Influenza Defective Interference Particles sequencing

The mechanisms and consequences of defective interfering particle(DIP) formation during influenza virus infection remains poorly understood. The developmentof next-generation sequencing (NGS) technologies has made it possibleto identify large numbers of DIP-associated sequences, providing a powerful tool tobetter understand their biological relevance. However, NGS approaches pose numeroustechnical challenges, including the precise identification and mapping of deletionjunctions in the presence of frequent mutation and base-calling errors, and thepotential for numerous experimental and computational artifacts. Here, we detail anIllumina-based sequencing framework and bioinformatics pipeline capable of generatinghighly accurate and reproducible profiles of DIP-associated junction sequences.We use a combination of simulated and experimental control data sets to optimizepipeline performance and demonstrate the absence of significant artifacts. Finally,we use this optimized pipeline to reveal how the patterns of DIP-associated junctionformation differ between different strains and subtypes of influenza A and B virusesand to demonstrate how these data can provide insight into mechanisms of DIP formation.Overall, this work provides a detailed roadmap for high-resolution