Quick Figure - Main.
Definition of binding sites from iCLIP signal
21 November 2024
Abstract
Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. The package BindingSiteFinder provides all functionalities to define exact binding sites defined from iCLIP data. The following vignette describes the complete workflow.
Package
BindingSiteFinder 2.5.2
1 Preface
1.1 Motivation
Most cellular processes are regulated by RNA-binding proteins (RBPs). Knowledge on their exact positioning can be obtained from individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments. In a recent publication we described a complete analysis workflow to detect RBP binding sites from iCLIP data. The workflow covers all essential steps from quality control of sequencing reads, different peak calling options, to the downstream analysis and definition of binding sites. The pre-processing and peak calling steps rely on publicly available software, whereas the definition of the final binding sites follows a custom procedure implemented in BindingSiteFinder. This vignette explains how equally sized binding sites can be defined from a genome-wide iCLIP coverage.
1.2 Prerequisites
The workflow described herein is based on our recently published complete iCLIP analysis pipeline (Busch et al. 2020). Thus, we expect the user to have preprocessed their iCLIP sequencing reads up to the point of the peak calling step. In brief, this includes basic processing of the sequencing reads, such as quality filtering, barcode handling, mapping and the generation of a single nucleotide crosslink file for all replicates under consideration. As we describe in our manuscript, replicate .bam files may or may not be merged prior to peak calling, for which we suggest PureCLIP (Krakau, Richard, and Marsico 2017). For simplicity, we address only the case in which peak calling was performed on the merge of all replicates.
Overview of the preprocessing workflow.
1.3 Installation
The BindingSiteFinder package is available at https://bioconductor.org and can be installed via BiocManager::install:
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("BindingSiteFinder")Note: If you use BindingSiteFinder in published research, please cite:
Busch, A., Brüggemann, M., Ebersberger, S., & Zarnack, K. (2020) iCLIP data analysis: A complete pipeline from sequencing reads to RBP binding sites. Methods, 178, 49-62. https://doi.org/10.1016/j.ymeth.2019.11.008
2 Quick start
This part is for the impatient user that wants to start as fast as possible. For that reason this section contains only the most basic steps and function calls to get to the final binding site set. For more details on each step see the Standard workflow section.
2.1 A quick look at the input data
It all starts with your input iCLIP data, which has to be in form of a BSFDataSet. This data-structure combines crosslink sites (the output from PureCLIP) with the individual crosslinks per replicate (iCLIP coverage). Further instructions on the data-set constructions are given in the chapter Construction of the BindingSiteFinder dataset.
files <- system.file("extdata", package="BindingSiteFinder")
load(list.files(files, pattern = ".rda$", full.names = TRUE))
bds## Dataset: Test set
## Object of class BSFDataSet
## #N Ranges: 19,749
## Width ranges: 1
## #N Samples: 4
## #N Conditions: 1To intersect binding sites with genomic features, such as genes and transcript regions the respective regions have to be presented as GenomicRanges objects. Further instructions on how to import annotations from various sources are given in chapter Construct the annotation objects.
load(list.files(files, pattern = ".rds$", full.names = TRUE)[1])
gns## GRanges object with 1387 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000008735.14 chr22 50600793-50613981 + | ENSG00000008735.14
## ENSG00000015475.19 chr22 17734138-17774770 - | ENSG00000015475.19
## ENSG00000025708.14 chr22 50525752-50530032 - | ENSG00000025708.14
## ENSG00000025770.19 chr22 50508224-50524780 + | ENSG00000025770.19
## ENSG00000040608.14 chr22 20241415-20283246 - | ENSG00000040608.14
## ... ... ... ... . ...
## ENSG00000288106.1 chr22 38886837-38903794 + | ENSG00000288106.1
## ENSG00000288153.1 chr22 30664961-30674134 + | ENSG00000288153.1
## ENSG00000288262.1 chr22 11474744-11479643 + | ENSG00000288262.1
## ENSG00000288540.1 chr22 48320412-48333199 - | ENSG00000288540.1
## ENSG00000288683.1 chr22 18077989-18131720 + | ENSG00000288683.1
## gene_type gene_name
## <character> <character>
## ENSG00000008735.14 protein_coding MAPK8IP2
## ENSG00000015475.19 protein_coding BID
## ENSG00000025708.14 protein_coding TYMP
## ENSG00000025770.19 protein_coding NCAPH2
## ENSG00000040608.14 protein_coding RTN4R
## ... ... ...
## ENSG00000288106.1 lncRNA AL022318.5
## ENSG00000288153.1 unprocessed_pseudogene AC003072.2
## ENSG00000288262.1 unprocessed_pseudogene CT867976.1
## ENSG00000288540.1 lncRNA AL008720.2
## ENSG00000288683.1 protein_coding AC016027.6
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsload(list.files(files, pattern = ".rds$", full.names = TRUE)[2])
regions## GRangesList object of length 4:
## $CDS
## GRanges object with 17035 ranges and 4 metadata columns:
## seqnames ranges strand | cds_id exon_id exon_name
## <Rle> <IRanges> <Rle> | <integer> <integer> <character>
## chr22 15528192-15529139 + | 1 <NA> <NA>
## chr22 15690078-15690314 + | 2 <NA> <NA>
## chr22 15690078-15690709 + | 3 <NA> <NA>
## chr22 15690246-15690709 + | 4 <NA> <NA>
## chr22 15690426-15690709 + | 5 <NA> <NA>
## . ... ... ... . ... ... ...
## chr22 50782188-50782294 - | 17031 <NA> <NA>
## chr22 50782188-50782294 - | 17032 <NA> <NA>
## chr22 50782188-50782294 - | 17033 <NA> <NA>
## chr22 50782188-50782294 - | 17034 <NA> <NA>
## chr22 50782234-50782294 - | 17035 <NA> <NA>
## exon_rank
## <integer>
## <NA>
## <NA>
## <NA>
## <NA>
## <NA>
## . ...
## <NA>
## <NA>
## <NA>
## <NA>
## <NA>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## ...
## <3 more elements>2.2 A quick way to define binding sites
The fastest way to accurately define binding site with very little manual intervention is running the wrapper function BSFind(). It will perform all necessary computations and will store all results and diagnostics internally. Various plotting functions can be used to make all sorts of diagnostics. Export functions can be used to extract resulting binding sites for further processing. Each step is covered in more detail in the Standard workflow section.
# run BSFind to compute binding sites
bdsQuick = BSFind(object = bds, anno.genes = gns, anno.transcriptRegionList = regions, est.subsetChromosome = "chr22")
# export output as .bed
exportToBED(bdsQuick, con = "./myBindingSites.bed")2.3 The quick picture, two figures for the lazy one
To get a fast glance at the data-set with minimal user intervention that is also visually appealing, the quickFigure() function exists.
# not run
quickFigure(bdsQuick, what = "main", save.filename = "./my_fig_main.pdf", save.device = "pdf")# not run
quickFigure(bdsQuick, what = "supp", save.filename = "./my_fig_supp.pdf", save.device = "pdf")
Quick Figure - Supp.
3 Standard workflow
We showcase the standard workflow on the example of the RNA-binding protein U2AF2. The data-set consists of four replicates. Sequencing reads from all replicates were pre-processed up to the point of .bam files. These were then merged and subjected to peak calling with PureCLIP. Resulting peaks as well as the processed coverage files (.bigwigs) are what is needed as input for the following sections.
3.1 Manage the input data
3.1.1 Build the BSFDataSet
To build the BSFDataSet one needs two types of information. First, the result of the peak calling step (crosslink sites) and second the iCLIP coverage in the form of .bigwig files for each replicate. Here we first import the crosslink sites to be represented as a GenomicRanges object. We also expect that all ranges are a single nucleotide wide1 Note: We remove the column additionalScore after the initial load, to clean our input.
# load crosslink sites
csFile <- system.file("extdata", "PureCLIP_crosslink_sites_examples.bed",
package="BindingSiteFinder")
cs = rtracklayer::import(con = csFile, format = "BED", extraCols=c("additionalScores" = "character"))
cs$additionalScores = NULL
cs## GRanges object with 1000 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 629906 + | 3 27.30000
## [2] chr1 629911 + | 3 1.72212
## [3] chr1 629912 + | 3 15.68900
## [4] chr1 629913 + | 3 13.82090
## [5] chr1 629915 + | 3 6.36911
## ... ... ... ... . ... ...
## [996] chr1 2396116 + | 3 7.74693
## [997] chr1 2396990 + | 3 3.92442
## [998] chr1 2396991 + | 3 14.52750
## [999] chr1 2396992 + | 3 6.59018
## [1000] chr1 2397001 + | 3 1.27159
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe .bigwig files with the coverage information are located in a single folder. The path is given in the files variable. We then use the path to list all .bigwig files from each strand separately.
