Optimizing pooling strategies for the massive next-generation sequencing of viral samples

Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, presenting novel prospects for studying the structure of viral populations and understanding virus evolution and epidemiology. It potentially provides an opportunity to implement large-scale molecular surveillance of viral diseases, which offers more precise estimations of epidemiological parameters, detection of transmissions and studying the structure of transmission networks, prediction of the epidemics progress and development of more effective vaccination strategies. A large-scale molecular surveillance requires sequencing of unprecedentedly large sets of viral samples. Although NGS has recently become less expensive and is expected to further decrease its cost in the future, massive NGS of tens of thousands of samples is still highly cost- and labor-intensive. Therefore it is highly important to develop a framework for identification of viral sequences from large number of samples using the smallest possible number of NGS runs.