tetano
Editor, Senior Moderator
J Gen Virol
. 2022 Nov;103(11).
doi: 10.1099/jgv.0.001802.
In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences
Trinita Roy[SUP] 1 [/SUP], Khushal Sharma[SUP] 1 [/SUP], Anjali Dhall[SUP] 1 [/SUP], Sumeet Patiyal[SUP] 1 [/SUP], Gajendra Pal Singh Raghava[SUP] 1 [/SUP]
Affiliations
Abstract
Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.
Keywords: Disease-surveillance; Infectious viral strains; Influenza A virus; Machine learning; Pandemic.
. 2022 Nov;103(11).
doi: 10.1099/jgv.0.001802.
In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences
Trinita Roy[SUP] 1 [/SUP], Khushal Sharma[SUP] 1 [/SUP], Anjali Dhall[SUP] 1 [/SUP], Sumeet Patiyal[SUP] 1 [/SUP], Gajendra Pal Singh Raghava[SUP] 1 [/SUP]
Affiliations
- PMID: 36318663
- DOI: 10.1099/jgv.0.001802
Abstract
Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.
Keywords: Disease-surveillance; Infectious viral strains; Influenza A virus; Machine learning; Pandemic.