DOI: 10.5061/dryad.pnvx0k6vb
title: Data from: Sequence-based detection of emerging antigenically novel influenza A viruses

description: The detection of evolutionary transitions in influenza A (H3N2)
 viruses' antigenicity is a major obstacle to effective vaccine design
 and development. In this study, we describe NIAViD, an unsupervised
 machine learning tool, adept at identifying these transitions, using HA1
 sequence and associated physicochemical properties. NIAViD, performed with
 88.9% (95% CI, 56.5%–98.0%) and 72.7% (95% CI,43.4%– 90.3%) sensitivity in
 training and validation respectively, outperforming the uncalibrated null
 model – 33.3% (95% CI,12.1%–64.6%) and does not require the need for
 potentially biased, time-consuming and costly laboratory assays. The
 pivotal role of Boman’s index, indicative of the virus’s cell surface
 binding potential, is underscored, enhancing the precision of detecting
 antigenic transitions. NIAViD's efficacy is not only in identifying
 influenza isolates that belong to novel antigenic clusters, but also in
 pinpointing potential sites driving significant antigenic changes, without
 the reliance on explicit modeling of hemagglutinin inhibition titers. Our
 approach holds immense promise to augment existing surveillance networks,
 offering timely insights for the development of updated, effective
 influenza vaccines. Consequently, NIAViD, in conjunction with other
 resources, could be used to support surveillance efforts and inform the
 development of updated influenza vaccines.
description: # Sequence-based detection of emerging antigenically novel influenza A
 viruses
 [https://doi.org/10.5061/dryad.pnvx0k6vb](https://doi.org/10.5061/dryad.pnvx0k6vb) ####  Description of the data and file structure The data for this study includes dependencies (in R), environments (in R), and scripts (in Python) needed to reproduce the results. Finally, a Python implementation of our NIAViD pipeline and associated physicochemical properties (**sequence_physioproperties_data.csv**) including hydrophobicity, charge, boman, instability, and isoelectric point are provided. #### Sharing/Access information Links to other publicly accessible locations of the data: * [https://doi.org/10.1126/science.1097211](https://doi.org/10.1126/science.1097211) * [https://doi.org/10.1093/bioinformatics/bty457 ](https://doi.org/10.1093/bioinformatics/bty457) Data was derived from the following sources: D. J. Smith, et al., Mapping the Antigenic and Genetic Evolution of Influenza Virus. *Science* 305, 371–376 (2004). Han, et al., Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A (H3N2) virus. *Bioinformatics* 35.1,77-87 (2019). #### License Compatibility Statement All data included in this dataset, including data derived from other sources, is compatible with the CC0 license waiver required by Dryad. Specifically, the data derived from the publications by D. J. Smith et al., titled "Mapping the Antigenic and Genetic Evolution of Influenza Virus" (Science 305, 371–376, 2004) and Han, et al., titled  "Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A (H3N2) virus" (Bioinformatics 35.1, 77-87, 2019), have been verified to be free of any copyright or license restrictions that would prevent redistribution under the CC0 waiver. This ensures that all data in this submission can be freely distributed, remixed, adapted, and built upon by future users without any conditions or restrictions. ## Code/Software The software versions (including loaded packages) used to run files are included in the scripts. Once the curated data is loaded, the NIAViD pipeline can be executed with the Python script (NIAViD Pipeline.py).