rRMSAnalyzer: package to analyze RiboMethSeq data

Abstract

RiboMethSeq is an innovative RNAseq-based approach, which was developed in 2015 to analyze 2’O-ribose methylation (2’Ome) at all sites of ribosomal RNAs (rRNA) at once, in yeast (Birkedal et al. 2014). This approach was then transferred to human using the Illumina technology (Marchand et al. 2016; Erales et al. 2017; Marcel et al. 2021). Briefly, the presence of 2’Ome protects the phosphodiester bond located at the 3’ of the 2’Ome nucleotide from alkaline hydrolysis. Thus, the presence of 2’Ome at the given nucleotide n induces under-representation of RNA fragments starting at the nucleotide n+1 and an over-representation of RNA fragments ending at the nucleotide n-1, allowing the extrapolation of 2’Ome levels at the corresponding nucleotide position n (or C-score) varying from 0 to 1 (Birkedal et al. 2014).

The rRMSAnalyzer package can be used for any kind of RNA and with all organisms. This package provides a set of user-friendly functions to compute C-scores from RiboMethSeq read end counts as input, adjust batch effect with ComBat-Seq, visualize the data and provide a table with the annotated human rRNA sites and their C-scores. Processing of the raw data to obtain read end counts from sequencing data was previously described (Marchand et al. 2016).

The rRMSAnalyzer package can be used for any type of RNA and on all organism. This package provides a set of user-friendly functions to calculate C-scores from RiboMethSeq read end counts as input, perform quality control of the dataset, adjust for potential batch effects, and provide tools to visualize and analyze the data. In addition, a list of potentially methylated human rRNA sites is provided for analyses.

Raw data processing to obtain read-end counts from sequencing data can be performed using our Nextflow pipeline ribomethseq-nf, as already described (Marchand et al. 2016).

Help, bug reports and suggestions

To report a bug or any suggestion to improve the package, please let us known by opening a new issue on: github issue link coming soon!

Acknowledgements

We would like to thank all our collaborators for their advices and suggestions.

Funding

This project has been funded by the French Cancer Institute (INCa, PLBIO 2019-138 MARACAS), the SIRIC Program (INCa-DGOS-Inserm_12563 LyRICAN) and Synergie Lyon Cancer Foundation.

Installation

The latest version of rRMSAnalyzer package can be installed from Github with :

library(devtools)
devtools::install_github("RibosomeCRCL/rRMSAnalyzer")

Usage

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")

RiboClass

RiboClass is the main class of the package that enables the storage of both the data matrices (counts and c-scores) and the associated metadata. It is automatically created when calling load_ribodata (see Loading data).

It is a list containing three main elements, individually described below:

Data: a list of dataframe, containing for each sample the 5’ and/or 3’ read-end counts provided by the user, and the calculated C-score.
Metadata: a dataframe, containing all the information related to the samples that can be provided by the user.
RNA_names: a dataframe, reporting the names of the RNA used in Data.

Some major function parameters (such as the normalization method used for c-score computation) are also kept in the RiboClass object as a reminder.

Loading data

Data to provide

Read-end counts

To use this package, the user must provide at least one csv/tsv file with the 5’, 3’ or 5’/3’ read end counts resulting from RiboMethSeq data per sample. The folder structure containing the csv files is not important, as long as either the directory and its sub-directories contain the necessary csv/tsv files.

The name of the RNA on which the read end counting was performed.
The number of the position on the RNA.
The value of the read end counts at the position.

Here is an example :

RNA	Position on RNA	read end count
18S	123	3746
18S	124	345
18S	125	324
18S	126	789
18S	127	1234

Note 1: it is not necessary to provide a header in the count files, because column index can be used in the function load_ribodata, using count_value, count_rnaid and count_pos parameters.

Note 2: if no metadata is specified, rRMSAnalyzer will try to fetch any csv files in the folder specified in count_path and its subfolders.

Metadata

The expected metadata is either a dataframe already in the R environment or a csv/tsv file.

Two columns are mandatory for the metadata :

filename: name of the csv file on disk containing the read end counts described above. Do not modify it unless the filename has changed on disk.
samplename: rename the samples that will be analyzed and displayed on the plots. This column can be modified, as long as the sample names are unique.

After these two mandatory columns, the user can provide as many columns as needed for the analysis.

