*2.6. RNA-Seq Data Analysis*

The high-throughput sequencing offline data (Raw Data) were filtered to obtain highquality data (Clean Data), and the data were filtered using UW's self-developed filtering software SOAPnuke (v1.4.0. Shenzhen, China) for statistics and trimmomatic (v0.36. Dortmund, Germany) for filtering. The clean reads were compared to the reference genome sequence using HISAT (Hierarchical Indexing for Spliced Alignment of Transcripts, http://www.ccb. jhu.edu/software/hisat, accessed on 1 August 2019). The clean reads were aligned to the genomic sequences using Bowtie2, and then the gene expression levels were calculated for each sample using RSEM (http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html, accessed on 1 August 2019). FPKM (Fragments Per Kilobases per Million reads) was used to express the gene expression levels. We defined genes with more than a two-fold difference and Q-value ≤ 0.001 to be screened as significantly differentially expressed genes (fold change ≥ 2 and adjusted *p* value ≤ 0.001). Based on the annotation results of GO (http://geneontology.org/, accessed on 1 August 2019) and KEGG (http://www.genome. jp/kegg, accessed on 1 August 2019) and the official classification, we functionally classified the differentially expressed genes and performed enrichment analysis using the phyper function in R software, with FDR correction for *p* values. Functions with Q values ≤ 0.05 were considered significantly enriched.
