RNA-seq data from mice

The processed data provided in this study consist of two files: gene_expression.xls (expression matrix file) and U_vs_V.gene_DE.xls (differential expression analysis results). The data analysis workflow is as follows: First, quality assessment and trimming of raw sequencing data were performed using FastQC for quality evaluation and Trimmomatic for quality trimming to obtain clean reads. Subsequently, RNA-seq mapping assessment was carried out by aligning the clean reads to the reference genome using HISAT2 to collect mapping statistics. RSeQC was used to analyze redundancy and insert size distribution, while Qualimap was employed to check uniformity and genomic structure distribution. BEDTools was used for gene coverage statistics and sequencing read distribution along chromosomes. For gene structure analysis, StringTie was used to assemble the mapped reads, and GffCompare was used to compare the assembly with known gene models to identify novel transcription regions. ASprofier was used for alternative splicing analysis. For expression level analysis, StringTie combined with known gene models was used to estimate gene expression levels. WGCNA was applied for gene co‑expression network analysis, and multi‑directional statistical exploration including sample comparison was performed based on the expression matrix. Finally, DESeq2 was used for differential expression analysis, and the results were visualized.