Here is an example of metadata for three samples:

filename (mandatory)	samplename (mandatory)	biological condition (optionnal)
sample1.csv	sample 1	condition 1
sample2.csv	sample 2	condition 1
sample3.csv	sample 3	condition 2

sample1.csv

sample 1

condition 1

sample2.csv

sample 2

condition 1

sample3.csv

sample 3

condition 2

Note: if no metadata is provided in load_ribodata, an empty metadata will be created with the “filename” and “samplename” columns pre-filled. The “samplename” column will be identical to “filename”, but can be modified by the user.

Here is an example of auto-generated metadata:

filename	samplename
sample1.csv	sample1.csv
sample2.csv	sample2.csv
sample3.csv	sample3.csv

How to load the data

To load both data and metadata, and store them in a RiboClass, the function load_ribodata is used.

The following example displays a call to load_ribodata, with all parameters shown :

path <- system.file("extdata", package="rRMSAnalyzer")

ribo <- load_ribodata(
                      #data & metadata files path
                      count_path = file.path(path,"miniglioma/"),
                      metadata = file.path(path,"metadata.csv"),
                      # data & metadata files separator
                      count_sep = "\t",
                      metadata_sep = ",",
                      # count data parameters :
                      count_header = FALSE,
                      count_value = 3,
                      count_rnaid = 1,
                      count_pos = 2,
                      # Metadata parameters :
                      metadata_key = "filename",
                      metadata_id = "samplename",
                      # c-score parameters :
                      flanking = 6,
                      method = "median",
                      ncores = 1)
#> ✔ Your data has been successfully loaded!

RNA names

Provided

RNA names are automatically obtained from the data and stored in a generated dataframe inside the RiboClass. It contains two columns :

original_name: original name of each RNA (e.g NR_023363.1).
current_name: current name of each RNA, reflecting any user’s change with rename_rna function (see Rename RNA).

This dataframe is used to keep track of the original name, which often includes the NCBI’s accession ID.

Here is an example:

original_name	current_name
NR_023363.1_5S	5S
NR_046235.3_5.8S	5.8S
NR_046235.3_18S	18S
NR_046235.3_28S	28S

Custom

The user must not modify this dataframe manually. To rename or remove RNA, the user can use rename_rna and remove_rna. The dataframe will be updated accordingly.

C-score calculation

What is a C-score ?

The C-score is a metric, calculated from the RiboMethSeq sequencing data, used to evaluate the level of 2’O-ribose methylation (2’Ome) at a given position in the RNA.

The C-score is calculated by normalizing the end read count with respect to the local environment at each genomic position and directly indicates the RNA 2’Ome level. The C-score ranges from 0 (i.e., no RNA molecules of the sample are 2’Ome at this specific site) to 1 (i.e., all RNA molecules of the sample are 2’Ome at this specific site). A C-score with an intermediate value between 0 to 1 means that the sample contains a mixture of un-2’Ome and 2’Ome RNA molecules.

To obtain a robust estimate of the 2’Ome level, different C-scores can be determined depending on the parameters used to compute the local coverage. In particular, the estimation method and the size of the local coverage to be considered can be changed.

By default, the local coverage is estimated by calculating the median of the end read counts in a flanking region of 6 (i.e., 6 nucleotides downstream the nucleotide n and 6 upstream the nucleotide n, where n is the nucleotid directly following the 2’Ome site of interest). This package provide the ability to change these two parameters either when loading the data or during the analysis.

When using the load_ribodata function, a C-score is automatically calculated for all genomic positions of the RNA. The C-score is computed using either the default parameters of the load_ribodata function or user-defined parameters as follows:

load_ribodata(count.path = "/path/to/csv/",
              metadata = "/path/to/metadata.csv",
              # everything below is linked to c-score computation
              flanking = 6, # flanking region size
              method = "median", # use mean or median on flanking region's values
              ncores = 8 # number of CPU cores to use for computation
              )

C-score computation during the analysis

During the analysis, the C-score calculating parameters (method and size of the flanking region) can be modified using the compute_cscore function, which will automatically update the C-score in the RiboClass.

In the following example, both the flanking region’s size of the local coverage and the computation method have been modified:

ribo <- compute_cscore(ribo,
                       flanking = 4,
                       method = "mean")

Important: this function will override the previous c-score of the RiboClass.

