RiboMethSeq is an RNAseq-based approach to analyze 2’O-ribose methylation (2’Ome).
rRMSAnalyzer is an R package that provides a set of easy-to-use functions to evaluate 2’Ome levels by computing C-scores from RiboMethSeq read end counts as input.
Available features (version 2):
- C-score computation (using either mean or median for the window of neighboring positions)
- Batch effect adjustment with CombatSeq
- Different visualizations to compare samples or sites
- Include a table of annotated human rRNA sites
- Export computed C-scores into a dataframe
- Semi-automated quality control report
Note We have also developed a dedicated Nextflow pipeline to process the data from sequencing output (fastq files) to useful raw data for rRMSAnalyzer (read end counts).
The latest version of rRMSAnalyzer package can be installed from Github with:
library(devtools)
devtools::install_github("RibosomeCRCL/rRMSAnalyzer")library(rRMSAnalyzer)
ribo <- load_ribodata(
count_path = "/path/to/your/csvfiles/directory/",
metadata = "path/to/metadata.csv",
metadata_key = "filename",
metadata_id = "samplename")
# Compute the c-score using different parameters,
# including calculation of the local coverage using the mean instead of the median
ribo <- compute_cscore(ribo, method = "mean")
# If necessary, adjust any technical biases using ComBat-Seq.
# Here, as an example, we use the "library" column in metadata.
ribo <- adjust_bias(ribo,"library")
# Plot a Principal Component Analysis (PCA) whose colors depend on the "condition" column in metadata
plot_pca(ribo,"condition")The “getting started” is available on our website: https://ribosomecrcl.github.io/rRMSAnalyzer/
A test dataset (ribo_toy) is included in the package.
To report a bug or any suggestion to improve the package, please let us known by opening a new issue on: https://github.com/RibosomeCRCL/rRMSAnalyzer/issues
We would like to thank all our collaborators from Jean-Jacques Diaz Team and the Bioinformatic Platform Gilles Thomas for their advices and suggestions.
This project has been funded by the French Cancer Institute (INCa, PLBIO 2019-138 MARACAS), the SIRIC Program (INCa-DGOS-Inserm_12563 LyRICAN), LabEX program (DEVweCan), the French association Ligue Nationale Contre le Cancer and Synergie Lyon Cancer Foundation.