An informatics approach to distinguish RNA modifications in nanopore direct RNA sequencing

Exploring the epitranscriptome by native RNA sequencing

“…These findings triggered several subsequent studies to detect m 6 A and Ψ modifications using base-calling "error signatures'' from nanopore sequencing data (Figure 1B, right panel) (Liu et al 2019;Parker et al 2020;Wongsurawat et al 2018;Viehweger et al 2019;Begik et al 2021;Abebe et al 2022). Furthermore, several studies took advantage of the current signal metrics to detect modified sites by comparison with a paired sample with fewer or no modifications (Figure 1B, middle panel) (Leger et al 2021;Begik et al 2021), using internal unmodified sequences from the same sequencing run (Ramasamy et al 2022) or implementing machine learning approaches to determine the proportion of modified molecules from single samples (Pratanwanich et al 2021;Begik et al 2021).…”

“…Furthermore, several studies took advantage of the current signal metrics to detect modified sites by comparison with a paired sample with fewer or no modifications ( Fig. 1 B, middle panel; Begik et al 2021 ; Leger et al 2021 ), using internal unmodified sequences from the same sequencing run ( Ramasamy et al 2022 ) or implementing machine learning approaches to determine the proportion of modified molecules from single samples ( Begik et al 2021 ; Pratanwanich et al 2021 ).…”

Ushering in the era of tRNA medicines

“…Ramasamy et al . ( 77 ) combined nanopore-based direct RNA sequencing with a chemical probe to identify pseudouridine modifications, keeping other modifications intact for further analysis. Abebe et al .…”

RNA Modification Related Diseases and Sensing Methods

“…These findings led to several subsequent studies [113][114][115] that discovered that m6A and other modifications exhibit an "error signature" of base calling. Additionally, several studies used internal unmodified sequences from the same sequence run to discover single modification sites [9,116], paired samples with few or no changes [9,117], or a machine learning strategy to estimate the percentages of the modified molecule in the sample. With the aid of computational tools, both unmodified and modified reads can be clearly distinguished based on their distinctive current signatures, and even minute variations in the proportion of modified molecules, known as modification chemistries, can be found between various conditions and cell types [9,117].…”