1 Introduction

RLSeq is a package for analyzing R-loop mapping data sets, and it is a core component of the RLSuite toolchain. It serves two primary purposes: (1) to facilitate the evaluation of data quality, and (2) to enable R-loop data analysis in the context of genomic annotations and the public data sets in RLBase. The main analysis steps can be conveniently run using the RLSeq() function. Then, an HTML report can be generated using the report() function. Individual steps of this pipeline are also accessible through separate functions which provide custom analysis capabilities.

This vignette will showcase the primary functionality of RLSeq with data from a publicly-available R-loop data mapping study in Ewing sarcoma cell lines, GSE68845. We have selected two DNA-RNA Immunoprecipitation sequencing (DRIP-seq) samples for demonstration purposes: (1) SRX1025890, a positive R-loop mapping sample (“POS”; condition: S9.6 -RNaseH1), and (2) SRX1025892, a negative control (“NEG”; condition S9.6 +RNaseH1). We will begin by showing a quick-start analysis on SRX1025890, and then we will proceed to discuss, in detail, the specific steps of this analysis with both samples.

2 Quick-start

Here, we demonstrate a simple analysis workflow which utilizes a publicly-available data set stored in RLBase (a database of R-loop consensus regions and R-loop-mapping experiments, also part of RLSuite). The commands below download these data, run RLSeq(), and generate the HTML report.

# Peaks and coverage can be found in RLBase
rlbase <- "https://rlbase-data.s3.amazonaws.com"
pks <- file.path(rlbase, "peaks", "SRX1025890_hg38.broadPeak")
cvg <- file.path(rlbase, "coverage", "SRX1025890_hg38.bw")

# Initialize data in the RLRanges object. 
# Metadata is optional, but improves the interpretability of results
rlr <- RLRanges(
  peaks = pks,
  coverage = cvg,
  genome = "hg38",
  mode = "DRIP",
  label = "POS",
  sampleName = "TC32 DRIP-Seq"
)

# The RLSeq command performs all analyses
rlr <- RLSeq(rlr)

# Generate an html report
report(rlr, reportPath = "rlseq_report_example.html")

The report generated by this code is found here.

3 Preliminary

3.1 Installation

RLSeq should be installed alongside RLHub to facilitate access to the data required for annotation and analysis. When downloading RLSeq from bioconductor, RLHub is already included.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("RLSeq")

Both packages can also be installed from github.

library(remotes)
install_github("Bishop-Laboratory/RLHub")
install_github("Bishop-Laboratory/RLSeq")

3.2 Obtaining data

RLSeq is compatible with R-loop data generated from a variety of pipelines and tools. However, it is strongly recommended that you use RLPipes, a snakemake-based CLI pipeline tool built specifically for upstream processing of R-loop datasets.

RLPipes can be installed using mamba or conda (slower).

# conda install -c conda-forge mamba
mamba create -n rlpipes -c bioconda -c conda-forge rlpipes
conda activate rlpipes

A typical config file (CSV) should be written as such:

experiment
SRX1025890
SRX1025892

And then the pipeline can be run.

RLPipes build -m DRIP rseq_out/ tests/test_data/samples.csv
RLPipes run rseq_out/

The resulting directory will contain peaks/, coverage/, bam/, and other processed data sets which are used in downstream analysis.

Note: If you choose to use a different pipeline, use macs2/macs3 for peak calling to ensure compatibility with RLBase.

4 End-to-end RLSeq

Here, we describe each step of the analysis pipeline which is run as part of the RLSeq() command.

library(RLSeq)
library(dplyr)
set.seed(1)

4.1 Data sets

For this example, we will be using DRIP-Seq data from a 2018 Nature paper on R-loops in Ewing sarcoma (Gorthi et al. 2018). The sample has been IP’d for R-loops (S9.6 -RNaseH1; label: “POS”). The data was processed using RLPipes and uploaded to RLBase. Peaks are converted to GRanges objects using a helper function from regioneR. URLs and file paths for peak files can also be supplied directly to without this step.

rlbase <- "https://rlbase-data.s3.amazonaws.com"

# Get peaks and coverage
s96Pks <- regioneR::toGRanges(file.path(rlbase, "peaks", "SRX1025890_hg38.broadPeak"))
s96Cvg <- file.path(rlbase, "coverage", "SRX1025890_hg38.bw")

For demonstration purposes, only 10000 ranges are analyzed here.

# For expediency, peaks we filter and down-sampled to the top 10000 by padj (V9)
# This is not necessary as part of the typical workflow, however
s96Pks <- s96Pks[s96Pks$V9 > 2,]
s96Pks <- s96Pks[sample(names(s96Pks), 10000)]

Finally, RLRanges objects were constructed. These are the primary objects used in all RLSeq functions. RLRanges are an extension of GRanges which provide additional metadata and validation functions.

