Comprehensive RNA-seq Data Analysis

I have a set of raw FASTQ files from a personal research project and I want to take them all the way through to biological insight. My primary goal is to identify differentially expressed genes, and I already have both the reference genome and its matching GTF/GFF annotation ready for you.

Here is the pipeline I want to see implemented:

• Initial quality check with FastQC followed by an aggregated MultiQC summary.
• Adapter and low-quality base trimming.
• Alignment to the reference (or transcript-level quantification if you prefer Salmon/kallisto) under a well-documented, reproducible Linux environment.
• Gene-level count matrix generation, then differential expression with DESeq2 in R.
• Exploratory visualisations: PCA, heatmap, and a volcano plot highlighting key DE genes.
• Functional interpretation through GO and KEGG pathway enrichment.

Deliverables must include:
• All processed result files and figure images in publication-ready resolution.
• Tidy tables of counts, normalised expression values, and DESeq2 outputs (padj, log2FC, etc.).
• The exact shell, R, and/or Python scripts or notebooks you ran, with comments.
• A concise, step-by-step report (Markdown, R Markdown, or Jupyter) so I can reproduce every step on my own workstation.

Please use standard RNA-seq tools—the typical stack would be FastQC, Cutadapt/Trim Galore, STAR or HISAT2, Salmon/kallisto, DESeq2, and clusterProfiler—but feel free to suggest sensible alternatives if they improve accuracy or speed. I work comfortably in Linux, so command-line oriented solutions are welcome, and I expect the code to run under a recent Ubuntu or CentOS environment without extensive tweaking.

If this matches your expertise in bioinformatics and you can turn around clear, reproducible results, I’d love to collaborate.