# Load clip signal files and define meta data object
files <- system.file("extdata", package="BindingSiteFinder")
clipFilesP <- list.files(files, pattern = "plus.bw$", full.names = TRUE)
clipFilesM <- list.files(files, pattern = "minus.bw$", full.names = TRUE)The strand specific vectors with the path information that point to each replicate .bigwig file are combined with additional meta information into a meta table data.frame. This data.frame has to have at minimum three columns, which must be named condition, clPlus and clMinus. The clPlus and clMinus columns point towards the strand-specific coverage for each replicate. This information will be imported as Rle objects upon object initialization.
# make the meta table
meta = data.frame(
id = c(1:4),
condition = factor(rep("WT", 4)),
clPlus = clipFilesP, clMinus = clipFilesM)
meta## id condition
## 1 1 WT
## 2 2 WT
## 3 3 WT
## 4 4 WT
## clPlus
## 1 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep1_clip_plus.bw
## 2 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep2_clip_plus.bw
## 3 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep3_clip_plus.bw
## 4 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep4_clip_plus.bw
## clMinus
## 1 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep1_clip_minus.bw
## 2 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep2_clip_minus.bw
## 3 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep3_clip_minus.bw
## 4 /tmp/RtmpTbFfzm/Rinst73ccf22a5be73/BindingSiteFinder/extdata/rep4_clip_minus.bwWith the crosslink sites and the meta table ready we are good to construct the BSFDataSet object using the BSFDataSetFromBigWig() function. We can get information of the constructed object using it’s show method, which show the number of ranges and crosslinks present in the object2 Note: We load a previously compiled BSFDataSet to save disc space..
library(BindingSiteFinder)
bds = BSFDataSetFromBigWig(ranges = cs, meta = meta, silent = TRUE)exampleFile <- list.files(files, pattern = ".rda$", full.names = TRUE)
load(exampleFile)
bds## Dataset: Test set
## Object of class BSFDataSet
## #N Ranges: 19,749
## Width ranges: 1
## #N Samples: 4
## #N Conditions: 1# exampleFile <- list.files(files, pattern = ".rda$", full.names = TRUE)
# load(exampleFile)3.2 Construct the annotation objects
To take advantage of the full functionality of the package one needs to provide gene annotation information. These will be used to map computed binding sites directly on target genes and and transcript regions of protein-coding genes. Here, we use GENCODE hg38 gene annotations.
3.2.1 Create annotation for genes
To present gene-level information we import the annotation as a .gff3 file into a GenomicRanges object. To combine the ranges with the available meta information for each gene (such as a gene_id, gene_name or gene_type), we have to read our annotation twice. First, we import the ranges into a TxDB database using functionalists from GenomicFeatures. This database can be queried using the genes() function to retrieve the positions of all genes from our resource as GRanges objects3 Note: It is of particular importance that one uses a valid format for the geneID (ENSEMBL, UCSC, …) at this point.. Second we use rtracklayer to import all ranges with the corresponding meta information. We then match both imports to create a single GRanges object which contains all genes from our annotation with relevant meta information added4 Note: We again only load a previously compiled object at this point to save disc space..
library(txdbmaker)
# Make annotation database from gff3 file
annoFile = "./gencode_v37_annotation.gff3"
annoDb = txdbmaker::makeTxDbFromGFF(file = annoFile, format = "gff3")
annoInfo = rtracklayer::import(annoFile, format = "gff3")
# Get genes as GRanges
gns = genes(annoDb)
idx = match(gns$gene_id, annoInfo$gene_id)
meta = cbind(elementMetadata(gns),
elementMetadata(annoInfo)[idx,])
meta = meta[,!duplicated(colnames(meta))]
elementMetadata(gns) = meta# Load GRanges with genes
geneFile <- list.files(files, pattern = "gns.rds$", full.names = TRUE)
load(geneFile)
gns## GRanges object with 1387 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000008735.14 chr22 50600793-50613981 + | ENSG00000008735.14
## ENSG00000015475.19 chr22 17734138-17774770 - | ENSG00000015475.19
## ENSG00000025708.14 chr22 50525752-50530032 - | ENSG00000025708.14
## ENSG00000025770.19 chr22 50508224-50524780 + | ENSG00000025770.19
## ENSG00000040608.14 chr22 20241415-20283246 - | ENSG00000040608.14
## ... ... ... ... . ...
## ENSG00000288106.1 chr22 38886837-38903794 + | ENSG00000288106.1
## ENSG00000288153.1 chr22 30664961-30674134 + | ENSG00000288153.1
## ENSG00000288262.1 chr22 11474744-11479643 + | ENSG00000288262.1
## ENSG00000288540.1 chr22 48320412-48333199 - | ENSG00000288540.1
## ENSG00000288683.1 chr22 18077989-18131720 + | ENSG00000288683.1
## gene_type gene_name
## <character> <character>
## ENSG00000008735.14 protein_coding MAPK8IP2
## ENSG00000015475.19 protein_coding BID
## ENSG00000025708.14 protein_coding TYMP
## ENSG00000025770.19 protein_coding NCAPH2
## ENSG00000040608.14 protein_coding RTN4R
## ... ... ...
## ENSG00000288106.1 lncRNA AL022318.5
## ENSG00000288153.1 unprocessed_pseudogene AC003072.2
## ENSG00000288262.1 unprocessed_pseudogene CT867976.1
## ENSG00000288540.1 lncRNA AL008720.2
## ENSG00000288683.1 protein_coding AC016027.6
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths3.2.2 Create annotation for transcript regions
To represent all possible transcript regions, we re-use the previously created TxDB annotation database. The GenomicFeatures package provides dedicated information to retrieve specific sets of ranges from such a database object, like CDS, UTRs, etc. Here we store these ranges into a GRangestList and name the elements accordingly5 Note: Just like for the genes, we load a pre-compiled list to save disc space..
# Count the overlaps of each binding site for each region of the transcript.
cdseq = cds(annoDb)
intrns = unlist(intronsByTranscript(annoDb))
utrs3 = unlist(threeUTRsByTranscript(annoDb))
utrs5 = unlist(fiveUTRsByTranscript(annoDb))
regions = GRangesList(CDS = cdseq, Intron = intrns, UTR3 = utrs3, UTR5 = utrs5)# Load list with transcript regions
regionFile <- list.files(files, pattern = "regions.rds$", full.names = TRUE)
load(regionFile)3.3 Performing the main analysis
Now that we have prepared our input data, we are good to use the main workhorse, the BSFind() function. This function is essentially a high-level wrapper around all core utility functions and provides an easy entry point into the analysis. At it’s core each analysis step consists of a utility function that computes the respective result. Each utility function further comes along with a set of diagnostic/ result plotting functions. These can be used to assess the quality of every computational step. We recommend to once run the entire analysis with BSFind() and then follow up on each step in detail using the plot functionalities6 Note: Here we set est.subsetChromosom='chr22' because we have only signal on chromosome 22 to save disc space.
bdsOut = BSFind(object = bds, anno.genes = gns, anno.transcriptRegionList = regions,
est.subsetChromosome = "chr22", veryQuiet = TRUE, est.maxBsWidth = 29)It is good practice to once visualize the entire binding site definition process after BSFind() was executed. This helps to keep track what steps were performed and how the flow into each other7 Note: Not all binding sites can be assigned to genes or transcript regions, in these cases NA is introduced, so no binding site is lost..
processingStepsFlowChart(bdsOut)
Figure 1: processingStepsFlowChart
As a default all of the shown steps are executed sequentially in the given order. To change that order, each utility function can be called individually to set up a customized workflow (for details see Construct your own pipeline).
3.3.1 Deciding on the binding site width
The first and main step of any binding site analysis is the actual binding site definition step. In the following we showcase how you can apply the main workflow functions to accurately define binding sites and judge the results.
3.3.1.1 Pre-filtering of crosslink sites
An optional step prior to the actual merging of crosslink sites into binding sites is pre-filtering. Depending on the experiment type or sequencing depth, it might be useful to retain only the most informative crosslink sites. In the case of PureCLIP, the positions called as significantly enriched (hereafter called PureCLIP sites) are associated with a binding affinity strength score (hereafter called PureCLIP score) which can be used as a metric for the pre-filtering step. As default PureCLIP sites with the lowest 1% score will be removed.
pureClipGlobalFilterPlot(bdsOut)
Figure 2: pureClipGlobalFilterPlot
The diagnostic shows the distribution of PureCLIP scores on a log2 scale with a 10% step increased coloring for easier visualization and comparability. The selected cutoff is indicated by the dashed line.
3.3.1.2 Selection of the binding site width
In case the option bsSize is not set, the optimal binding site width is estimated from the data directly (using the estimateBsWidth function). Since the final binding site width depends on the quality of the input crosslink sites, bsSize is estimated together with the gene-wise filter level (cutoff.geneWiseFilter). That is filtering by the top X% crosslink sites per gene. Filtering on gene level has been shown to be really effective in diluting the most informative signal from the data8 Note: Since we did not specify bsSize in BSFind() the binding site size is estimated automatically..
estimateBsWidthPlot(bdsOut)
Figure 3: estimateBsWidthPlot
The diagnostic show the ratio between the crosslink events within binding sites and the crosslink events in adjacent windows to the binding sites. This is effectively a ‘percent-bound’ type ratio, with the higher the better the associated binding site width captures the distribution of the underlying crosslink events. The plot shows how that ratio behaves for different binding site width under varying levels of the gene-wise filter.
