Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

Vujaklija, Ivan; Biđin, Siniša; Volarić, Marin; Bakić, Sara; Li, Zhe; Foo, Roger; Liu, Jianjun; Šikić, Mile (2025) Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering. Nucleic Acids Research, 53 (1). ISSN 0305-1048

PDF - Published Version - article Available under License Creative Commons Attribution Non-commercial. Download (2MB)

Abstract

To date, over 40 epigenetic and 300 epitranscriptomic modifications have been identified. However, current short-read sequencing-based experimental methods can detect <10% of these modifications. Integrating long-read sequencing technologies with advanced computational approaches, including statistical analysis and machine learning, offers a promising new frontier to address this challenge. While supervised machine learning methods have achieved some success, their usefulness is restricted to a limited number of well-characterized modifications. Here, we introduce Modena, an innovative unsupervised learning approach utilizing long-read nanopore sequencing capable of detecting a broad range of modifications. Modena outperformed other methods in five out of six benchmark datasets, in some cases by a wide margin, while being equally competitive with the second best method on one dataset. Uniquely, Modena also demonstrates consistent accuracy on a DNA dataset, distinguishing it from other approaches. A key feature of Modena is its use of ‘dynamic thresholding’, an approach based on 1D score-clustering. This methodology differs substantially from the traditional statistics-based ‘hard-thresholds.’ We show that this approach is not limited to Modena but has broader applicability. Specifically, when combined with two existing algorithms, ‘dynamic thresholding’ significantly enhances their performance, resulting in up to a threefold improvement in F1-scores.

Item Type:	Article
Uncontrolled Keywords:	epigenomics; modifications; Nanopore sequencing
Subjects:	NATURAL SCIENCES > Biology
Divisions:	Division of Molecular Biology
Depositing User:	Lorena Palameta
Date Deposited:	24 Apr 2025 11:10
URI:	http://fulir.irb.hr/id/eprint/9734
DOI:	10.1093/nar/gkae1168

Actions (login required)

View Item