Comprehensive RNA-seq Data Analysis

Linux, Computer Security, Data Visualization, Data Analysis, Bioinformatics · 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 (Mark