Signal-to-flank score values are estimated on all binding sites on a given chromosome (est.bsResolution) for varying levels of accuracy. For est.geneResolution options “coarse” results in 20% steps, “medium” in 10%, “fine” in 5% and “finest” in 1% steps. For est.bsResolution option “coarse” and “medium” approximate binding sites by extending the center of the each merged crosslink region. In “coarse” the center is found by the maximum PureCLIP score, in “medium” the center is found by the position with the most stacked crosslinks. Option “fine” employs a full binding site definition cycle. Binding sites are tested from the minimum size, which is 3, up to maxBsWidth.
After iterating over all sets given the above resolutions a mean score per binding site width is calculated and the binding site width that yielded the highest score is selected as optimal. The est.minimumStepGain option allows control over the minimum additional gain in the score that a tested width has to have to be selected as the best option (the default is 2% increase). This essentially lets binding site sizes to be computed more conservatively, opting for smaller binding sites rather than larger ones. This is particularly useful when the estimated mean score flattens out at a certain point showing only a very small increase in the score per binding site width increase. Samples with broad binding RBPs or samples with poor coverage tend to show this behavior.
In case you already know your binding site width and/or gene-wise filtering threshold you can invoke BSFind() with these parameters, to avoid the execution of the utility function estimateBsWidth(). For details see section If you already know your binding site size.
In case you want to manually investigate which binding site size to use see the section How to manually test different binding site sizes.
3.3.1.3 Apply the gene-wise filter
The gene-wise filter cutoff estimated in the previous step (cutoff.geneWiseFilter) is applied to keep only the top percent of crosslink sites, ranked by their PureCLIP score. In case the value for cutoff.geneWiseFilter was given directly without estimation, that value is used directly.
Such a filter essentially helps to improve the binding site quality by using only the defined subset per gene of all PureCLIP sites for the binding site definition. This is because the strength of a binding site is tied to the expression level of the hosting gene. Thus a gene-wise percentage filter will increase the quality on all hosting genes in the same order.
Since a single PureCLIP site might overlap with multiple different genes given in the annotation, a diagnostic function helps to visualize the magnitude of that problem.
duplicatedSitesPlot(bdsOut)
Figure 4: duplicatedSitesPlot
Diagnostic plot that show how many crosslink sites overlap how many different genes in the annotation. The grey area for 2 and 3 overlaps indicates what proportion the option overlaps=‘keepSingle’ removes in order to keep crosslink site numbers stable.
To handle such overlapping cases the overlaps parameter can be used with options "keepSingle", "removeAll" and "keepAll". We recommend to stick with the default (overlaps="keepSingle"), which reduces the number of crosslink sites on overlapping loci to a single instance. This will conserve the number of crosslink sites. The additional options overlaps="removeAll" and overlaps="keepAll" will either increase or decrease the number of crosslink sites in the set and should be handled with care.
3.3.1.4 Merge crosslink sites into binding sites
In this step we turn the single-nucleotide wide PureCLIP sites into binding sites of a desired size (bsSize). This most essential parameter can either be estimated automatically, or be set directly. For the inexperienced user we recommend to use the estimated size in almost all cases. An exception would be very sparse or incomplete data, which should result in warning messages during the estimation step. If you do want to use a manual size, because you have eg. prior-knowledge of your RBP please have a look at the If you already know your binding site size section first.
Binding site merging happens by concatenation of neighboring crosslink sites into larger regions. These regions are then split up again using the crosslink coverage to guide the position of the final binding sites. This process happens iteratively, where each merge and resize round resolves more regions unitl all regions are converted into binding sites. The following diagnostic plots the number of regions that are still left to be resolved after each round and compares it to the size of these regions9 Note: Since we use a very small data-set for demonstration purpose, this plot might look different with a real-sized data-set..
mergeCrosslinkDiagnosticsPlot(bdsOut)
Figure 5: mergeCrosslinkDiagnosticsPlot
Diagnostic plot with number and region size at each fitting itteration loop.
A variety of different filtering options for placing a final binding site inside a merged region exist. Most importantly merge.minClSites gives the minimum number of initial crosslink sites that have to overlap a final binding site. How this and all other options affect the final binding site set size is displayed by the following diagnostic.
makeBsSummaryPlot(bdsOut)
Figure 6: makeBsSummaryPlot
Diagnostic bar chart indicating the number of binding site after each of the indicated filter options is applied given their input values.
3.3.2 How to ensure reproducibility among replicates
Since the initially used crosslink sites are computed from the merged signal of all replicates, binding sites resulting from the previous merge might not be reproducible among all replicates. For that reason, we specifically ask in this section which of the computed binding sites are reproduced by the individual replicates.
3.3.2.1 The replicate-specific threshold
Since replicates might differ in library size, a replicate-specific threshold is computed base on the binding site support distribution. That is how many crosslinks fall into the computed binding site for each replicate. In other words, we compute the number of crosslinks per binding site and replicate. This results in the following histogram.
When calling BSFind() we did not specify any further cutoff for this particular step, so the 5% quantile (cutoff) with a lower boundary of 1 crosslink (minCrosslinks) is used as default. Here for example a binding site would be called reproducible by replicate No.3 if at least 4 crosslinks from that replicate fall into the range of the binding site[^8].
[^8] Note: These settings can be adapted to for example produce high and low confidence binding site sets.
reproducibilityFilterPlot(bdsOut)
Figure 7: reproducibilityFilterPlot
Reproducibility crosslink histogram, with the number of crosslink sites over the number of crosslinks per binding site for each replicate. The dashed line indicates the replicate-specific cutoff.
3.3.2.2 How many replicates are enough?
After computing the replicate-specific thresholds as indicated above, one must decide on the number of replicates that must meet the thresholds for a binding site to be seen as reproducible.
A binding site that meets the threshold for all replicates in the set would be highly reproducible, whereas a binding site that meets the threshold for a single replicate only would be not reproducible at all. To find a balance in this question we suggest to stay with the default of N-1 for the nReps parameter. However if your replicates show a very high variability among each other changing this parameter could be beneficial to increase the specificity of the reproducible set. The following diagnostic assists in that decision, by visualizing the degree of which reproducible sets overlap among each other.
reproducibilitySamplesPlot(bdsOut)
Figure 8: reproducibilitySamplesPlot
UpSet plot indicating the degree of reproducibility overlap between each replicate.
3.3.2.3 Do my replicate corrlate well?
A common question for reproducibility is the visualization of the between replicate pairwise reproducibility. The following scatter plot does exactly that. Depending on when the correlations are calculated, the binding site set is either already corrected for reproducibility or not. To compare pairwise correlations before and after the application of the reproducibility filtering see Reproducibility scatter before and after correction.
reproducibilityScatterPlot(bdsOut)
Figure 9: reproducibilityScatterPlot
Bottom left) Pairwise correlations between all replicates as scatter. Upper right) Pairwise pearson correlation coefficient. Diagnoal) Coverage distribution as density.
3.3.3 Genomic target identification
A major question the follow from binding site definition is the assessment of the genomic targets that the RBP under consideration binds to. In the following section we showcase how target genes and transcript regions can be identified and what one has to consider doing so. In case you don’t have a gene annotation at hand but still want to compute binding sites, have a look at the section Work without a gene annotation.
3.3.3.1 Target gene identification
Here we essentially overlap the annotation given in anno.genes with the reproducible set of binding site computed above. Depending on the source and organism, gene annotations overlap each other to some degree.
We implemented several strategies to resolve these overlaps with overlaps="frequency" being the recommended default. Here the most frequently present gene_type is used to resolve overlaps. This however requires that the annotated genes (given in anno.genes) carry gene type information as a meta column. The exact column name can be specified with match.geneType. The same requirement holds true for the option overlaps="hierarchy", where a manual hierarchical rule on the gene type is used (overlaps.rule is required)10 Note: If an overlaps exists, but gene types are identical options “frequency” and “hierarchy” will cause the gene that was seen first to be selected as representative. .
The following plot visualizes among which gene types the most overlaps exist11 Note: Strategies that work without gene type information are overlaps="remove" and overlaps="keep" that respectively remove or keep all overlapping cases..
geneOverlapsPlot(bdsOut)
Figure 10: geneOverlapsPlot
UpSet plot with the number of binding sites overlapping a specific gene type from the annotation before overlaps were resolved.
The final target gene spectrum can now be visualized with the following plot. Depending on which of the overlap strategies are being used, numbers in this plot will change slightly.
targetGeneSpectrumPlot(bdsOut)
Figure 11: targetGeneSpectrumPlot
Bar chart with the number of gene and binding sites of a given gene type after overlaps were resolved.
