Time-series transcriptome sequencing dataset of mouse lung tissues following respiratory syncytial virus infection

This study aims to characterize the dynamic gene expression profiles of mouse lung tissues during the progression of respiratory syncytial virus (RSV) infection. We established a mouse RSV infection model and collected lung tissue samples at multiple time points after infection. Total RNA was extracted from each tissue sample, and paired-end RNA sequencing libraries were constructed. High-throughput transcriptome sequencing was performed on the Illumina sequencing platform. Raw sequencing reads were subjected to quality control and filtering. Clean reads were aligned to the mouse reference genome, followed by transcript assembly and gene expression quantification. Further bioinformatics analyses were conducted, including identification of differentially expressed genes via DESeq2, Gene Ontology (GO) functional enrichment analysis via clusterProfiler, construction of weighted gene co-expression network via WGCNA, and time-series expression pattern soft clustering via the Mfuzz method, to systematically explore key regulatory genes and functional pathways involved in the infection process. This project contains raw sequencing data and corresponding metadata for all samples. The dataset provides publicly reusable fundamental resources for investigating the pathogenesis of RSV and deciphering the transcriptomic characteristics of respiratory viral infection.