Quality control

Due to technical limitations, it is sometimes necessary to conduct wet-lab preparations of large cohorts in several batches. The main risk when making several batches is to introduce technical biases or batch effect in the dataset.

Quality control report

A quality control (QC) report can be performed. It uses several metrics to help identify outlier samples and/or batch effects, including end read counts and the C-score itself at all the 7217 genomic positions. The QC verifies that the coverage is uniform and reproducible between samples, eliminating the possibility of bias due to sequencing and outliers

QC can be either performed using a panel of ready to use functions, which correspond to data visualization, or automatically.

The automatic QC report can be generated using the report_qc function:

With the ribo_toy example, the column that contains the information about samples batches is called “run”. The name is thus given to the library_col parameter.

report_qc(ribo = ribo_toy, library_col = "run")

The QC report includes the following visualizations:

End read counts distribution by sample (boxplot_count)
Relative log coverage (i.e., end read count) by sample (plot_rle)
RNA fraction of end read counts per sample (plot_count_fraction)
Heatmap summarizing the correlation matrix of the end read counts (plot_heatmap_corr)
Correspondence analysis of the end read counts (plot_coa)
Principal component analysis (plot_pca)

Batch effect identification

Technical bias (i.e., batch effect) can be identified by plotting C-scores at all the genomic positions of the RNA for each sample on a PCA (see also [Visualization with PCA] for more uses).

Here is an example:

plot_pca(ribo = ribo,
         color_col = "run")

In this example, the technical replicates RNA1 and RNA2 included in library 1 and 2 respectively, are distant from each other on the PC1 axis. Moreover, the samples should not be grouped by library or batch. The following section will resolve this batch effect.

Batch effect adjustment

Batch effect of RiboMethSeq data can be adjusted using the ComBat-seq method (Paraqindes et al, in revision; Zhang, et al, 2020). The rRMSAnalyzer package includes a wrapper (adjust_bias) to perform ComBat-seq adjustment which provides a new RiboClass with adjusted read end count values and C-scores automatically recalculated with the same setup parameters.

ribo_adjusted <- adjust_bias(ribo, batch = "run")
#> Found 2 batches
#> Using null model in ComBat-seq.
#> Adjusting for 0 covariate(s) or covariate level(s)
#> Estimating dispersions
#> Fitting the GLM model
#> Shrinkage off - using GLM estimates for parameters
#> Adjusting the data
#> Recomputing c-score with the following parameters :
#> - C-score method : median
#> - Flanking window : 6

Batch effect adjustment can be verified using the plot_pca function on the new RiboClass:

plot_pca(ribo_adjusted,"run")

After batch effect adjustment using ComBat-seq method, the two technical replicates RNA1 and RNA2 show reduced dispersion, and the samples are separated on the PCA axes independently of the library they belong to.

Verifying local coverage

The C-score depends on the local coverage around the site. For a simple visualization of the read end count coverage of the flanking region of a given genomic position of interest, use the plot_counts_env function. Two plotting methods are available:

display all samples (default parameters): by displaying the end read count coverage of all samples using a boxplot at the +6/-6 genomic position relative to the genomic position of interest (green box). Such a plot is automatically used when number of samples in up to x. The median of the read end count is shown as a dashed red line.

Here is an example:

data("ribo_toy")
plot_counts_env(ribo_adjusted,"NR_046235.3_5.8S",14)

sample of interest: by displaying the end read count coverage of the sample of interest only using the profile at the +6/-6 genomic position relative to the genomic position of interest (vertical green line). The median of the read end count is displayed as a horizontal dotted red line.

Here is an example:

plot_counts_env(ribo_adjusted,"NR_046235.3_5.8S",14,c("S1","S2"))

Sample manipulation

Keep or remove samples

A sample subset can be easily analyzed by specifying which samples to keep or which to remove. The user can then create a new RiboClass object containing the data and metadata of the samples of interest. In both cases, only the metadata of the remaining samples are kept in the RiboClass object, so no manual updating is required.