The assigned gene with it’s ID, name and gene type (if given) is also attached to the binding site meta data, which can be retrieved as GRanges object using the getRanges() function.
3.3.3.2 Transcript region identification
Similar to the target gene, each binding site can be assigned to a hosting transcript region. This is done using the anno.transcriptRegionList resource that we defined earlier. Essentially each binding site is overlapped with each of the ranges given in the region list. As one would expect, the problem of overlapping annotations is increased manifold on the level of transcript regions compared to entire genes.
Options to resolve these are similar to those on gene level. Option overlaps="frequency" will resolve based on the most frequently observed transcript region, overlaps="hierarchy" uses a manual hierarchic assignment based on overlaps.rule12 Note: This time the transcript type is pulled from the names of the GRangesList defined in anno.transcriptRegionList. . Additionally, option overlaps="flag" will just flag all overlaps with the tag ambiguous and option overlaps="remove" will remove all those cases. The following plot illustrates the degree of overlap for our example data-set.
transcriptRegionOverlapsPlot(bdsOut)
Figure 12: transcriptRegionOverlapsPlot
UpSet plot with the number of binding sites overlapping a specific transcript region from the annotation before overlaps were resolved.
We observe that most overlaps are generated by binding sites that are either within an intron or a 3’UTR. As indicated by the plot we use the default strategy overlaps="frequency" is used to resolve these cases. Since introns were most frequently observed in the data, the transcript region intron was selected in these cases.
The final binding spectrum on transcript region level can be visualized with the following plot. Depending on which of the overlap strategies are being used, numbers in this plot will change slightly.
transcriptRegionSpectrumPlot(bdsOut)
Figure 13: transcriptRegionSpectrumPlot
Bar chart with the number of binding sites for each transcript region after overlaps were resolved.
The assigned transcript region is also attached to the binding site meta data, which can be retrieved as GRanges object using the getRanges() function.
If you want to learn about how to normalize these profiles by the length of each region see section Normalize transcript regions.
The described matching of binding sites and transcript regions is entirely based on overlaps with the regions given in anno.transcriptRegionList. This means one can customize these regions by using different sets of ranges to extend and modify this list. For further details on how to do this see section Use a different set of transcript regions.
3.3.4 Assessing further binding site properties
Another common task is to assess the strength and shape of a binding site. We provide two additional metrics as part of our core workflow that allow to assess exactly that.
3.3.4.1 Binding site strength
The affinity of a binding site to the RNA is already given to some degree by PureCLIP. The PureCLIP model computes a strength score that captures affinity for each crosslink site. Here we re-map these scores from the initial crosslink sites to the final binding sites. This is done through the match.score argument that point to the meta column name with the score from the initial crosslink sites13 Note: Essentially any numerical score can be transferred from the initial crosslinks to the final binding site using this option.. As for the default we assign the highest score from all overlapping crosslink site to the binding site (match.option="max"). The following plot shows the distribution of scores as density for all binding sites after re-assignment.
globalScorePlot(bdsOut)
Figure 14: globalScorePlot
The diagnostic shows the distribution of PureCLIP scores on a log2 scale with a 10% step increased coloring for easier visualization and comparability.
3.3.4.2 Binding site definedness
How well the defined binding sites fit to the observed crosslink coverage can be assess through binding site definedness. This also gives first insights into the overall binding site shape and the binding mode of the RBP. Here we assess this property through a percent bound score, that divides the number of crosslinks inside the binding sites by the total number of crosslinks in a region around the binding site. As default, this background region is two times the size of the binding site. In other words, we compare the signal within a binding site to a same sized window directly flanking the binding site on both sides. A splicing factor that recognizes very clear motifs will for example form very sharp peaks, which will lead to a high percent bound degree. A broad binding RBP on the other hand, that scans more along the RNA, will lead to a lower definedness score.
Depending on which steps were executed in the Genomic target identification section, the plot can be grouped by the assignment (by="all", by="transcript_region" or by="gene_type").
bindingSiteDefinednessPlot(bdsOut, by = "transcript")
Figure 15: bindingSiteDefinednessPlot
Density distribution of binding site definedness as percent bound grouped by transcript region.
3.4 Exporting your results
For proper integration with any other downstream analysis and application we provide a set of export functionalities that allow to quickly and easily retrieve any type of result.
3.4.1 For additional analysis in R/Bioconductor
If one stays inside the R/ Bioconductor environment one can simply extract the final binding sites as GenomicRanges, which enables full functionality that comes with this powerful data class. As indicated by the print all information from each processing step is kept in the meta data of the granges object. This makes it easy to combine your analysis results with orthogonal data and projects.
getRanges(bdsOut)## GRanges object with 2017 ranges and 9 metadata columns:
## seqnames ranges strand | bsID bsSize currIdx
## <Rle> <IRanges> <Rle> | <character> <numeric> <integer>
## 1 chr22 11630138-11630142 + | BS1 5 1
## 2 chr22 11630413-11630417 + | BS2 5 2
## 3 chr22 15785546-15785550 + | BS3 5 3
## 4 chr22 15786268-15786272 + | BS4 5 4
## 5 chr22 15786287-15786291 + | BS5 5 5
## ... ... ... ... . ... ... ...
## 2188 chr22 50626769-50626773 - | BS2368 5 2013
## 2190 chr22 50769684-50769688 - | BS2370 5 2014
## 2191 chr22 50776992-50776996 - | BS2371 5 2015
## 2192 chr22 50777869-50777873 - | BS2372 5 2016
## 2193 chr22 50783298-50783302 - | BS2373 5 2017
## geneID geneName geneType transcriptRegion score
## <character> <character> <character> <character> <numeric>
## 1 <NA> <NA> Intergenic OTHER 334.1870
## 2 <NA> <NA> Intergenic OTHER 77.5833
## 3 ENSG00000206195.11 DUXAP8 lncRNA INTRON 15.2087
## 4 ENSG00000206195.11 DUXAP8 lncRNA INTRON 16.5390
## 5 ENSG00000206195.11 DUXAP8 lncRNA INTRON 13.8455
## ... ... ... ... ... ...
## 2188 ENSG00000100299.18 ARSA protein_coding INTRON 37.1189
## 2190 ENSG00000079974.18 RABL2B protein_coding INTRON 20.3594
## 2191 ENSG00000079974.18 RABL2B protein_coding INTRON 37.5507
## 2192 ENSG00000079974.18 RABL2B protein_coding INTRON 22.7803
## 2193 ENSG00000079974.18 RABL2B protein_coding INTRON 16.2296
## signalToFlankRatio
## <numeric>
## 1 0.891358
## 2 0.826923
## 3 0.612903
## 4 0.781250
## 5 0.372881
## ... ...
## 2188 0.885714
## 2190 0.892857
## 2191 0.636364
## 2192 0.810811
## 2193 0.814815
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths3.4.2 Export as UCSC BED
For visual inspection and integration with further data-sets results can be easily exported in the widely used .bed format. This offers a quick way for manual inspection of the resulting binding sites in tools like IGV or UCSC genome browser.
exportToBED(bdsOut, con = "./myResults.bed")3.4.3 Export genes and feature lists
Gene targets from the target gene assignment can be exported to either csv or xslx grouped by geneType or transcriptRegion. Genes can additionally be sorted by the number of overlapping binding sites or by binding site strength score.
exportTargetGenes(bdsOut, format = "xslx", split = "transcriptRegion")3.4.4 Export iCLIP signal
To export iCLIP coverage directly we do not provide a dedicated function, but rather we use the export function from the rtracklayer package. For details see the section Exporting iCLIP signal.
4 Diagnostic coverage polots
The analysis workflow described in the Standard workflow section contains all steps for a comprehensive transcriptome-wide binding site analysis. However in some instances it might be useful to drill these analysis down to specific binding sites. In the following chapter we provide some useful ideas and functions when this could be useful and what type of questions could be addressed.
4.1 Visualize the iCLIP coverage
To visualize the iCLIP signal (aka coverage) on a specific binding site, we provide the bindingSiteCoveragePlot() function. It takes a reference index for the binding site and then plots the coverage as bars in a defined range around a selected binding site. The function is based on the Gviz package. The plotIdx indicates which range should be used as center, the flankPos parameter allows to zoom in and out. In addition to the selected range, also all other ranges which fall into the selected window will be shown.
To specifically assess for example the reproducibility on a particular binding site, one could plot the coverage from each replicate on that site. The plot below shows the coverage from each replicate on an exemplaric binding site.
bindingSiteCoveragePlot(bdsOut, plotIdx = 8, flankPos = 100, autoscale = TRUE)
Figure 16: bindingSiteCoveragePlot
Coverage plot on replicate level.
Sometimes it can also be useful to merge all replicates into a single combined coverage. This can be done by setting mergeReplicates=TRUE.
