Mouse Anti-Influenza A Virus HA (stem region) monoclonal antibody, clone HA

A large number of human genes associated with viral infections contain single nucleotide polymorphisms (SNPs), which represent a genetic variation caused by the change of a single nucleotide in the DNA sequence. SNPs are located in coding or non-coding genomic regions and can affect gene expression or protein function by different mechanisms. Furthermore, they have been linked to multiple human diseases, highlighting their medical relevance. Therefore, the identification and analysis of this kind of polymorphisms in the human genome has gained high importance in the research community, and an increasing number of studies have been published during the last years. As a consequence of this exhaustive exploration, an association between the presence of some specific SNPs and the susceptibility or severity of many infectious diseases in some risk population groups has been found. In this review, we discuss the relevance of SNPs that are important to understand the pathology derived from influenza A virus (IAV) infections in humans and the susceptibility of some individuals to suffer more severe symptoms. We also discuss the importance of SNPs for IAV vaccine effectiveness.

Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN). As proof of principle, we used IPT-CNN to predict different subtypes of Influenza A virus (IAV). Over 8000 sequences of surface proteins haemagglutinin (HA) and neuraminidase (NA) from different IAV subtypes were used to create polynomial or binary vector datasets. The datasets were then converted into binary images. Analysis of these images enabled the classification of IAV subtypes with 100% accuracy and, compared to non-image-based approaches, within a shorter time frame. The proteome-based IPT-CNN approach described here may be used for analysis and proteome-based classification of other proteins.