# RNA Modification Related Diseases and Sensing Methods
## 5 Detection Methods for RNA Modifications
### Bio Nanopore  
A more comprehensive answer could be provided by nanopore sequencing technologies, such as those developed by Oxford Nanopore Technologies (ONT) [109; 110]. The nanopore sequencing technique measures changes in ionic current that occur as nucleic  
Figure 3: **Types of sequencers and detectable RNA modifications.** Sequencing technologies, RNA modifications reported to be detectable by the technologies, and their structural formulas are shown. The red-colored sites in the nucleotide structure formula indicate the portion added by the modification.  
acids move through protein nanopores encased in the membrane of the flow cell [111]. If this long-read third-generation sequencing technology can directly sequence native RNA and has a modified signal that can be distinguished from that of unmodified nucleotides, it can theoretically detect multiple RNA modifications with a resolution of a single nucleotide and a single molecule [110]. According to a recent groundbreaking study using direct ONT RNA sequencing (Figure 3) [110], the m6A moiety in produced RNA had a different current signal than non-modified moieties at the same site. Shortly after, variations in current intensity were also noticed at the m7G alteration site, which was validated by bacterial direct RNA sequencing of 16S rRNA (Figure 3) [112]. It was later demonstrated in research that base-calling data mismatches, insertions, and deletions were likewise a reflection of variations in electrical current intensity [113]. 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]. Additionally, a current method termed nano-COP demonstrates that nascent RNA can be identified directly using RNA nanopore sequencing, offering a promising foundation for future research on the interplay between splicing and co-occurring RNA modifications [118]. However, the inability to distinguish between various RNA modification signals within nanopore sequencing signals restricts the ability to increase the repertoire of epistranscriptome changes that may be directly examined using RNA nanopore sequencing. Therefore, the identification of all changes in full-length RNA has become necessary [119]. Signals frequently require sophisticated signal processing and analysis software because they often represent five nucleotides going through a pore at once. Machine learning algorithms are necessary in various situations [9; 109; 117]. In theory, a standard solution is to build training datasets for nanopore sequencing using synthetic arrays, including all conceivable dimers that are in vitro transcribed with either unmodified (A, C, G, or U) or modified nucleotides [113]. The basic calling signatures for the m6A, I, 5 mC, m1G, m7G, and Nm (Figure 3) alterations were found using these and similar data sets [120]. This has been useful for predicting de novo RNA modifications [9; 118].