## Build RLRanges ##
# S9.6 -RNaseH1
rlr <- RLRanges(
  peaks = s96Pks, 
  coverage = s96Cvg,
  genome = "hg38",
  mode = "DRIP",
  label = "POS",
  sampleName = "TC32 DRIP-Seq"
)

4.2 Sample quality

Sample quality is assessed by analyzing the association of peaks with R-loop-forming sequences (RLFS). RLFS are genomic sequences that favor the formation of R-loops (Jenjaroenpun et al. 2015). While R-loops can form outside RLFS, there is a strong relationship between them, which provides an unbiased test of whether a set of peaks actually represents successful R-loop mapping.

4.2.1 Permutation tests

RLSeq first implements a permutation test to evaluate the enrichment of peaks within RLFS and build a Z-score distribution around RLFS sites.

# Analyze RLFS for positive sample
rlr <- analyzeRLFS(rlr, quiet = TRUE)

The resulting objects now contain the permutation test results. These results can be easily visualized with the plotRLFSRes function.

plt <- plotRLFSRes(rlr)
# try() is used to prevent errors on latest MacOS during CI/CD testing.  
# You don't need try() when running this normally.
try(plt, silent = TRUE)

Figure 1: Plot of permutation test results (S9.6 -RNaseH1)

4.2.2 Quality classification

The quality classifier is an ensemble model based on an online-learning scheme. It predicts “POS” for samples which are predicted to show robust R-loop mapping and “NEG” for samples which are not. The latest version can be accessed via RLHub. For greater detail, please see the RLHub::modes reference. To apply the model and predict sample quality, use the predictCondition() function.

# Predict 
rlr <- predictCondition(rlr)

The results from testing our example samples:

# Access results
s96_pred <- rlresult(rlr, "predictRes")
cat("Prediction: ", s96_pred$prediction)

## Prediction:  POS

4.3 Noise analysis

Note: The code in this section does not work on Windows OS machines.

The next step of the RLSeq quality workflow is to analyze the noisiness of the coverage signal for the user-supplied sample (requires that coverage is provided when creating the RLRanges object). The approach used in this analysis step was derived from the work of Diaz et al., 2012 (Diaz et al. 2012). The method is run using the following function:

if (.Platform$OS.type != "windows") {
  rlr <- noiseAnalyze(rlr)
}

The results can then be visualized in two ways. A Fingerprint plot and a noiseComparisonPlot.

4.3.1 Fingerprint plot

To visualize the results of noiseAnalyze we can use a “fingerprint plot” (named after the deepTools implementation by the same name (Ramírez et al. 2016)).

if (.Platform$OS.type != "windows") {
    plotFingerprint(rlr)
}

This plot shows the proportion of signal contained in the corresponding proportion of coverage bins. In the plot above, we can observe that relatively few bins contain nearly all the signal. This is exactly what we would expect to see when our sample has good signal-to-noise ratio, a sign of good quality in R-loop mapping datasets.

4.3.2 Noise comparison plot

While a fingerprint plot is useful for getting a quick view of the dataset, it is also useful to compare the analyzed sample to publicly-available the datasets provided by RLBase. The noiseComparisonPlot enables this comparison.

if (.Platform$OS.type != "windows") {
    noiseComparisonPlot(rlr)
}

This plot displays the average standardized signal across bins (noise index) from all publicly-available datasets from the same modality (“DRIP-Seq”) and genome (“hg38”) as the user-supplied sample. The data are also divided by prediction/label combination. Finally, the user-supplied sample is indicated as a diamond (

Diaz, Aaron, Kiyoub Park, Daniel A. Lim, and Jun S. Song. 2012. “Normalization, Bias Correction, and Peak Calling for Chip-Seq.” Statistical Applications in Genetics and Molecular Biology 11 (3). https://doi.org/10.1515/1544-6115.1750.

Gorthi, Aparna, July Carolina Romero, Eva Loranc, Lin Cao, Liesl A. Lawrence, Elicia Goodale, Amanda Balboni Iniguez, et al. 2018. “EWSFLI1 Increases Transcription to Cause R-Loops and Block Brca1 Repair in Ewing Sarcoma.” Nature 555 (7696): 387–91. https://doi.org/10.1038/nature25748.

Jenjaroenpun, Piroon, Thidathip Wongsurawat, Surya Pavan Yenamandra, and Vladimir A. Kuznetsov. 2015. “QmRLFS-Finder: A Model, Web Server and Stand-Alone Tool for Prediction and Analysis of R-Loop Forming Sequences.” Nucleic Acids Research 43 (W1): W527–W534. https://doi.org/10.1093/nar/gkv344.

Ramírez, Fidel, Devon P Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S Richter, Steffen Heyne, Friederike Dündar, and Thomas Manke. 2016. “DeepTools2: A Next Generation Web Server for Deep-Sequencing Data Analysis.” Nucleic Acids Research 44 (W1): W160–W165. https://doi.org/10.1093/nar/gkw257.

Analyzing R-loop data with RLSeq

7 November 2022

Abstract

Package