4.2 Trace back a binding site
A common case that makes use of this function is when one wants to see why a particular binding site is lost after a certain filtering step. Here, we look at a binding site that was filtered out from the final object by the reproducibility filter. Doing so, we can visually confirm that binding sites 5 and 6 were correctly removed by the reproducibility filter function.
rangesBeforeRepFilter = getRanges(bds)
rangesAfterRepFilter = getRanges(bdsOut)
idx = which(!match(rangesBeforeRepFilter, rangesAfterRepFilter, nomatch = 0) > 0)
rangesRemovedByFilter = rangesBeforeRepFilter[idx]
bdsRemovedRanges = setRanges(bds, rangesRemovedByFilter)
bindingSiteCoveragePlot(bdsRemovedRanges, plotIdx = 2, flankPos = 50)
We could also look at the final binding site definition and see how they were derived from the initial PureCLIP sites. To achieve this, we make use of the customRange slot to add these sites at the bottom of the coverage plot.
pSites = getRanges(bds)
bindingSiteCoveragePlot(bdsOut, plotIdx = 8, flankPos = 20, autoscale = TRUE,
customRange = pSites, customRange.name = "pSites", shiftPos = -10)4.3 Gene wise coverage
Another use case would be to check the coverage on some example genes, or as we do here on the binding site with the highest number of crosslinks:
bindingSiteCoverage = coverageOverRanges(bdsOut, returnOptions = "merge_positions_keep_replicates")
idxMaxCountsBs = which.max(rowSums(as.data.frame(mcols(bindingSiteCoverage))))
bindingSiteCoveragePlot(bdsOut, plotIdx = idxMaxCountsBs, flankPos = 100, mergeReplicates = FALSE, shiftPos = 50)It is also possible to anchor the plot on any other GenomicRange. Here, we take annotated CDS regions and ask for the one with the most overlapping binding sites. We then use this ranges as center for the plot and further zoom in to a particular range. We then make use of the customRange slot to re-include the binding site ranges as additional annotation shown underneath the signal. Additionally, one could also add a custom annotation track in the form of a GenomicRanges or TxDB object.
bdsCDS = setRanges(bdsOut, regions$CDS)
cdsWithMostBs = which.max(countOverlaps(regions$CDS, getRanges(bdsOut)))
bindingSiteCoveragePlot(bdsCDS, plotIdx = cdsWithMostBs, showCentralRange = FALSE,
flankPos = 250, shiftPos = 50, mergeReplicates = TRUE,
highlight = FALSE, customRange = getRanges(bdsOut),
customAnnotation = regions$CDS)5 Variations of the standard workflow
5.1 Normalize transcript regions
When assessing the number of binding sites per transcript region, it is common sense that more binding sites will map to intron as appose to for example 5’UTRs due to the size difference of these regions. Here we describe how one can be used to normalize for this effect.
The normalization scheme we provide is based on the hosting region length of each binding site. That means the number of binding sites per region can be divided by either the sum, mean or median of the hosting region length from all binding sites (see normalize.factor when using transcriptRegionSpectrumPlot()). To avoid biases by very short or long transcript regions an upper and lower boundary can be set (see normalize.exclude.upper and normalize.exclude.lower when using assignToTranscriptRegions()).
Here we show the transcript region spectrum on a percentage scale once normalized and once un-normalized.
transcriptRegionSpectrumPlot(bdsOut, normalize = FALSE, values = "percentage")transcriptRegionSpectrumPlot(bdsOut, normalize = TRUE, values = "percentage", normalize.factor = "median")5.2 How to manually test different binding site sizes
In some instances the estimateBsWidth() function used in the BSFind() wrapper might not lead to optimal results. This can be the case if the starting data is of below average quality with very few crosslink sites to begin with. Here manual binding site profiling can be used to achieve the best possible result given the input data.
To guide the choice of a good width, we recommend to explicitly compute binding sites for a variety of width. Here we selected 3, 7, 19 and 29nt and manually execute makeBindingSites()14 Note: Notice that we do not subset on a specific chromosome, but rather include all ranges to avoid any local pitfalls. .
# compute binding sites
bds1 <- makeBindingSites(object = bds, bsSize = 3)
bds2 <- makeBindingSites(object = bds, bsSize = 9)
bds3 <- makeBindingSites(object = bds, bsSize = 19)
bds4 <- makeBindingSites(object = bds, bsSize = 29)
# summarize in list
l = list(`1. bsSize = 3` = bds1, `2. bsSize = 9` = bds2,
`3. bsSize = 19` = bds3, `4. bsSize = 29` = bds4)The combined coverage over these different binding sites can help to quickly judge if the selected size does fit the data appropriately (rangeCoveragePlot()). Here, each plot is centered around the binding site’s midpoint and the computed width is indicated by the gray frame.
rangeCoveragePlot(l, width = 20, show.samples = TRUE, subset.chromosome = "chr22") 
Figure 17: rangeCoveragePlot
Crosslink coverage summarized at potential binding sites, with current size given by the grey area.
In our example, size = 3 appears too small, since not all of the relevant peak signal seems to be captured. On the contrary, size = 29 appears extremely large. Here, we decided for size = 9 because it seems to capture the central coverage peak best.
5.3 If you already know your binding site size
In case you already know what binding site size you want to use BSFind() can be called directly with that input size. This is usually the case if one has prior knowledge from orthogonal experiments or if the present data-set should be fix to the same size as another data-set for comparison. With a fixed binding site at hand we can call BSFind() with that explicit size to avoid estimation and use our manual input instead15 Note: We also set cutoff.geneWiseFilter=0 to turn off gene-wise filtering..
bdsManual = BSFind(object = bds, anno.genes = gns, anno.transcriptRegionList = regions, bsSize = 9, cutoff.geneWiseFilter = 0)If you are not sure how to choose an appropriate binding site size for your data-set, see Standard workflow which makes use of the estimateBsWidth() function, or see the chapter How to manually test different binding site sizes, to manually select a size value.
5.4 Call utility functions seperately
In the case on does not want to work with BSFind() as main wrapper function, all core utility function can be called separately. In our example from above where we manually decided on a binding site width of 9nt we could also call makeBindingSites() directly. This avoids turning off certain default options and will result in the most basic result possible, a simple set of merged binding site.
bdsSimple = makeBindingSites(object = bds, bsSize = 9)5.5 Construct your own pipeline
If one is using more than one utility function with some pre-defined input options it is recommended to construct a small custom pipeline. This greatly increase readability and reproducibility of the code. Here we extend our binding site merging to 9nt from above by a pre-filter and add another layer of post-processing to it.
customBSFind <- function(object) {
this.obj = pureClipGlobalFilter(object)
this.obj = makeBindingSites(this.obj, bsSize = 9)
this.obj = calculateSignalToFlankScore(this.obj)
return(this.obj)
}
bdsSimple = customBSFind(bds)Processing steps in custom pipelines can also be visualized with the flow chart function.
5.6 Work without a gene annotation
Setting up a custom pipeline can also be needed when one does not have any supporting gene annotation at hand. In those cases a simplified workflow pipeline can be defined that still allows binding site definition. To achieve this we simply combine all processing functions that do not require an explicit annotation into a new reduced wrapper16 Note: We highly recommend using an adequate gene annotation since it improves the quality of the analysis..
noAnnotationBSFind <- function(object) {
this.obj = pureClipGlobalFilter(object)
this.obj = makeBindingSites(this.obj, bsSize = 9)
this.obj = reproducibilityFilter(this.obj)
this.obj = annotateWithScore(this.obj, getRanges(object))
this.obj = calculateSignalToFlankScore(this.obj)
return(this.obj)
}
bdsSimple = noAnnotationBSFind(bds)Since no gene or transcript level assignment was performed (assignToGenes() and assignToTranscriptRegions() was not run), the related diagnostic plots are not available. Yet the diagnostics for the functions we have called in noAnnotationBSFind() can be used to assess the performance of our analysis.
5.7 Reproducibility scatter before and after correction
In order to visualize how the reproducibility filtering function improves pairwise replicate reproducibility, the reproducibilityScatterPlot() function can be called before and after reproducibility filtering.
bds.before = makeBindingSites(bds, bsSize = 9)
reproducibilityScatterPlot(bds.before)bds.after = reproducibilityFilter(bds.before, minCrosslinks = 2)
reproducibilityScatterPlot(bds.after)5.8 Use a different set of transcript regions
When assigning binding sites to transcript regions, the assignToTranscriptRegions() utility function takes the annotation ranges as a GRangesList input. Then binding sites are simply assigned to each list entry using the respective list name. This fact can be exploited to essentially use any kind of range set as input to assign binding site to any custom set of genomic ranges, while handling overlaps as it is described in assignToTranscriptRegions().
6 Additional functions
Besides the standard workflow and main processing functions a variety of further useful functions that come in handy when working with iCLIP data are provided to the user. In the following sections we demonstrate some these functionalities.
