rnaseq-pipeline/README.md

RNA-seq DESeq2 Pipeline

A sequential Kubernetes Job pipeline for differential expression analysis on yeast RNA-seq data. Each stage runs as a one-shot Job against a shared PVC, in order.

Dataset

• Source: Gierliński et al., ENA accession PRJEB5348
• Reads: 50bp single-end
• Conditions: wild-type (WT: ERR458493–495) vs. snf2 deletion mutant (snf2: ERR458500–502)

Pipeline stages

Order	File	Stage
1	01-pvc.yaml	Shared PersistentVolumeClaim for pipeline data and intermediate files
2	02-job-sra-download.yaml	Downloads raw FASTQ reads from SRA/ENA
2b	02b-job-sra-download-extra.yaml	Downloads the remaining replicate samples
3	03-job-fastqc.yaml	FastQC read quality control
4	04-job-star.yaml	STAR alignment to the reference genome
4b	04b-job-star-extra.yaml	STAR alignment for the remaining replicate samples
5	05-job-featurecounts.yaml	Gene-level count matrix from aligned reads
6	06-job-deseq2.yaml	DESeq2 differential expression analysis (WT vs. snf2)

Results

• STAR alignment: ~85–90% mapping rate across samples
• DESeq2 output visualized (volcano plot, etc.) in a Jupyter R notebook

Running

Namespace: rnaseq. Jobs are sequential — each depends on the previous stage's output landing on the shared PVC, so apply and wait for completion before moving to the next:

kubectl apply -f 01-pvc.yaml
kubectl apply -f 02-job-sra-download.yaml
kubectl get jobs -n rnaseq -w          # wait for Completed before continuing
kubectl apply -f 02b-job-sra-download-extra.yaml
kubectl apply -f 03-job-fastqc.yaml
kubectl apply -f 04-job-star.yaml
kubectl apply -f 04b-job-star-extra.yaml
kubectl apply -f 05-job-featurecounts.yaml
kubectl apply -f 06-job-deseq2.yaml