HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction

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 receiv...

Full description

Saved in:
Bibliographic Details
Main Authors: Yin, Rui, Zhou, Xinrui, Rashid, Shamima, Kwoh, Chee Keong
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/10356/146980
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-146980
record_format dspace
spelling sg-ntu-dr.10356-1469802021-03-18T08:21:28Z HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction Yin, Rui Zhou, Xinrui Rashid, Shamima Kwoh, Chee Keong School of Computer Science and Engineering Engineering::Computer science and engineering Influenza Reassortment Estimation 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. Published version 2021-03-18T08:21:28Z 2021-03-18T08:21:28Z 2020 Journal Article 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). https://dx.doi.org/10.1186/s12920-019-0656-7 1755-8794 0000-0002-1403-0396 https://hdl.handle.net/10356/146980 10.1186/s12920-019-0656-7 31973709 2-s2.0-85078287253 1 13 en BMC Medical Genomics © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Influenza
Reassortment Estimation
spellingShingle Engineering::Computer science and engineering
Influenza
Reassortment Estimation
Yin, Rui
Zhou, Xinrui
Rashid, Shamima
Kwoh, Chee Keong
HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Yin, Rui
Zhou, Xinrui
Rashid, Shamima
Kwoh, Chee Keong
format Article
author Yin, Rui
Zhou, Xinrui
Rashid, Shamima
Kwoh, Chee Keong
author_sort Yin, Rui
title HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
title_short HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
title_full HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
title_fullStr HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
title_full_unstemmed HopPER : an adaptive model for probability estimation of influenza reassortment through host prediction
title_sort hopper : an adaptive model for probability estimation of influenza reassortment through host prediction
publishDate 2021
url https://hdl.handle.net/10356/146980
_version_ 1696984367316860928