6.1 Subset data for faster iterations
Sub-setting a BSFinderData object can be useful in a variety of cases, e.g. for reducing the object size for faster parameter testing, limiting the analysis to some candidate genes etc. Here, we subset the object by a random set of 100 binding sites and plot their count distribution.
set.seed(1234)
bdsSub = bds[sample(seq_along(getRanges(bds)), 100, replace = FALSE)]
cov = coverageOverRanges(bdsSub, returnOptions = "merge_positions_keep_replicates")
df = mcols(cov) %>%
as.data.frame() %>%
pivot_longer(everything())
ggplot(df, aes(x = name, y = log2(value+1), fill = name)) +
geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
scale_fill_brewer(palette = "Greys") +
theme_bw() +
theme(legend.position = "none") +
labs(x = "Samples", y = "#Crosslinks (log2)")6.2 Merge replicate signal
Depending on the task at hand, one either wants to keep the iCLIP signal separated by replicates or merge the signal over the replicates (e.g. of the same condition). Merging signal can be done using the collapseReplicates() function. Doing so allows for example to identify the proportion of crosslink events that each sample contributes to the total of a binding site. Here, we did this for the first 100 binding sites. We sort all binding sites by their fraction and color the plot based on the replicate.
bdsMerge = collapseReplicates(bds)[1:100]
covTotal = coverageOverRanges(bdsMerge, returnOptions = "merge_positions_keep_replicates")
covRep = coverageOverRanges(bds[1:100], returnOptions = "merge_positions_keep_replicates")
df = cbind.data.frame(mcols(covTotal), mcols(covRep)) %>%
mutate(rep1 = round(`1_WT`/ WT, digits = 2) * 100,
rep2 = round(`2_WT`/ WT, digits = 2) * 100,
rep3 = round(`3_WT`/ WT, digits = 2) * 100,
rep4 = round(`4_WT`/ WT, digits = 2) * 100) %>%
tibble::rowid_to_column("BsID") %>%
dplyr::select(BsID, rep1, rep2, rep3, rep4) %>%
pivot_longer(-BsID) %>%
group_by(BsID) %>%
arrange(desc(value), .by_group = TRUE) %>%
mutate(name = factor(name, levels = name)) %>%
group_by(name) %>%
arrange(desc(value), .by_group = TRUE) %>%
mutate(BsID = factor(BsID, levels = BsID))
ggplot(df, aes(x = BsID, y = value, fill = name)) +
geom_col(position = "fill", width = 1) +
theme_bw() +
scale_fill_brewer(palette = "Set3") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = 7)) +
labs(x = "Binding site ID",
y = "Percentage",
fill = "Replicate"
) +
scale_y_continuous(labels = scales::percent)6.3 Exporting iCLIP signal
Similar to exporting the binding sites as BED file for external visualization, one can do the same with the iCLIP signal itself. Here we use the export function from the rtracklayer package. We could now first merge the signal from all replicates into a single combined signal. This signal can then be exported to .bigwig format for easy visualization in IGV or th UCSC genome browser.
sgn = getSignal(bds)
export(sgn$signalPlus$`1_WT`, con = "./WT_1_plus.bw", format = "bigwig")
export(sgn$signalMinus$`1_WT`, con = "./WT_1_minus.bw", format = "bigwig")bdsMerge = collapseReplicates(bds)
sgn = getSignal(bdsMerge)
export(sgn$signalPlus$WT, con = "./sgn_plus.bw", format = "bigwig")
export(sgn$signalPlus$WT, con = "./sgn_minus.bw", format = "bigwig")6.4 Calculate coverage
Each BSFDataSet contains the binding sites as ranges as well as the individual crosslinks per sample. With the clipCoverage() function one can easily compute all sorts of coverage over these ranges.
In the purest form this would result in a matrix with the number of columns being equal to the number of nucleotides in the binding sites (bsSize) and the number of rows being equal to the number of binding sites. If this matrix is computed for all samples the result is a list of matrices with the described dimensions.
To simplify this output three parameter options exist to merge either of the three dimensions (samples.merge, positions.merge, ranges.merge). Merging samples will combine the coverage signal from individual samples17 Notes: Samples can also be combined by condition if multiple conditions are present.. Merging positions combine all single nucleotide positions of the binding sites into a single value. Merging ranges will combine all ranges. For each merging operation one can choose between using the sum or mean. Depending on the shape of the result it can either be attached to the ranges present in the BSFDataSet which results in a granges type of output, or it can be returned as a data.frame.
cov = clipCoverage(bdsOut, ranges.merge = TRUE, samples.merge = FALSE, positions.merge = FALSE)
cov## GRanges object with 2017 ranges and 13 metadata columns:
## seqnames ranges strand | bsID bsSize currIdx
## <Rle> <IRanges> <Rle> | <character> <numeric> <integer>
## BS1 chr22 11630138-11630142 + | BS1 5 1
## BS2 chr22 11630413-11630417 + | BS2 5 2
## BS3 chr22 15785546-15785550 + | BS3 5 3
## BS4 chr22 15786268-15786272 + | BS4 5 4
## BS5 chr22 15786287-15786291 + | BS5 5 5
## ... ... ... ... . ... ... ...
## BS2368 chr22 50626769-50626773 - | BS2368 5 2013
## BS2370 chr22 50769684-50769688 - | BS2370 5 2014
## BS2371 chr22 50776992-50776996 - | BS2371 5 2015
## BS2372 chr22 50777869-50777873 - | BS2372 5 2016
## BS2373 chr22 50783298-50783302 - | BS2373 5 2017
## geneID geneName geneType transcriptRegion
## <character> <character> <character> <character>
## BS1 <NA> <NA> Intergenic OTHER
## BS2 <NA> <NA> Intergenic OTHER
## BS3 ENSG00000206195.11 DUXAP8 lncRNA INTRON
## BS4 ENSG00000206195.11 DUXAP8 lncRNA INTRON
## BS5 ENSG00000206195.11 DUXAP8 lncRNA INTRON
## ... ... ... ... ...
## BS2368 ENSG00000100299.18 ARSA protein_coding INTRON
## BS2370 ENSG00000079974.18 RABL2B protein_coding INTRON
## BS2371 ENSG00000079974.18 RABL2B protein_coding INTRON
## BS2372 ENSG00000079974.18 RABL2B protein_coding INTRON
## BS2373 ENSG00000079974.18 RABL2B protein_coding INTRON
## score signalToFlankRatio 1_WT 2_WT 3_WT 4_WT
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## BS1 334.1870 0.891358 55 98 132 76
## BS2 77.5833 0.826923 11 21 39 15
## BS3 15.2087 0.612903 0 6 8 5
## BS4 16.5390 0.781250 3 9 12 1
## BS5 13.8455 0.372881 1 5 14 2
## ... ... ... ... ... ... ...
## BS2368 37.1189 0.885714 3 7 17 4
## BS2370 20.3594 0.892857 2 9 12 2
## BS2371 37.5507 0.636364 2 9 9 8
## BS2372 22.7803 0.810811 4 8 13 5
## BS2373 16.2296 0.814815 4 5 8 5
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsHere we added the number of crosslinks per binding site and replicate to the granges object of the binding sites.
7 Differential binding analysis
In this section we go beyond basic binding site definition and demonstrate how binding sites of a given RBP can be compared between conditions. For example one might be interested in how the binding pattern on of an RBP changes upon a specific mutation or condition.
Comparing two sets of binding sites from different biological conditions to each other is not straight forward, since a change observed between any two binding sites can also be caused by expression level changes of the hosting transcript. For that reason the following computational approach will correct for this effect resulting in a log2 fold-change for each binding site that is corrected for a potential transcript expression level change.
7.1 Concept and idea
The main idea of the approach is to divide given iCLIP crosslinks into two bins for each gene. The first bin holds all crosslinks that are overlapping defined binding sites and thus can be attributed to the binding affinity of the RBP. We call this bin the binding-bin. The second bin holds all other crosslinks, which can be attributed to the expression level of the hosting RNA. We call this bin the background-bin.
We now make use of the condition specific changes in the background-bin to correct all fold-changes for each binding site in the binding-bin. This is done using the DESeq2 likelihood ratio test, by comparing a “full” model to a reduced “null” model.
Overview of differential binding model and workflow.
7.2 Preparation of the input data
In the most basic approach one wants to compute fold-changes for each binding site for the same RBP found in of two conditions. For the following example we name our conditions WT and KO.
7.2.1 Define binding sites for each condition
At first binding sites have to be defined for each condition separately. This ensures that regardless of sequencing depth or expression levels not binding site can be missed or masked by either condition 18 Note: This requires that both data sets need to be handled strict separately, from pre-processing to peak-calling and binding site definition..
Here we load two pre-computed data sets, each having two replicates for WT and two for the KO 19 Note: Both data-sets were artificially created from the same condition for demonstration purposes.. To learn how these sets can be generated from two individual data set, see the Starting from scratch section.
