RNAseq_workflow

This is a RNAseq workflow.

Dependency

All the dependency and version are based on my current platform. The other version maybe compatible, but were untested.

Basic

• perl v5.26.3
• Python 3.9.16
• R version 4.2.1
  • tidyverse_2.0.0
  • argparser_0.7.1
  • jsonlite_1.8.5
  • ggsci_3.0.0
  • cowplot_1.1.1
  • reshape2_1.4.4
  • rmarkdown_2.22
  • knitr_1.43
  • DT_0.28

Prepare genomic data

• cufflinks-2.2.1
• Another GFF Analysis Toolkit (AGAT) - Version: v1.0.0
• HISAT2 version 2.2.1
• STAR 2.7.3a

Filter raw sequencing data

• fastp 0.23.2
• FastQC v0.11.9

Mapping

• HISAT2 version 2.2.1
• STAR 2.7.3a
• Python 3.9.16
  • RSeQC v5.0.1

Quantitative

• R version 4.2.1
  • Rsubread_2.10.5
  • limma_3.52.4
  • edgeR_3.38.4
  • PCAtools_2.8.0

DEG analysis

• R version 4.2.1
  • DESeq2_1.36.0
  • edgeR_3.38.4
  • ggrepel_0.9.3
  • ggtext_0.1.2 (optional)

Enrichment

• R version 4.2.1
  • clusterProfiler_4.4.4
  • pathview_1.36.1
  • enrichplot_1.16.2

Co-expression

• R version 4.2.1
  • WGCNA_1.72-1

Preparation

Sample information

Prepare 00.data/samples.txt, this is a tab-separated file with four columns (groupName sampleName fq1 fq2), e.g.:

groupA	sampleA1	<path to in1.fq of sampleA1>	<path to in2.fq of sampleA1>
groupA	sampleA2	<path to in1.fq of sampleA2>	<path to in2.fq of sampleA2>
groupB	sampleB1	<path to in1.fq of sampleB1>	<path to in2.fq of sampleB1>
groupB	sampleB2	<path to in1.fq of sampleB2>	<path to in2.fq of sampleN2>

Contrasts for DEG analysis

Prepare 04.DE_analysis/contrasts.txt, this is a tab-separated file with two columns (treatment control), e.g.:

groupA	groupB

Genome and annotation

• The genome file and a gff3 file should be exsited in ./db/ directory.
• For enrichment analysis, a R package <orgdb> for GO enrichment should be installed in db/R_Library, and <organism>.kegg_info.RData should be existed in ./db/.
• functionalAnnotation.txt is a tab-separated file with first column GeneID.

Usage

Set variables

Some variables should be set, which is included in ./script/.conf.

Run RNAseq pipeline

If all the files and variables are prepared, execute run_RNAseq.sh.

cd ./script
nohup sh run_RNAseq.sh &

When the pipeline is finished without error, all result should be generated in corresponding directory.

Generate report

There is a report.Rmd file, open it in Rstudio and click knit, you will get a analysis report in HTML format and a result directory containing all the result file. The gene functional information in ./db/functionalAnnotation.txtwas added to expression file and DEG file. You can packaging this files and delivery to your client.

tar zcvf RNAseq_result.tar.gz result/ report.html image/ libs/

Citation

If you use this pipeline to processing transcriptome sequencing data, please cite:

Wang Pengfei. (2023). laowang1992/RNAseq_workflow: a workflow for processing transcriptome sequencing data (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8354341