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Predicting host tropism of influenza A virus proteins using random forest
Christine LP Eng1, Joo Chuan Tong12 and Tin Wee Tan1*
* Corresponding author: Tin W Tan tinwee@bic.nus.edu.sg
Author Affiliations
1 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
2 Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore
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BMC Medical Genomics 2014, 7(Suppl 3):S1 doi:10.1186/1755-8794-7-S3-S1
The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1755-8794/7/S3/S1
Published: 8 December 2014
? 2014 Eng et al.; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
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Abstract
Background
Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.
Results
In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.
Conclusions
From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.
Keywords:
Influenza; zoonotic; machine learning; random forest; prediction; host range restriction; host tropism; bioinformatics
full article
http://www.biomedcentral.com/1755-8794/7/S3/S1
Christine LP Eng1, Joo Chuan Tong12 and Tin Wee Tan1*
* Corresponding author: Tin W Tan tinwee@bic.nus.edu.sg
Author Affiliations
1 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
2 Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore
For all author emails, please log on.
BMC Medical Genomics 2014, 7(Suppl 3):S1 doi:10.1186/1755-8794-7-S3-S1
The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1755-8794/7/S3/S1
Published: 8 December 2014
? 2014 Eng et al.; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
Formula display:
Abstract
Background
Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.
Results
In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.
Conclusions
From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.
Keywords:
Influenza; zoonotic; machine learning; random forest; prediction; host range restriction; host tropism; bioinformatics
full article
http://www.biomedcentral.com/1755-8794/7/S3/S1