Abstract
Adenosine-to-inosine (A-to-I) RNA editing, mediated by the ADAR family of enzymes, is pervasive in metazoans and functions as an important mechanism to diversify the proteome and control gene expression. Over the years, there have been multiple efforts to comprehensively map the editing landscape in different organisms and in different disease states. As inosine (I) is recognized largely as guanosine (G) by cellular machineries including the reverse transcriptase, editing sites can be detected as A-to-G changes during sequencing of complementary DNA (cDNA). However, such an approach is indirect and can be confounded by genomic single nucleotide polymorphisms (SNPs) and DNA mutations. Moreover, past studies rely primarily on the Illumina platform, which generates short sequencing reads that can be challenging to map. Recently, nanopore direct RNA sequencing has emerged as a powerful technology to address the issues. Here, we describe the use of the technology together with deep learning models that we have developed, named Dinopore (Detection of inosine with nanopore sequencing), to interrogate the A-to-I editome of any organism.
Original language | English |
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Title of host publication | ADARs |
Editors | Peter Beal |
Publisher | Academic Press Inc. |
Pages | 187-205 |
Number of pages | 19 |
ISBN (Print) | 9780443315848 |
DOIs | |
Publication status | Published - Jan 2025 |
Externally published | Yes |
Publication series
Name | Methods in Enzymology |
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Volume | 710 |
ISSN (Print) | 0076-6879 |
ISSN (Electronic) | 1557-7988 |
Bibliographical note
Publisher Copyright:© 2025
ASJC Scopus Subject Areas
- Biochemistry
- Molecular Biology
Keywords
- Deep learning
- Direct RNA sequencing
- Inosine
- Nanopore sequencing
- RNA editing
- RNA modifications