WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction

Liu, Lian, Song, Bowen, Chen, Kunqi, Zhang, Yuxin, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465, Rigden, Daniel J ORCID: 0000-0002-7565-8937, Lei, Xiujuan and Wei, Zhen (2022) WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction. METHODS, 203. pp. 378-382.

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Official URL: http://dx.doi.org/10.1016/j.ymeth.2021.07.003

Abstract

The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m<sup>6</sup>A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.

Item Type:	Article
Uncontrolled Keywords:	Genomic coordinate, Web server, Epitranscriptome
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	28 Nov 2022 08:39
Last Modified:	18 Jan 2023 19:43
DOI:	10.1016/j.ymeth.2021.07.003
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166292