4.6. RNA-Seq Data Analysis

To obtain high quality clean reads, the raw sequencing reads were further filtered by fastp (version 0.18.0) [70]. The parameters were as follows: (1) removing reads containing adapters; (2) removing reads containing more than 10% of unknown nucleotides (N); (3) removing low quality reads containing more than 50% of low quality (Q-value ≤ 20) bases; and (4) removing sequences containing rRNA. The clean reads were aligned to the rice reference genome using HISAT2.2.4 with “-rna-strandness RF” and other parameters set as a default [71].

For each transcription region, a FPKM value was calculated to quantify its expression abundance and variations which then indicate the difference in expression between the two comparisons, using StringTie software [72,73].

Differentially expressed gene (DEG) analysis was performed by DESeq2 [74] software between two different comparison groups. The genes with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change ≥2 (log₂fold change ≥ 1 or log₂fold change ≤ −1) were taken as thresholds to be considered differentially expressed.

GO [75] is an international standardized gene functional classification system which has three ontologies: molecular function, cellular component, and biological process. GO enrichment analysis gives the GO function classification annotation of the gene and screens for all the GO terms that are significantly enriched in DEGs compared to their genomic background, as well as filters the DEGs that correspond to specific biological functions. Firstly, all DEGs were mapped to GO terms in the Gene Ontology database (http://www.geneontology.org/), the number of genes in each term was calculated, and significantly enriched GO terms in DEGs compared to the genomic background were defined by a hypergeometric test. The calculated p-value was done with FDR ≤ 0.05 as a threshold through FDR Correction. Finally, it is concluded that GO terms meeting this condition were defined as significantly enriched compared with the entire genome background.

Genes usually interact with each other to play roles in certain biological functions. Meanwhile, pathway-based analysis helps to further understand the biological functions of genes. KEGG [76] is the major public pathway-related database [77]. Pathway enrichment analysis identifies significantly enriched metabolic pathways or signal transduction pathways in DEGs compared with the whole genome background, and then understand what biological functions are played. The calculated p-value was done through FDR Correction, taking FDR ≤ 0.05 as a threshold. Pathways meeting this condition were defined as significantly enriched.