Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data



Huang, Daiyun, Song, Bowen, Wei, Jingjue, Su, Jionglong, Coenen, Frans ORCID: 0000-0003-1026-6649 and Meng, Jia ORCID: 0000-0003-3455-205X
(2021) Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. In: 29th Intelligent Systems for Molecular Biology and 20th International Society for Computational Biology (ISMB-ISCB) joint conference, England.

[img] Text
huangISMB-ISCB_2021.pdf - Author Accepted Manuscript

Download (590kB) | Preview

Abstract

<h4>Motivation</h4>Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available.<h4>Results</h4>We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution.<h4>Availability implementation</h4>The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: RNA, Sequence Analysis, RNA, Protein Binding, Algorithms, Software, Supervised Machine Learning
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 20 Jul 2021 08:52
Last Modified: 07 Sep 2023 09:56
DOI: 10.1093/bioinformatics/btab278
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3130682