4.4. Transcript Abundance Estimation and Differential Expression Analysis
The mapped reads of each sample were assembled using StringTie [65]. Then, the transcriptomes from all the samples were merged to reconstruct a comprehensive transcriptome using perl scripts. After the final transcriptome was generated, StringTie and Ballgown [66] were used to estimate the expression levels of the transcripts. StringTie was used to determine expression levels for mRNAs by calculating fragments per kilobase of exon per million reads [67].
A differential expression analysis between the treatments (three biological replicates per treatment) was performed using the DESeq R package. DESeq provides statistical methods for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The differentially expressed mRNAs and genes were determined with log2 (fold change) > 1 or log2 (fold change) ≤ 1 and with statistical significance (p-value < 0.05) using the Ballgown R package.