• Project name: A side-by-side comparison of peptide-delivered antisense antibiotics employing different nucleotide mimics

• Experiments: Chandradhish Ghosh, Linda Popella, Dhamodharan Venugopalan

• Supervision: Lars Barquist, Claudia Höbartner, Jörg Vogel

• Data analysis: Jakob Jung

• Start: September 2022

This project directory contains the analysis of RNA-Seq results obtained after short term Salmonella SL1344 exposure to various ASO-chemistries.

The project is divided in 3 main directories:

• data : contains all raw, intermediate and final data of the project.
• analysis: contains analysis files, such as figures, plots, tables, etc.
• scripts: contains scripts to process and analyze data from the data directory.

Some directories have their own README.md file with information on the respective files.

Here I describe the workflow, which can be followed to reproduce the RNA-Seq results & plots from the article.

For running the whole RNA-seq analysis, one needs following packages/tools/software:

For running the whole analysis, one needs following packages/tools/software:

• BBMap (v38.84) & BBDuk

• R (v.4.1.1) along with packages from Bioconductor/CRAN

• Linux shell (we used Ubuntu 20.04) for commands & bash scripts

• featureCounts (v2.0.1) from Subread package

• bedtools (v2.26.0)

• samtools (v1.12)

All raw FastQ files are located in the folder ./data/fastq and were deposited to the GEO repository under accession number: GSE232819. Details on samples and setup of the experiment can be found in the methods section of the manuscript. Navigate to data/libs to find fastQC quality statistics in HTML format of the trimmed reads.

To run the mapping, run the bash script ./scripts/trimm_map_BB.sh . The script loops through the fastq-files, trims off adapters using BBDuk, maps against the reference genome (reference fasta and gff files can be found in ./data/reference_sequences/) and counts the reads mapped to coding sequences and sRNAs using featureCounts.

Trimming, mapping and counting statistics are stored in the log file ./scripts/my.stdout . The directory ./data/rna_align includes all bam-alignment files as well as the count table ./data/rna_align/counttable.txt . This count table was imported into R for Differential expression analysis.

To run the differential expression analysis, run the R markdown script ./scripts/aso_screen_RNASeq.Rmd . This outputs all figures of the manuscript, which are saved as PDF and/or SVG files to the ./analysis directory. It might take up to 3 minutes to run this script on a low-memory laptop. Some packages have to be pre-installed, such as edgeR, complexHeatmap, etc.