Repository for the C.difficiles RNAseq analyses of the Kreis et al. 2024 : "Dual RNA-seq study of the dynamics of coding and non-coding RNAs expression during Clostridioides difficile infection in a mouse model"
Dual-RNAseq data is available under identifier PRJEB64651.
The RNAseq analysis process consists of several parts: quality control (01_initial_qc), cleaning (02_data_preprocessing), mapping (03_mapping_genome), prediction of new candidate ncRNA genes (04_detrprok), counting the reads associated with each gene (05_featureCounts_genome), analysis of differential gene expression (06_DESeq2), and comparisons (08_post_analysis_and_figures) with similar already published analyses (07_Fletcher_and_Pruss_analyses).
References genomes (fasta format) and annotations (gff format) used may be downloaded from:
- Mus musculus genome assembly GRCm39
- Clostridioides difficile 630. The annotation file for this organism was downloaded the 28th February 2023 from the MicroScope platform and given in the
00_initial_datafolder (NC_009089.1_MaGe.gff).
Prediction of new candidate ncRNA genes and comparisons with similar already published analyses concern only C.difficiles organism.
The prediction of new candidate ncRNA genes was done with:
- DETR'PROK v2.1.3
Clusterize v1.0.3andCompare Overlapping v1.0.4of the S-MART tools
For the comparisons with similar published analyses part, the analysis workflow (01-06) was taken over in the form of a snakemake pipeline, as the data were not from a dual-RNAseq and therefore required adaptation.
The publisehd analysis used are:
- Fletcher, J.R., Pike, C.M., Parsons, R.J. et al. Clostridioides difficile exploits toxin-mediated inflammation to alter the host nutritional landscape and exclude competitors from the gut microbiota. Nat Commun 12, 462 (2021). doi: 10.1038/s41467-020-20746-4
- Pruss, K.M., Sonnenburg, J.L. C. difficile exploits a host metabolite produced during toxin-mediated disease. Nature 593, 261–265 (2021). doi: 10.1038/s41586-021-03502-6
The files selection_from_Fletcher_study.txt and selection_from_Pruss_study.txt contain the selected samples (dowloaded from the NCBI) and used for the comparisons (see in 07_Fletcher_and_Pruss_analyses/data_example folder). With the objective to compare sRNAs expression from 3 experiments we select 2 conditions in Fletcher and Pruss experiment and named so on : k stands for our article, p stands for the Pruss experiment, and f stands for the Fletcher experiment. The term "pBase" designs the "Base" condition of the Pruss experiment while "pWT" stands for its "WT" condition. Similarly "fwtd2" and "fwtTY" concern the 2 conditions used from the Fletcher experiment.
Third-party softwares may be accessible with the conda environment files present in the 07_Fletcher_and_Pruss_analyses/conda_env repository (command line example: conda env create -f conda_env/*.yml)
To test snakefile operation on a lightweight example:
1- go to the 07_Fletcher_and_Pruss_analyses folder
2- download of the template_script_DESeq2_CL.r of the the SARTools package in the 07_Fletcher_and_Pruss_analyses folder (needed for the differential analysis step)
3- extract the archive Dual_Seq_Cdiff_Mouse_smk_example.tar.gz that contains the genome (fasta format) and annotation files (gff format) of Clostridioides difficile 630 and the first 10000 reads of R1 and R2 RNAseq data of the Pruss study (WT: SRR12762560 and SRR12762561 ; Base: SRR12766943 and SRR12766946) in the 07_Fletcher_and_Pruss_analyses folder: tar -xvzf Dual_Seq_Cdiff_Mouse_smk_example.tar.gz
4- adapt path of the fQC_bwt2_ftCounts_DEseq2_annot.yml config file to you own environment
5- Under the activated Dual_Seq_Cdiff_Mouse_smk conda environment (conda activate env_Dual_Seq_Cdiff_Mouse_smk) and in the 07_Fletcher_and_Pruss_analyses folder, run : snakemake --snakefile fQC_bwt2_ftCounts_DEseq2_annot.smk --configfile fQC_bwt2_ftCounts_DEseq2_annot.yml --cores 1
6- The functional test is completed if there is no difference between the expected result file and the current result file (excluding columns based on statistical estimation): diff <(cut -f 1-12,21- data_example/expectedResultOfFunctionalTest.txt) <(cut -f 1-12,21- 05_DEG/functional_example/tables/WTvsBase.complete_annot.txt) (07_Fletcher_and_Pruss_analyses folder)
Post-analysis and figures for the three experiments are based on the tables from the differential expression analyses for all genes (complete) for each experiment. Dedicated files and scripts stand in the 08_post_analysis_and_figures directory.
Chi2 test comparisons and heapmaps (with the ComplexHeatmap package) of differentially expressed genes in at least two experiments are performed by the figures_stats_noNA4allStat.R script. The corresponding conda environment may be created with the conda_env/env_R_for_figures_and_stats_Kreis.yml file.
Gene set enrichment analyses (GSEA) were performed using a manual replay (Ma2Html_for_blitzgsea_analysis) of the Ma2HTML (M. Monot personnal communication, export 1652263129) database to define gene sets, the Wald statistic (stat column proposed by SARTools) as gene signatures, and the blitzgsea tool using the blitzgsea_Ma2Html.py script.
The pie charts are based on the differentially expressed genes in the leading_edge lists from the GSEA analysis.