Here is an example of how to create a new RiboClass by retaining two samples of interest (“S1” and “S2”):

ribo_2samples <- keep_ribo_samples(ribo_adjusted,c("S1","S2"))
print(ribo_2samples)
#> a RiboClass with 2 samples and 4 RNA(s) :
#> Name : NR_023363.1_5S, length : 121
#>  Name : NR_046235.3_5.8S, length : 157
#>  Name : NR_046235.3_18S, length : 1869
#>  Name : NR_046235.3_28S, length : 5070

Here is an example to generate a new RiboClass by removing two samples (“S1” and “S2”):

ribo_removed_samples <- remove_ribo_samples(ribo,c("S1","S1"))
print(ribo_removed_samples)
#> a RiboClass with 13 samples and 4 RNA(s) :
#> Name : NR_023363.1_5S, length : 121
#>  Name : NR_046235.3_5.8S, length : 157
#>  Name : NR_046235.3_18S, length : 1869
#>  Name : NR_046235.3_28S, length : 5070

In both cases, only the remaining samples’ metadata are kept in the RiboClass object. There is no need to update it manually.

RNA manipulation and annotation

RNA manipulation

Remove RNA

A subset of RNA can be easily analyzed by specifying the RNA to be removed. The user can thus create a new RiboClass object containing the data of the RNAs of interest, without affecting the metadata of the samples.

Here is an example where the RNA 5S is removed:

ribo_adjusted <- remove_rna(ribo, rna_to_remove = "NR_023363.1_5S")
print(ribo_adjusted)
#> a RiboClass with 14 samples and 3 RNA(s) :
#> Name : NR_046235.3_5.8S, length : 157
#>  Name : NR_046235.3_18S, length : 1869
#>  Name : NR_046235.3_28S, length : 5070

Rename RNA

The annotation of rRNA 2’Ome sites using the lists provided by this package requires the use of specific RNA names.

Here is an example to check if the RNA names provided by the user in the RiboClass match the ones used by this package :

data("human_methylated")
cat("human_methylated's rna names: ", unique(human_methylated$rRNA),"\n")
#> human_methylated's rna names:  5.8S 18S 28S
cat("ribo's rna names: ", as.character(ribo_adjusted$rna_names$current_name))
#> ribo's rna names:  NR_046235.3_5.8S NR_046235.3_18S NR_046235.3_28S

In this example, the names are different and need to be updated before annotation.

The rename_rna function automatically updates the rRNA names given by the rRNA size order:

ribo_adjusted <- rename_rna(ribo_adjusted,
                            new_names = c("5.8S", "18S", "28S")) 
                            # from the shortest RNA in our RiboClass to the longest.

Annotation of RNA 2’Ome sites

The rRMSAnalyzer package calculates a C-score for each genomic position of the RNAs. However, not every sites of the reference 2’Ome RNA is necessarily methylated. Therefore, it is expected that the user provides a list of potentially methylated sites of interest, called “annotated sites”. This will make it easier to restrict further analysis to this list of sites.

Included annotation : Human 2’Ome rRNA sites

By default, rRMSAnalyzer package includes two dataframes containing the positions and the annotations of the human rRNA 2’Ome sites:

human_methylated: a dataframe, containing the 112 known 2’Ome sites for the human rRNAs.
human_suspected: a dataframe, containing the 17 sites that are putative 2’Ome sites for the human rRNAs, as described in the litterature.

Customize 2’Ome sites annotations

Instead of using the list of human rRNA 2’Ome sites provided by the rRMSAnalyzer package, the user can provide their own list of annotated sites which will be attached to the RiboClass object using the annotate_site function (see 10.3 section : annotation of RNA sites).

This annotate_site function expects an “annot” parameter which must be a dataframe object that contains the following three mandatory columns :

RNA name: the name of the RNA, matching the RNA name of the RiboClass.
Position on RNA: the number of the position on the RNA.
Nomenclature: the name given to the site of interest.

You can see an example below :

Position	rRNA	Nomenclature
15	5.8S	Um14
76	5.8S	Gm75
28	18S	Am27

Annotate RNA sites

The 2’Ome sites of interest must be attached to the RiboClass object for further analysis using the annotate function with either the provided annotations or custom annotations.

Here is an example using the included human methylated annotations:

ribo_adjusted <- annotate_site(ribo_adjusted,
                               annot = human_methylated,
                               anno_rna = "rRNA",
                               anno_pos = "Position",
                               anno_value = "Nomenclature")

Note : rRMSAnalyzer will display a warning message when there is a mismatch between the annot and RiboClass RNA names.

