Department of Basic Science, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.
Tencent AI Lab, Tencent, Shenzhen, China.
Bioinformatics. 2019 Jan 1;35(1):77-87. doi: 10.1093/bioinformatics/bty457.
Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies and protocol implementation. A novel method without such limitations is needed for antigenic variant identification.
We developed a Graph-Guided Multi-Task Sparse Learning (GG-MTSL) model that uses multi-sourced serologic data to learn antigenicity-associated mutations and infer antigenic variants. By applying GG-MTSL to influenza H3N2 hemagglutinin sequences, we showed the method enables rapid characterization of antigenic profiles and identification of antigenic variants in real time and on a large scale. Furthermore, sequences can be generated directly by using clinical samples, thus minimizing biases due to culture-adapted mutation during virus isolation.
MATLAB source codes developed for GG-MTSL are available through http://sysbio.cvm.msstate.edu/files/GG-MTSL/.
Supplementary data are available at Bioinformatics online.
流感病毒的抗原变体不断出现并导致疾病爆发。为了选择疫苗株，人们通常使用耗时、昂贵且中通量的基于病毒分离物的血清学方法来鉴定流感抗原变体。然而，由于试剂、供应品和方案实施的变化，这些数据非常嘈杂，难以解释和整合。因此，需要一种没有这些限制的新型方法来鉴定抗原变体。
我们开发了一种基于图引导的多任务稀疏学习（GG-MTSL）模型，该模型使用多源血清学数据来学习与抗原性相关的突变并推断抗原变体。通过将 GG-MTSL 应用于流感 H3N2 血凝素序列，我们表明该方法能够快速表征抗原谱，并实时、大规模地鉴定抗原变体。此外，还可以直接使用临床样本生成序列，从而最大限度地减少由于病毒分离过程中适应培养的突变而导致的偏差。
用于 GG-MTSL 的 MATLAB 源代码可通过 http://sysbio.cvm.msstate.edu/files/GG-MTSL/ 获得。
补充数据可在生物信息学在线获得。
