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Liu HCorresponding AuthorBegik OAuthorNovoa, EmCorresponding Author

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June 21, 2021
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EpiNano: Detection of m6A RNA Modifications Using Oxford Nanopore Direct RNA Sequencing

Publicated to:Crispr Knock-Ins In Organoids To Track Tumor Cell Subpopulations. 2298 31-52 - 2021-01-01 2298(), DOI: 10.1007/978-1-0716-1374-0_3

Authors: Liu, HL; Begik, O; Novoa, EM

Affiliations

Barcelona Inst Sci & Technol, Ctr Genom Regulat CRG, Barcelona, Spain - Author
Barcelona Institute of Science and Technology (BIST) - Author
Garvan Inst Med Res, Dept Neurosci, Darlinghurst, NSW, Australia - Author
Garvan Institute of Medical Research - Author
Univ Pompeu Fabra UPF, Barcelona, Spain - Author
Universitat Pompeu Fabra Barcelona - Author
UNSW Sydney - Author
UNSW Sydney, St Vincents Clin Sch, Darlinghurst, NSW, Australia - Author
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Abstract

RNA modifications play pivotal roles in the RNA life cycle and RNA fate, and are now appreciated as a major posttranscriptional regulatory layer in the cell. In the last few years, direct RNA nanopore sequencing (dRNA-seq) has emerged as a promising technology that can provide single-molecule resolution maps of RNA modifications in their native RNA context. While native RNA can be successfully sequenced using this technology, the detection of RNA modifications is still challenging. Here, we provide an upgraded version of EpiNano (version 1.2), an algorithm to predict m6A RNA modifications from dRNA-seq datasets. The latest version of EpiNano contains models for predicting m6A RNA modifications in dRNA-seq data that has been base-called with Guppy. Moreover, it can now train models with features extracted from both base-called dRNA-seq FASTQ data and raw FAST5 nanopore outputs. Finally, we describe how EpiNano can be used in stand-alone mode to extract base-calling “error” features and current intensity information from dRNA-seq datasets. In this chapter, we provide step-by-step instructions on how to produce in vitro transcribed constructs to train EpiNano, as well as detailed information on how to use EpiNano to train, test, and predict m6A RNA modifications in dRNA-seq data.

Keywords

base-calling errorsbase-calling “errors”direct rna sequencingin vitro transcriptionn6-methyladenosinenanopore sequencingnative rnanucleosidesoxford nanopore technologiesrna modificationsupport vector machineBase-calling “errors”Direct rna sequencingIn vitro transcriptionMessenger-rnaN6-methyladenosineNanopore sequencingNative rnaOxford nanopore technologiesRna modificationSupport vector machine

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Crispr Knock-Ins In Organoids To Track Tumor Cell Subpopulations, Q4 Agency Scopus (SJR), its regional focus and specialization in Genetics, give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 11.5, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-20, the following number of citations:

  • WoS: 39
  • Scopus: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-20:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 42.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 45 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.5.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Australia.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Liu, Huanle) and Last Author (NOVOA PARDO, EVA MARIA).

the authors responsible for correspondence tasks have been Liu, Huanle and NOVOA PARDO, EVA MARIA.