This vignette also provides some explanations on how to create your own sites annotation dataset with Customize 2’Ome sites annotations.

Analytic functions for 2’Ome profiling

To determine whether RNA 2’Ome profiles are different between conditions and identify the most variable RNA 2’Ome sites, several functions have been implemented to obtain a ggplot. Moreover, the parameter only_annotated, which is included in all the plot related functions, enables the plotting of only the annotated RNA 2’Ome sites of interest (i.e., with biological relevance) when set to true. Here is a list of the implemented plots with the function name:

principal component analysis using the C-scores of the annotated RNA 2’Ome sites (plot_pca)
heatmap using the C-scores of the annotated RNA 2’Ome sites (plot_heatmap)
boxplot using the C-scores of the annotated RNA 2’Ome sites in ascending order of variability (boxplot_count)

As an example, the plot_pca function is presented below.

Here is an example comparing samples reflecting different biological conditions based on the rRNA 2’Ome profile of the provided human_methylated list:

plot_pca(ribo_adjusted,
         color_col = "condition",
         only_annotated = TRUE)

Note 1 : by default, PC1 and PC2 axes are plotted. However, the user can choose the PCA axes of interest using the “axes” parameter.

plot_pca(ribo_adjusted,
         color_col = "condition",
         axes = c(2,3), #PC2 and PC3 will be plotted
         only_annotated = TRUE)

Note 2 the function returns the complete dudi.pca object instead of the plot by setting object_only to TRUE:

pca <- plot_pca(ribo_adjusted,
         color_col = "condition",
         only_annotated = TRUE,
         object_only = TRUE)

Analytic functions for site-by-site comparison of 2’Ome levels

Two cut-offs are currently used to identify RNA sites with significantly different 2’Ome levelsè between biological conditions (Marcel et al , NAR Cancer 2021):

statistical significance: a p-value < 0.01 using Kruskal-Wallis test.
biological significance: a ΔC-score (i.e., Mean C-score_max – Mean C-score_min) > 0.05, indicating a variation of 5% of the RNA 2’Ome level between the conditions.

The plot_diff_sites function displays only the RNA sites that meet these two cut-off criteria. It produces a boxplot visualizing the median C-scores per biological condition and the p-value.

Here is an example:

plot_diff_sites(ribo_adjusted,factor_column = "condition")
#> 0 significant sites found !

#Because no RNA site was found to fulfill the default cut-offs, the p-value cut-off is changed for the following example
plot_diff_sites(ribo_adjusted,factor_column = "condition",p_cutoff = 0.1)
#> 7 significant sites found !

Exporting data

Data can be exported as two different objects.

Export as a dataframe

The user can export data as a dataframe using the extract_data function.

By default, C-scores for all the genomic RNA positions are exported.

ribo_df <- extract_data(ribo_adjusted,
                        col = "cscore")

The user can export data related to the subset of annotated RNA 2’Ome sites by setting the only_annotated parameter to TRUE.

ribo_df <- extract_data(ribo_adjusted,
                        col = "cscore",
                        only_annotated = TRUE)

Excerpt from the output dataframe, where S1, S2 and S3 are samples:
site	S1	S2	S3
18S_Am27	0.9783333	0.9745514	0.9810412
18S_Am99	0.9680204	0.9646470	0.9724150
18S_Um116	0.9276274	0.9470968	0.9407083
18S_Um121	0.9630216	0.9684459	0.9691576
18S_Am159	0.9629730	0.9620986	0.9686766
18S_Am166	0.9809492	0.9744627	0.9744998
18S_Um172	0.9510189	0.9400922	0.9517981
18S_Cm174	0.9119119	0.8668456	0.8806886
18S_Um354	0.9709273	0.9722054	0.9756174
18S_Um428	0.9268668	0.9276018	0.9504224

Export as a ggplot-ready dataframe

The user can export data as a ggplot-ready dataframe using the format_to_plot function.

By default, C-scores for all the genomic RNA positions are exported. The user can export additional information contained in the metadata by specifying the name of the column of interest. The user can export information related to the subset of annotated RNA 2’Ome sites by setting the only_annotated parameter to True.