# make artifical data sets
myDataSets = makeExampleDataSets()
bds.wt = myDataSets$wt
bds.ko = myDataSets$ko
bds.wt## Dataset: Example.WT
## Object of class BSFDataSet
## #N Ranges: 2,522
## Width ranges: 5
## #N Samples: 2
## #N Conditions: 2bds.ko## Dataset: Example.KO
## Object of class BSFDataSet
## #N Ranges: 2,554
## Width ranges: 5
## #N Samples: 2
## #N Conditions: 27.2.2 Merge binding sites into a single object for testing
We follow up on the separate binding site definition by combining both binding site sets into a single set on which we then perform the differential binding testing. Merging two BSFDataSets can be done using the + operator as shown below or by using the combineBSF() function.
bds.comb = bds.wt + bds.ko
getRanges(bds.comb)## GRanges object with 5076 ranges and 6 metadata columns:
## seqnames ranges strand | bsID bsSize
## <Rle> <IRanges> <Rle> | <character> <integer>
## 1 chr22 11630138-11630142 + | BS1 5
## 2 chr22 11630413-11630417 + | BS2 5
## 3 chr22 15785523-15785527 + | BS3 5
## 4 chr22 15785540-15785544 + | BS4 5
## 5 chr22 15785546-15785550 + | BS5 5
## ... ... ... ... . ... ...
## 5072 chr22 50776987-50776991 - | BS5072 5
## 5073 chr22 50776992-50776996 - | BS5073 5
## 5074 chr22 50777821-50777825 - | BS5074 5
## 5075 chr22 50777869-50777873 - | BS5075 5
## 5076 chr22 50783298-50783302 - | BS5076 5
## geneID geneName geneType dataset
## <character> <character> <character> <character>
## 1 <NA> <NA> Intergenic Example.KO
## 2 <NA> <NA> Intergenic Example.KO
## 3 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## 4 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## 5 ENSG00000206195.11 DUXAP8 lncRNA Example.WT
## ... ... ... ... ...
## 5072 ENSG00000079974.18 RABL2B protein_coding Example.WT
## 5073 ENSG00000079974.18 RABL2B protein_coding Example.WT
## 5074 ENSG00000079974.18 RABL2B protein_coding Example.KO
## 5075 ENSG00000079974.18 RABL2B protein_coding Example.WT
## 5076 ENSG00000079974.18 RABL2B protein_coding Example.KO
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths7.3 Count matrix generation
To perform the differential testing we first need to extract crosslink counts for each binding site and background-bin per gene. It is all crosslinks that are attributed for the background-bin that are used for the expression change correction. This makes the differentiation of the iCLIP signal in binding-bin and background-bin one of the must critical steps.
To fine-tune this step a variety of options exist. First each binding site can be extend by an offset region. All crosslinks overlapping the offset will be removed. This means they are not counted towards the background-bin or the binding-bin. The same holds true for the blacklist-region which is an additional container which can be used to remove counts from certain ranges from the background-bin20 Note: The background counts per replicate is identical for binding sites on the same hosting gene..
Background-bin generation options.
7.3.1 Calculate binding sites and background counts
Calculating the background counts requires a gene annotation information to be present in the form of a GRanges object. For more details on how to generate this object from a .gtf/.gff3 file please see the section Create annotation for genes. The final counts are added to the meta columns of the binding site GRanges objects 21 Note: Binding sites that do not overlap a distinct gene may end with NA values as background counts and can be removed in the next step..
bds.diff = calculateBsBackground(bds.comb, anno.genes = gns)
getRanges(bds.diff)## GRanges object with 4961 ranges and 14 metadata columns:
## seqnames ranges strand | bsID bsSize
## <Rle> <IRanges> <Rle> | <character> <integer>
## BS3 chr22 15785523-15785527 + | BS3 5
## BS4 chr22 15785540-15785544 + | BS4 5
## BS5 chr22 15785546-15785550 + | BS5 5
## BS6 chr22 15785946-15785950 + | BS6 5
## BS7 chr22 15785962-15785966 + | BS7 5
## ... ... ... ... . ... ...
## BS5072 chr22 50776987-50776991 - | BS5072 5
## BS5073 chr22 50776992-50776996 - | BS5073 5
## BS5074 chr22 50777821-50777825 - | BS5074 5
## BS5075 chr22 50777869-50777873 - | BS5075 5
## BS5076 chr22 50783298-50783302 - | BS5076 5
## geneID geneName geneType dataset
## <character> <character> <character> <character>
## BS3 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS4 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS5 ENSG00000206195.11 DUXAP8 lncRNA Example.WT
## BS6 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS7 ENSG00000206195.11 DUXAP8 lncRNA Example.WT
## ... ... ... ... ...
## BS5072 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5073 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5074 ENSG00000079974.18 RABL2B protein_coding Example.KO
## BS5075 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5076 ENSG00000079974.18 RABL2B protein_coding Example.KO
## counts.bs.1_WT counts.bs.2_WT counts.bs.3_KO counts.bs.4_KO
## <numeric> <numeric> <numeric> <numeric>
## BS3 2 5 6 2
## BS4 3 1 6 2
## BS5 0 6 8 5
## BS6 1 1 5 0
## BS7 0 2 8 1
## ... ... ... ... ...
## BS5072 0 1 7 1
## BS5073 2 9 9 8
## BS5074 2 2 5 3
## BS5075 4 8 13 5
## BS5076 4 5 8 5
## counts.bg.1_WT counts.bg.2_WT counts.bg.3_KO counts.bg.4_KO
## <numeric> <numeric> <numeric> <numeric>
## BS3 11 71 74 22
## BS4 11 71 74 22
## BS5 11 71 74 22
## BS6 11 71 74 22
## BS7 11 71 74 22
## ... ... ... ... ...
## BS5072 11 46 71 27
## BS5073 11 46 71 27
## BS5074 11 46 71 27
## BS5075 11 46 71 27
## BS5076 11 46 71 27
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths7.3.2 Filter binding site and background counts
Depending on the crosslink signal at hand some genes might not be suitable for differential testing. This is for example when there is overall not enough signal per gene, if the distribution between conditions is heavily tailored towards one side or if there is a strong imbalance between the background-bin and binding-bin per gene. The following function can be used to remove such cases to create a more reliable test set22 Note: The plotBsBackgroundFilter() function can be used to visualize each filtering option..
bds.diff = filterBsBackground(bds.diff)## Warning in filterBsBackground(bds.diff): Found 34 genes with less total counts than 100.
## Removing a total of 34 genes and 39 binding sites.## Warning in filterBsBackground(bds.diff): Found 12 genes with bs to bg ratio not meeting thresholds.
## Removing a total of 12 genes and 30 binding sites.## Warning in filterBsBackground(bds.diff): Found 0 genes with condition balance ratio not meeting thresholds.
## Removing a total of 0 genes and 0 binding sites.7.4 Perform differential testing
Finally differential testing can be carried out using the calculateBsFoldChange() function. This function computes fold-changes for each binding site using the background-bin for correction and the likelihood ratio test for significance testing (for details on these test see the DESeq2 user manual). Additionally, gene-level fold-changes are computed for each hosting gene using the standard Wald test (again see the DESeq2 user manual for further details)23 Note: Depending on the data one might have to use the removeNA argument to remove edge cases where no background-bin counts could be computed. It is worth to manually look at such cases..
bds.diff = calculateBsFoldChange(bds.diff)
getRanges(bds.diff)## GRanges object with 4892 ranges and 26 metadata columns:
## seqnames ranges strand | bsID bsSize
## <Rle> <IRanges> <Rle> | <character> <integer>
## BS3 chr22 15785523-15785527 + | BS3 5
## BS4 chr22 15785540-15785544 + | BS4 5
## BS5 chr22 15785546-15785550 + | BS5 5
## BS6 chr22 15785946-15785950 + | BS6 5
## BS7 chr22 15785962-15785966 + | BS7 5
## ... ... ... ... . ... ...
## BS5072 chr22 50776987-50776991 - | BS5072 5
## BS5073 chr22 50776992-50776996 - | BS5073 5
## BS5074 chr22 50777821-50777825 - | BS5074 5
## BS5075 chr22 50777869-50777873 - | BS5075 5
## BS5076 chr22 50783298-50783302 - | BS5076 5
## geneID geneName geneType dataset
## <character> <character> <character> <character>
## BS3 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS4 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS5 ENSG00000206195.11 DUXAP8 lncRNA Example.WT
## BS6 ENSG00000206195.11 DUXAP8 lncRNA Example.KO
## BS7 ENSG00000206195.11 DUXAP8 lncRNA Example.WT
## ... ... ... ... ...
## BS5072 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5073 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5074 ENSG00000079974.18 RABL2B protein_coding Example.KO
