ARF (v 1.0)

R package for the Analysis of Ribosomal RNA Fragments, generally generated by Ribosome Profiling (Ribo-Seq) experiments

Currently, it includes the following methods:

• dripARF, ribosome heterogeneity identification pipeline that performs differential ribosomal protein incorporation predictions by combining rRNA abundance data with the 3D structure of the ribosome.

Installation

In R (>= 2.10) environment

install.packages("devtools")
devtools::install_github("fallerlab/ARF@main")

Required R libraries

Please make sure that you have the following packages installed as dripARF requires them:

• bedr
• DESeq2 (>= 1.30.1)
• clusterProfiler
• ComplexHeatmap
• enrichplot
• fgsea
• grid
• ggplot2
• ggrepel
• matrixStats
• reshape2
• scales
• SummarizedExperiment

ARF package - How to use?

To be able to use the ARF package, you first need to align your adapter-trimmed Ribo-seq reads to given rRNA sequences which are provided under the "rRNAs/" folder for two organisms; human and mouse. Using these alignments, ARF quantifies the rRNA fragments at each rRNA position for further analysis.

Currently, ARF package contains only the dripARF method. Please stay tuned for future additions :)

The dripARF method/pipeline

Based on position-specific rRNA abundances and the 3D structure of the ribosome, dripARF predicts which ribosomal proteins have a change in their ribosomal incorporation between given conditions. With such prediction, it identifies the prime candidates that contributes to the inter-sample heterogeneity of ribosomes across conditions.

The input for the dripARF pipeline is as follows:

• Read alignment files that are generated by aligning adapter-trimmed Ribo-seq reads to given rRNA sequences
• tab-seperated samples file; this file contains the sample id as first column, file path to the rRNA read alignment file as second column, and a group/condition identifier as the third column (see test_data/samples.tsv as an example).

Prior to using dripARF, assuming rRNA read alignments are ready, we also suggest to perform your positional rRNA abundance quantifications using the "bedtools" software. Note that this is not mandatory since you can also use your indexed .bam files as input. However, using pre-processed alignments (.bedGraph) will significantly increase the computational speed of dripARF. Following is the example on how you can create your .bedGraph files.

bedtools genomecov -bg -ibam /your/bam/file.bam >  /your/bedGraph/file.bedGraph

If your alignment files, whether in .bam or .bedGraph format, are ready and accessible through the paths given within samples.tsv file, and your target folder is accessible, you can run the dripARF wrapper function in one line within your R environment.

dripARF_results <- ARF::dripARF(samplesFile = "samples.tsv", organism="mm", QCplot=TRUE, targetDir="dripARF_results/")

This function will run the whole pipeline and will return a data frame with the ribosome heterogeneity prediction results for all possible comparisons. It will also create comparison-specific ".csv" files to store your results in addition to a few informative QC plots. Most importantly, the pipeline will save a scatterplot and heatmap visualization summarizing your results.

Within this function, there exists a few other options as well. For example, you can specify which comparisons you want to focus on, or which samples to exclude from the analysis.

Please note that you have to specify the organism and align your reads strictly to given rRNA sequences when running the pipeline.

dripARF() function in detail

dripARF() wrapper function uses series of other dripARF functions to obtain your results. We summarize below what these functions are used for.

• ARF_check_organism() : Checks if given organism is supported within ARF.
• read_ARF_samples_file() : This function allows you to read the tab-seperated ARF-samples file, returning a dataframe.
• dripARF_read_rRNA_fragments() : Read .bedgraph/.bam files to create the rRNA count data.
• dripARF_get_DESEQ_dds() : The function that normalizes rRNA counts with DESEQ2.
• dripARF_report_RPset_group_counts() : Calculate the average read count of RP contact point sets.
• dripARF_predict_heterogenity() : Overlap differential rRNA count data with 3d ribosome, rRNA-RP proximity data and predict heterogeneity candidates across groups.
• dripARF_simplify_results() : Simplify dripARF results based on given thresholds.
• dripARF_result_heatmap() : Draw heatmap of NES (ES1) and NES_randZscore (ES2) enrichments scores, where RPs are filtered based on given thresholds.
• dripARF_result_scatterplot() : Draw comparison-specific scatter plots representing RPSEA and ORA analyses results.
• dripARF_report_RPspec_pos_results() : Obtain the positional differential abundance change values for RP proximity sets.
• dripARF_rRNApos_heatmaps() : Draw rRNA fragment change heatmaps to visualize position-specific differential rRNA fragment abundances.
• dripARF_threshold_test() : (Experimental function) This function allows you to run the whole dripARF pipeline with varying proximity thresholds.

TESTING ARF

If you have installed the ARF package successfully, you can test it with the data provided under the test_data/ folder.

Copyright

Copyright 2021 by Ferhat Alkan

ARF is released under the GNU General Public License.

GNU GENERAL PUBLIC LICENSE

This is a free software: you can redistribute it and/or modify it under the terms of the GNU General Public License, either version 3 of the License, or (at your option) any later version. You should have received a copy of the GNU General Public License along with ARF, see file LICENSE. If not, see http://www.gnu.org/licenses/.

This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Citations

If you actively use the ARF platform, please cite the following publications:

Ferhat Alkan, Oscar Wilkins, Santiago Hernandez-Perez, Sofia Ramalho, Joana Silva, Jernej Ule, William J Faller; Identifying ribosome heterogeneity using ribosome profiling data, Nucleic Acids Research, 2022; gkac484, https://doi.org/10.1093/nar/gkac484

Contact

For questions, queries, bug reports; please use the github issues tab or contact: fallerlab@gmail.com and/or feralkan@gmail.com.