2.12 | Library preparation, Transcriptome sequencing
and analysis
Accurate detection of RNA integrity and total volume with Agilent 2100
bioanalyzer. NEB general library building using NEBNext® Ultra™ RNA
Library Prep Kit for Illumina® kit and strand-specific library building
using NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina®
kit. After passing the library test, the different libraries are pooled
according to the effective concentration and the target downstream data
volume required for Illumina sequencing. The basic principle of
sequencing is sequencing while synthesizing. The library construction
and Illumina sequencing were conducted at Novogene limited liability
company (Beijing, China).
Clean data were obtained after quality clipping of the raw data and Q20,
Q30, and GC content. All the downstream analyses were based on clean
data with high quality. We selected the genome of Pse. libanoticaas the reference genome. Hisat2 (v2.0.5) as the mapping tool for that
Hisat2 can generate a database of splice junctions based on the gene
model annotation file and thus produce a better mapping result than
other non-splice mapping tools. The mapped reads of each sample were
assembled by StringTie (v1.3.3b) (Pertea et al., 2015) in a
reference-based approach.
FeatureCounts v1.5.0 was used to count the reads numbers mapped to each
gene. And the FPKM values were then mapped back to read counts according
to known gene lengths. Differential expression analysis of two
conditions/groups was performed using the DESeq2 R package (1.20.0). The
resulting P-values were adjusted using the Benjamini and Hochberg’s
approach for controlling the false discovery rate. Genes with an
adjusted P-value <0.05 found by DESeq2 were assigned as
differentially expressed.
Gene Ontology (GO) enrichment analysis of differentially expressed genes
was implemented by the clusterProfiler R package, in which gene length
bias was corrected. GO terms with corrected P-values less than 0.05 were
considered significantly enriched by differential expressed genes. We
used the clusterProfiler R package to test the statistical enrichment of
differential expression genes in KEGG pathways.