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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study

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Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach

Ivan Vujaklija¹, Siniša Biđin¹, Marin Volarić²

¹Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.

Nucleic Acids Research

|December 10, 2024

Summary

Modena, an unsupervised machine learning method, uses long-read sequencing to detect numerous epigenetic and epitranscriptomic modifications. Its novel dynamic thresholding approach significantly improves detection accuracy across datasets.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Over 40 epigenetic and 300 epitranscriptomic modifications are known.
Current short-read sequencing methods detect less than 10% of these modifications.
Existing supervised machine learning approaches are limited to well-characterized modifications.

Purpose of the Study:

To introduce Modena, an unsupervised learning approach for detecting a broad range of epigenetic and epitranscriptomic modifications.
To leverage long-read nanopore sequencing for enhanced modification detection.
To present dynamic thresholding as a novel computational strategy.

Main Methods:

Utilized long-read nanopore sequencing data.
Developed Modena, an unsupervised machine learning algorithm.
Implemented a dynamic thresholding method based on 1D score-clustering.

Main Results:

Modena outperformed existing methods on five out of six benchmark datasets.
Modena demonstrated consistent accuracy on DNA modification detection.
Dynamic thresholding significantly improved the performance of existing algorithms, tripling F1-scores in some cases.

Conclusions:

Modena offers a powerful new approach for broad-spectrum epigenetic and epitranscriptomic modification detection.
Dynamic thresholding represents a broadly applicable and effective computational strategy for modification analysis.
Long-read sequencing integrated with advanced machine learning holds significant promise for advancing epigenetics and epitranscriptomics research.