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Title: HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
Authors: Yin, Rui
Zhou, Xinrui
Rashid, Shamima
Kwoh, Chee Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. (2020). HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction. BMC Medical Genomics, 13(1).
Journal: BMC Medical Genomics
Abstract: Background: Influenza reassortment, a mechanism where influenza viruses exchange their RNA segments by co-infecting a single cell, has been implicated in several major pandemics since 19th century. Owing to the significant impact on public health and social stability, great attention has been received on the identification of influenza reassortment. Methods: We proposed a novel computational method named HopPER (Host-prediction-based Probability Estimation of Reassortment), that sturdily estimates reassortment probabilities through host tropism prediction using 147 new features generated from seven physicochemical properties of amino acids. We conducted the experiments on a range of real and synthetic datasets and compared HopPER with several state-of-the-art methods. Results: It is shown that 280 out of 318 candidate reassortants have been successfully identified. Additionally, not only can HopPER be applied to complete genomes but its effectiveness on incomplete genomes is also demonstrated. The analysis of evolutionary success of avian, human and swine viruses generated through reassortment across different years using HopPER further revealed the reassortment history of the influenza viruses. Conclusions: Our study presents a novel method for the prediction of influenza reassortment. We hope this method could facilitate rapid reassortment detection and provide novel insights into the evolutionary patterns of influenza viruses.
ISSN: 1755-8794
DOI: 10.1186/s12920-019-0656-7
Rights: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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