Below a brief description with Bulk and scRNAseq workflow adopted in this work.
**Bulk RNAseq** analysis was performed using a standard pipeline that includes the follwing steps:
1. Quality control by [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
2. Trimming of bad quality reads with [TrimGalore](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)<details><summary>Running command</summary>trim_galore --quality 20 --fastqc --length 25 --output_dir {outdir} --paired {input.r1} {inout.r2}</details>
3. Alignment with [STAR](https://github.com/alexdobin/STAR)
<details><summary>Running command</summary>
"STAR " +
"--runThreadN {threads} " +
"--genomeDir {input.genome} " +
"--readFilesIn {params.trim_seq} " +
"--outSAMstrandField intronMotif " +
"--outFileNamePrefix {params.aln_seq_prefix} " +
"--outSAMtype BAM SortedByCoordinate " +
"--outSAMmultNmax 1 " +
"--outFilterMismatchNmax 10 " +
"--outReadsUnmapped Fastx " +
"--readFilesCommand zcat "
</details>
4. Gene expression quantification with [FeatureCounts](https://academic.oup.com/bioinformatics/article/30/7/923/232889)
<details><summary>Running command</summary>
"featureCounts " +
"-a {input.annot} " +
"-o {output.fcount} " +
"-g gene_name " +
"-p -B -C " +
"-s {params.strand} " +
"--minOverlap 10 " +
"-T {threads} " +
"{input.bams} "
</details>
5. DGE analysis with [edgeR]:
We employed a data analysis workflow that relied on edgeR DEGs identification setting custom biological variation coefficients (BCV) to 0.1-0.3 for the different 1vs1 comparisons performed (72h vs 24h – in each batch separately). Counts were normalized using TMM method and differential test was performed with exactTest function provided by the edgeR package (v3.32). Genes with an adjusted p.value (Benjiamini Hochberg FDR method) < 0.05 were considered differentially expressed.
Details are provided in the script: [DGE.R](http://www.bioinfotiget.it/gitlab/custom/zonari_mpbhscexp_2025/zonari_mpbhscexp_2025_bulkrnaseq/-/blob/main/DGE/DGE.R)
6. Dowstream functional Analysis with [ClusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html).
In order to retrieve functional annotation from DE analysis, we performed **O**ver **R**epresentation **A**nalysis and **G**ene **S**et **E**nrichment **A**nalysis by using the functions provided by the package.
**ORA** analysis was performed in particular on Gene Ontology Terms (Biological Process sub-group) retrieved by the function.
**GSEA** analysis was performed against MSigDB gene sets C1 to C6 + Hallmarks.
Details are provided in the script[Fig5F_processing_and_plot.R](http://www.bioinfotiget.it/gitlab/custom/zonari_mpbhscexp_2025/zonari_mpbhscexp_2025_bulkrnaseq/-/blob/main/Fig5F_processing_and_plot.R)
