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Aw, A. J. G. (2022). RNA structure probing using long-read nanopore sequencing technology. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158914
Abstract: RNA can fold into complex structures that can perform specific biological functions and the ability to characterise the structures of the RNA is the key in understanding its functions. Therefore, various groups have developed several methods that utilizes enzymatic or chemical structure probes and coupling with high-throughput short-read sequencing to obtain the structural information from in-vitro and in-vivo samples. However, high-throughput short-read sequencing has its limitation, where it is difficult to accurately generate the structural information for gene with multiple isoforms and the structural information between reads are lost. Therefore, we developed POREcupine, a method that uses direct RNA sequencing, a high-throughput long-read sequencing technology to detect the modifications caused by NAI-N3, a singlestranded chemical probe. Using PORE-cupine, we showed that shared sequences in different transcript isoforms of the same gene can fold into different structures. We also demonstrate that structural differences between transcript isoforms of the same gene lead to differences in translation efficiency. We also apply PORE-cupine to investigate the structural difference of the RNA of the wild type and Δ382 strain of SARS-CoV2. Similarly, we found that shared sequences in different subgenomic RNA shows structural differences, highlighting the importance of long-read sequencing for obtaining phase information. Lastly, we have shown that besides NAI-N3, PORE-cupine can detect other structure probing compounds, like 1AI and CMCT.
URI: https://hdl.handle.net/10356/158914
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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