The transcriptomes of A. solani were analysed 3, 4, and 5 dpi, and compared to those at obtained 0 dpi. The raw paired-end reads were trimmed and quality controlled by SeqPrep (https://github.com/jstjohn/SeqPrep ) and Sickle (https://github.com/najoshi/sickle ) set with default parameters. Then clean reads were separately aligned to the A. solani reference genome (GCA_002837235.1, https://www.ncbi.nlm.nih.gov/assembly/GCA_002837235.1 ) in orientation mode using HISAT2 (v2.1.0, http://ccb.jhu.edu/software/hisat2/ ) software [19 (link)–21 (link)]. Due to the lack of annotations in the reference genome, the coding genes were first predicted using MAKER2 (v2.31.9) and then subjected to a BLASTX search against six common functional databases (NR/Swissport/GO/KEGG/EGGNOG/Pfam) and a pathogen-host interaction database (PHI-base; http://www.phi-base.org ) (E value < = 1e−5). Where available, a hit with the lowest E-score among the characterized genes was screened and functionally annotated. Then, the mapped reads of each sample were assembled and counted by StringTie (v1.3.3b, https://ccb.jhu.edu/software/stringtie/ ) using a reference-based approach [22 (link)]. The calculated raw expression value of each gene was normalized according to the fragments per kilobase of transcript per million fragments mapped (FPKM) method.
To identify the differentially expressed genes (DEGs) in the three different comparisons, the DESeq2 package (v1.24.0) in R software was utilized. Essentially, DEGs with an |log2 fold change|> 1 and a Q value ≤ 0.05 were considered to be significantly differentially expressed genes [23 (link)]. To better explore the expression pattern of the DEGs via three comparisons, the total DEGs with similar expression patterns in four multiple samples were clustered via Short Time-series Expression Miner (STEM) software. Profile with P values ≤ 0.05 were considered to be significant. In addition, a functional-enrichment analysis including KEGG enrichment analysis was carried out with KEGG (www.kegg.jp/kegg/kegg1.html ) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do ) databases [24 (link)].
To identify the differentially expressed genes (DEGs) in the three different comparisons, the DESeq2 package (v1.24.0) in R software was utilized. Essentially, DEGs with an |log2 fold change|> 1 and a Q value ≤ 0.05 were considered to be significantly differentially expressed genes [23 (link)]. To better explore the expression pattern of the DEGs via three comparisons, the total DEGs with similar expression patterns in four multiple samples were clustered via Short Time-series Expression Miner (STEM) software. Profile with P values ≤ 0.05 were considered to be significant. In addition, a functional-enrichment analysis including KEGG enrichment analysis was carried out with KEGG (
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