Here is an example of ggplot-ready dataframe containing the C-scores of all the genomic RNA positions as well as the condition related to the particular sample of interest:

ggplot_table <- format_to_plot(ribo_adjusted,"condition")

Excerpt from the output ggplot-ready dataframe
	sample	site	cscore	condition
501	RNA1	18S_0501	0.0000000	RNA ref
502	RNA1	18S_0502	0.2601804	RNA ref
503	RNA1	18S_0503	0.0000000	RNA ref
504	RNA1	18S_0504	0.3179712	RNA ref
505	RNA1	18S_0505	0.5132894	RNA ref
506	RNA1	18S_0506	0.6354548	RNA ref
507	RNA1	18S_0507	0.0000000	RNA ref
508	RNA1	18S_0508	0.0000000	RNA ref
509	RNA1	18S_0509	0.0000000	RNA ref
510	RNA1	18S_0510	0.9380812	RNA ref

Export as a dataframe by condition

The mean_samples_by_condition function provdes the summarized values per modality of a variable in the metadata dataframe.

By default, the function provides for all the genomic RNA positions the name of the position, the mean and standard deviation (sd) of the C-scores. By using the value=”count” parameter, it is als possible to calculate these values from the read end counts. Calculations can be limited to only annotated RNA 2’Ome sites by setting the only_annotated parameter to TRUE.

Here is an example of dataframe showing the summarized C-scores per modality of the “condition” variable for all the genomic RNA positions:

mean_tb <- mean_samples_by_conditon(ribo_adjusted,
                                    value = "cscore",
                                    metadata_condition = "condition",
                                    only_annotated = TRUE)

Excerpt from the output dataframe by condition
site	condition	mean	sd
18S_Am1031	RNA ref	0.9749922	0.0047461
18S_Am1031	cond1	0.9703699	0.0032725
18S_Am1031	cond2	0.9723714	0.0057723
18S_Am1383	RNA ref	0.9796876	0.0035939
18S_Am1383	cond1	0.9765212	0.0027083
18S_Am1383	cond2	0.9779684	0.0028759
18S_Am159	RNA ref	0.9684126	0.0022025
18S_Am159	cond1	0.9609920	0.0054882
18S_Am159	cond2	0.9623362	0.0065239
18S_Am166	RNA ref	0.9826490	0.0033169

Sessioninfo

sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] rRMSAnalyzer_2.0.1
#> 
#> loaded via a namespace (and not attached):
#>   [1] ade4_1.7-22             tidyselect_1.2.0        dplyr_1.1.2            
#>   [4] farver_2.1.1            blob_1.2.4              bitops_1.0-7           
#>   [7] Biostrings_2.68.1       RCurl_1.98-1.12         fastmap_1.1.1          
#>  [10] XML_3.99-0.14           digest_0.6.31           factoextra_1.0.7       
#>  [13] lifecycle_1.0.3         survival_3.5-5          KEGGREST_1.40.0        
#>  [16] RSQLite_2.3.1           magrittr_2.0.3          genefilter_1.82.1      
#>  [19] compiler_4.3.1          rlang_1.1.1             sass_0.4.6             
#>  [22] tools_4.3.1             utf8_1.2.3              yaml_2.3.7             
#>  [25] knitr_1.43              ggsignif_0.6.4          labeling_0.4.2         
#>  [28] bit_4.0.5               abind_1.4-5             BiocParallel_1.34.2    
#>  [31] withr_2.5.0             purrr_1.0.1             BiocGenerics_0.46.0    
#>  [34] desc_1.4.2              grid_4.3.1              stats4_4.3.1           
#>  [37] fansi_1.0.4             ggpubr_0.6.0            xtable_1.8-4           
#>  [40] colorspace_2.1-0        edgeR_3.42.4            ggplot2_3.4.2          
#>  [43] scales_1.2.1            MASS_7.3-60             cli_3.6.1              
#>  [46] crayon_1.5.2            rmarkdown_2.22          ragg_1.2.5             
#>  [49] generics_0.1.3          httr_1.4.6              DBI_1.1.3              
#>  [52] cachem_1.0.8            stringr_1.5.0           splines_4.3.1          
#>  [55] zlibbioc_1.46.0         parallel_4.3.1          AnnotationDbi_1.62.1   
#>  [58] XVector_0.40.0          matrixStats_1.0.0       vctrs_0.6.3            
#>  [61] Matrix_1.5-4.1          jsonlite_1.8.5          sva_3.48.0             
#>  [64] carData_3.0-5           car_3.1-2               IRanges_2.34.0         
#>  [67] S4Vectors_0.38.1        bit64_4.0.5             rstatix_0.7.2          
#>  [70] ggrepel_0.9.3           systemfonts_1.0.4       locfit_1.5-9.8         
#>  [73] limma_3.56.2            tidyr_1.3.0             jquerylib_0.1.4        
#>  [76] annotate_1.78.0         glue_1.6.2              pkgdown_2.0.7          
#>  [79] codetools_0.2-19        stringi_1.7.12          gtable_0.3.3           
#>  [82] GenomeInfoDb_1.36.1     munsell_0.5.0           tibble_3.2.1           
#>  [85] pillar_1.9.0            htmltools_0.5.5         GenomeInfoDbData_1.2.10
#>  [88] R6_2.5.1                textshaping_0.3.6       rprojroot_2.0.3        
#>  [91] lattice_0.21-8          evaluate_0.21           Biobase_2.60.0         
#>  [94] highr_0.10              png_0.1-8               backports_1.4.1        
#>  [97] memoise_2.0.1           broom_1.0.5             bslib_0.5.0            
#> [100] Rcpp_1.0.10             nlme_3.1-162            mgcv_1.8-42            
#> [103] xfun_0.39               fs_1.6.2                MatrixGenerics_1.12.2  
#> [106] pkgconfig_2.0.3

