Beth Bioinformatics Co., Ltd (BethBio) is a biotech company developing state-of-the-art technologies for genomic sequence analytics and intelligent vaccine design. The company uses bioinformatics, computational biology and AI to provide cutting-edge solutions for new vaccine development, vaccine optimizations, and meaningful genome data interpretation. BethBio is working closely with pharmaceutical companies, genetic-testing industry and health organizations to translate our technologies into positive health impact.

Technology & Service Area
Virus Mutation Prediction
BethBio closely monitors the evolution of important infectious viruses and predicts potential future mutations with high accuracy. Their impact on vaccine effectiveness could also be evaluated real-time in-silico.
Solutions for Vaccine Development
We provide antigen designs and vaccine strain selections that will generate optimized vaccine effectiveness. We also provide other services such as codon deoptimization to support development of new and better vaccines.
Bioinformatics Consulting
BethBio’s team specializes in meaningful interpretation of both human and viral genome data. Let us know what you aim to achieve with your genetic data, and we will provide or develop suitable solutions for you.
Learn more about our technology

Associated research articles published by Beth research team

In silico prediction of influenza vaccine effectiveness by sequence analysis

Cao, L., Lou, J., Zhao, S., Chan, R., Chan, M., & Wu, W. et al. (2021). Vaccine, 39(7), 1030-1034. doi: 10.1016/j.vaccine.2021.01.006

Genetic evolution of Human Enterovirus A71 subgenotype C4 in Shenzhen, China, 1998–2013

He, Y., Zou, L., Chong, M., Men, R., Xu, W., & Yang, H. et al. (2016). Journal Of Infection, 72(6), 731-737. doi: 10.1016/j.jinf.2016.03.014

Predicting the dominant influenza A serotype by quantifying mutation activities

Lou, J., Zhao, S., Cao, L., Chong, M., Chan, R., & Chan, P. et al. (2020). International Journal Of Infectious Diseases, 100, 255-257. doi: 10.1016/j.ijid.2020.08.053

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic

Lou, J., Zheng, H., Zhao, S., Cao, L., Wong, E., & Chen, Z. et al. (2022). Journal Of Infection And Public Health, 15(3), 338-342. doi: 10.1016/j.jiph.2022.01.020

Use of a least absolute shrinkage and selection operator (LASSO) model to selected ion flow tube mass spectrometry (SIFT-MS) analysis of exhaled breath to predict the efficacy of dialysis: a pilot study

Wang, M., Chong, K., Storer, M., Pickering, J., Endre, Z., & Lau, S. et al. (2016). Journal Of Breath Research, 10(4), 046004. doi: 10.1088/1752-7155/10/4/046004

Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin

Wang, M., Lou, J., Cao, L., Zhao, S., Chan, R., & Chan, P. et al. (2021). Journal Of Infection, 83(6), 671-677. doi: 10.1016/j.jinf.2021.09.026

A fast and powerful W-test for pairwise epistasis testing

Wang, M., Sun, R., Guo, J., Weng, H., Lee, J., & Hu, I. et al. (2016). Nucleic Acids Research, gkw866. doi: 10.1093/nar/gkw866

Bacteria pathogens drive host colonic epithelial cell promoter hypermethylation of tumor suppressor genes in colorectal cancer

Xia, X., Wu, W., Wong, S., Liu, D., Kwong, T., & Nakatsu, G. et al. (2020). Microbiome, 8(1). doi: 10.1186/s40168-020-00847-4

Quantifying the importance of the key sites on haemagglutinin in determining the selection advantage of influenza virus: Using A/H3N2 as an example

Zhao, S., Lou, J., Cao, L., Chen, Z., Chan, R., & Chong, M. et al. (2020). Journal Of Infection, 81(3), 452-482. doi: 10.1016/j.jinf.2020.05.066

Quantifying the transmission advantage associated with N501Y substitution of SARS-CoV-2 in the UK: an early data-driven analysis

Zhao, S., Lou, J., Cao, L., Zheng, H., Chong, M., & Chen, Z. et al. (2021). Journal Of Travel Medicine, 28(2). doi: 10.1093/jtm/taab011

Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example

Zhao, S., Lou, J., Cao, L., Zheng, H., Chong, M., & Chen, Z. et al. (2021). Theoretical Biology And Medical Modelling, 18(1). doi: 10.1186/s12976-021-00140-3