We present a computational method based on genome sequencing that enables rapid selection of an antigenically matched and high-yield influenza vaccine strain directly from clinical samples.
This repository contains five folders
- Training and testing code for all the methods metioned in the paper which includes 10-fold cross validation.
- AntCode folder has all the necessary code for antigenicity analysis in the paper.
- YieCode folder has all the necessary code for yield analysis in the paper.
- GlyCode folder has all the necessary code for glycan binding analysis in the paper.
- Antigenicity model
- Serological data was collected from literatures, HA1 seuqences were collected from public databases (NCBI(https://www.ncbi.nlm.nih.gov/genomes/FLU/), GISAID (https://www.gisaid.org/) and Influenza Research Database (https://www.fludb.org/)) and N-linked glycosylation sites were predicted by NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/services/NetNGlyc/).There are 8 tasks for training purpose.
- 11424 seqeunces were collected from GISAID (https://www.gisaid.org/) with their accession number in fasta file for testing purpose.
- Yield model
- Growth data was collected from our lab experiments (see Table S4) from both cell and egg for training purpose.
- 11424 seqeunces were collected from GISAID (https://www.gisaid.org/) with their accession number in fasta file for testing purpose.
- Glycan Bidning model
- binding data was collected from our lab experiments (see Table S4) for training purpose.
- Prediction model
- The main function for predict antigenic distance and virus yield.
- Matlab enviroment required (version R2023a or under on a Windows system). No extra toolbox requirment.
- Run MAIVeSS 10-fold cross validation model using pre-processed data by Main10fold.m in AntCode/YCode/GlyCode folder (training).
- Run MAIVeSS testing model using pre-processed data by MainTesting.m in AntCode/YCode/GlyCode folder (testing).
- Run Prediction.m in Prediction folder to get predicted antigenic distance and virus yield (Prediction).
Let me know if you have any questions or comments at chenggao@mail.missouri.edu or wanx@missouri.edu