Reference

Birkedal, Ulf, Mikkel Christensen-Dalsgaard, Nicolai Krogh, Radhakrishnan Sabarinathan, Jan Gorodkin, and Henrik Nielsen. 2014. “Profiling of Ribose Methylations in RNA by High-Throughput Sequencing.” Angewandte Chemie International Edition, November, n/a–. https://doi.org/10.1002/anie.201408362.

Erales, Jenny, Virginie Marchand, Baptiste Panthu, Sandra Gillot, Stéphane Belin, Sandra E. Ghayad, Maxime Garcia, et al. 2017. “Evidence for rRNA 2′-o-Methylation Plasticity: Control of Intrinsic Translational Capabilities of Human Ribosomes.” Proceedings of the National Academy of Sciences 114 (49): 12934–39. https://doi.org/10.1073/pnas.1707674114.

Marcel, Virginie, Janice Kielbassa, Virginie Marchand, Kundhavai S Natchiar, Hermes Paraqindes, Flora Nguyen Van Long, Lilia Ayadi, et al. 2021. “Ribosomal RNA 2’ O-methylation as a novel layer of inter-tumour heterogeneity in breast cancer.” NAR Cancer 3 (1). https://doi.org/10.1093/narcan/zcab006.

Marchand, Virginie, Florence Blanloeil-Oillo, Mark Helm, and Yuri Motorin. 2016. “Illumina-based RiboMethSeq approach for mapping of 2’-O-Me residues in RNA.” Nucleic Acids Research 44 (16): e135–35. https://doi.org/10.1093/nar/gkw547.

Théo COMBE, Hermes PARAQINDES, Janisse KIELBASSA, Emilie THOMAS, Anthony FERRARI and Virginie MARCEL

06/23/2023

Help, bug reports and suggestions

Acknowledgements

Funding

Installation

Usage

RiboClass

Loading data

Data to provide

Read-end counts

Metadata

How to load the data

RNA names

Provided

Custom

C-score calculation

What is a C-score ?

C-score computation when loading data

C-score computation during the analysis

Quality control

Quality control report

Batch effect identification

Batch effect adjustment

Verifying local coverage

Sample manipulation

Keep or remove samples

RNA manipulation and annotation

RNA manipulation

Remove RNA

Rename RNA

Annotation of RNA 2’Ome sites

Included annotation : Human 2’Ome rRNA sites

Customize 2’Ome sites annotations

Annotate RNA sites

Analytic functions for 2’Ome profiling

Analytic functions for site-by-site comparison of 2’Ome levels

Exporting data

Export as a dataframe

Export as a ggplot-ready dataframe

Export as a dataframe by condition

Sessioninfo

Reference