RNA-seq expression quantity

The transcriptome analysis process encapsulated by Zhanglab graduate Feng Huadong was used

FastQC and Fastp were utilized for data quality control and preprocessing.   For the first reference-based pipeline, HISAT2 was chosen for alignment, StringTie for transcriptome assembly and quantitative analysis, and the R package DESeq2 for differential gene expression analysis.   The second pipeline employed STAR for alignment, HTSeq-count for quantitative analysis, and edgeR for differential gene expression analysis.   In the reference-free pipeline, Bowtie2 was used for alignment, Trinity for transcriptome assembly, RSEM for quantitative analysis, DESeq2 for differential gene expression analysis, and TransDecoder and Trinotate for open reading frame (ORF) prediction and functional annotation of assembled transcripts.

This study fully isolated the analysis pipelines, addressing issues of server environment redundancy and software installation and configuration, greatly facilitating cross-server transplantation and saving researchers' time.

Our project is publicly available on Docker Hub for open access.   The encapsulated pipeline images can be easily transplanted between servers, allowing even researchers without a bioinformatics background to perform automated transcriptome data analysis according to the user manual, obtaining gene and transcript expression levels, differentially expressed genes, and volcano plots, heatmaps, etc., of differential genes from raw data.   Furthermore, the reference-free pipeline includes ORF prediction and functional annotation for assembled transcripts.