## BS5075 ENSG00000079974.18 RABL2B protein_coding Example.WT
## BS5076 ENSG00000079974.18 RABL2B protein_coding Example.KO
## counts.bs.1_WT counts.bs.2_WT counts.bs.3_KO counts.bs.4_KO
## <numeric> <numeric> <numeric> <numeric>
## BS3 2 5 6 2
## BS4 3 1 6 2
## BS5 0 6 8 5
## BS6 1 1 5 0
## BS7 0 2 8 1
## ... ... ... ... ...
## BS5072 0 1 7 1
## BS5073 2 9 9 8
## BS5074 2 2 5 3
## BS5075 4 8 13 5
## BS5076 4 5 8 5
## counts.bg.1_WT counts.bg.2_WT counts.bg.3_KO counts.bg.4_KO
## <numeric> <numeric> <numeric> <numeric>
## BS3 11 71 74 22
## BS4 11 71 74 22
## BS5 11 71 74 22
## BS6 11 71 74 22
## BS7 11 71 74 22
## ... ... ... ... ...
## BS5072 11 46 71 27
## BS5073 11 46 71 27
## BS5074 11 46 71 27
## BS5075 11 46 71 27
## BS5076 11 46 71 27
## bs.baseMean bs.log2FoldChange bs.lfcSE bs.stat bs.pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## BS3 18.06576 0.00201385 0.814051 6.13075e-06 0.998024
## BS4 17.21228 0.49510922 1.263454 1.48423e-01 0.700047
## BS5 19.86013 1.00907610 1.040451 9.79359e-01 0.322357
## BS6 8.30315 0.92938363 1.450254 4.27508e-01 0.513214
## BS7 10.09457 1.87552092 1.359956 2.17815e+00 0.139982
## ... ... ... ... ... ...
## BS5072 8.45359 2.1633690 1.395628 2.9873966 0.083915
## BS5073 32.75253 -0.0813876 0.622872 0.0170241 0.896190
## BS5074 16.31571 0.2781398 0.929807 0.0907884 0.763177
## BS5075 34.75249 -0.1357579 0.602860 0.0505005 0.822195
## BS5076 29.04008 -0.1901605 0.684449 0.0765903 0.781972
## bs.padj bg.baseMean bg.log2FoldChange bg.lfcSE bg.stat bg.pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## BS3 0.999250 34.6044 -0.256956 0.464566 -0.55311 0.580188
## BS4 0.936368 34.6044 -0.256956 0.464566 -0.55311 0.580188
## BS5 0.811975 34.6044 -0.256956 0.464566 -0.55311 0.580188
## BS6 0.893694 34.6044 -0.256956 0.464566 -0.55311 0.580188
## BS7 0.621117 34.6044 -0.256956 0.464566 -0.55311 0.580188
## ... ... ... ... ... ... ...
## BS5072 0.511223 31.4992 0.251674 0.466657 0.539312 0.589672
## BS5073 0.983324 31.4992 0.251674 0.466657 0.539312 0.589672
## BS5074 0.950474 31.4992 0.251674 0.466657 0.539312 0.589672
## BS5075 0.966006 31.4992 0.251674 0.466657 0.539312 0.589672
## BS5076 0.956831 31.4992 0.251674 0.466657 0.539312 0.589672
## bg.padj
## <numeric>
## BS3 0.995469
## BS4 0.995469
## BS5 0.995469
## BS6 0.995469
## BS7 0.995469
## ... ...
## BS5072 0.995469
## BS5073 0.995469
## BS5074 0.995469
## BS5075 0.995469
## BS5076 0.995469
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsResulting fold-changes and P values are again added to the meta columns of the binding sites (GRanges format). As a quick visualization of the results the functions plotBsMA() and plotBsVolcano() can be used, each of which can produce a plot either for the binding sites (what = "bs") or for the background (what = "bg").
plotBsMA(bds.diff, what = "bs")
Figure 18: plotBsMA
MA plot for binding site changes.
7.5 Differential binding variations
7.5.1 Starting from scratch
The following code chunks show how one can start fully from scratch with the binding site definition for the purpose of a downstream differential binding analysis. In the following example we will demonstrate the binding site definition for the conditions WT and KO.
At first one has to construct a BSFDataSet for each condition with the crosslinks form the peak-calling step (PureCLIP output). This serves as input for the binding sites definition with BSFind() (for further details see the Performing the main analysis section). As a result one gets a set of binding sites. This has to be done for each condition (in our example these are WT and KO)24 Note: It is desired that the same binding site size is used for both conditions, which can require setting the bsSize argument explicitly when calling BSFind()..
############################
# Construct WT binding sites
############################
# Locate peak calling result
peaks.wt = "./myFolder/wt/pureclip.bed"
peaks.wt = import(con = peaks.wt, format = "BED", extraCols=c("additionalScores" = "character"))
peaks.wt = keepStandardChromosomes(peaks.wt, pruning.mode = "coarse")
# Locate bigwig files
clipFiles = "./myFolder/wt/bigwig/"
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFiles = clipFiles[grepl("WT", clipFiles)]
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
name = "Example.WT", id = c(1:2),
condition = factor(c("WT", "WT"), levels = c("WT")),
clPlus = clipFilesP, clMinus = clipFilesM)
# Construct BSFDataSet object
bdsWT.initial = BSFDataSetFromBigWig(ranges = peaksInitial, meta = colData)
# Define binding sites
bdsWT = BSFind(bdsWT.initial, anno.genes = gns, anno.transcriptRegionList = trl)############################
# Construct KO binding sites
############################
# Locate peak calling result
peaks.ko = "./myFolder/ko/pureclip.bed"
peaks.ko = import(con = peaks.ko, format = "BED", extraCols=c("additionalScores" = "character"))
peaks.ko = keepStandardChromosomes(peaks.ko, pruning.mode = "coarse")
# Locate bigwig files
clipFiles = "./myFolder/ko/bigwig/"
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFiles = clipFiles[grepl("KO", clipFiles)]
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
name = "Example.KO", id = c(1:2),
condition = factor(c("KO", "KO"), levels = c("KO")),
clPlus = clipFilesP, clMinus = clipFilesM)
# Construct BSFDataSet object
bdsKO.initial = BSFDataSetFromBigWig(ranges = peaksInitial, meta = colData)
# Define binding sites
bdsKO = BSFind(bdsKO.initial, anno.genes = gns, anno.transcriptRegionList = trl)7.5.2 Starting from existing binding sites
Sometimes one already has a set of binding sites computed and just wants to perform the differential binding analysis part for the specific set at hand. In this case the pre-computed binding sites can be imported alongside the bigwig files from both conditions into a single BSFDataSet. This set can then directly be used for background generation (see section Count matrix generation).
# Locate bigwig files - KO
clipFilesKO = "./myFolder/ko/bigwig/"
clipFilesKO = list.files(clipFilesKO, pattern = ".bw$", full.names = TRUE)
clipFilesKO = clipFilesKO[grepl("KO", clipFilesKO)]
# Locate bigwig files - WT
clipFilesWT = "./myFolder/wt/bigwig/"
clipFilesWT = list.files(clipFilesWT, pattern = ".bw$", full.names = TRUE)
clipFilesWT = clipFilesWT[grepl("WT", clipFilesWT)]
# Merge both conditions
clipFiles = c(clipFilesWT, clipFilesKO)
# Split by strand
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
name = "Example.KO", id = c(1:4),
condition = factor(c("WT", "WT", "KO", "KO"), levels = c("WT", "KO")),
clPlus = clipFilesP, clMinus = clipFilesM)
# Construct BSFDataSet object from pre-defined binding sites
# -> myBindingSites must be a GRanges object
bds.comb = BSFDataSetFromBigWig(ranges = myBindingSites, meta = colData)7.5.3 How to deal with multiple comparisons?
In the case of a more complex experimental design that requires the comparison of more than two conditions, we recommend to compute as many pair-wise comparison as necessary. If one for example wants to incorporate an additional control condition (named CTRL) to the above setup, we would split the comparisons into three pair-wise designs (CTRL-WT, CTRL-KO and WT-KO). To ensure that all sets contain all binding sites found in either of the three conditions a merge over all three conditions has to be done prior to the testing.
Assuming that binding sites were defined for each condition, one would merge all binding sites doing the following:25 Note: This also merges the meta data which we have to revert for the specific comparison that we want to compute..
bds.complex = combineBSF(list(ctrl = bds.ctrl, wt = bds.wt, ko = bds.ko))Next all binding site ranges from this set can be extracted and used to create a new BSFDataSet object for each comparison that should be computed (here we compare WT-vs-KO). This is very similar to starting a differential binding analysis from an existing set of binding sites (see chapter Starting from existing binding sites).
# extract binding site ranges from complex merge
bindingSites.complex = getRanges(bds.complex)
# Locate bigwig files - KO
clipFilesKO = "./myFolder/ko/bigwig/"
clipFilesKO = list.files(clipFilesKO, pattern = ".bw$", full.names = TRUE)
clipFilesKO = clipFilesKO[grepl("KO", clipFilesKO)]
# Locate bigwig files - WT
clipFilesWT = "./myFolder/wt/bigwig/"
clipFilesWT = list.files(clipFilesWT, pattern = ".bw$", full.names = TRUE)
clipFilesWT = clipFilesWT[grepl("WT", clipFilesWT)]
# Merge both conditions
clipFiles = c(clipFilesWT, clipFilesKO)
# Split by strand
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
name = "Example.KO", id = c(1:2),
condition = factor(c("KO", "KO"), levels = c("KO")),
clPlus = clipFilesP, clMinus = clipFilesM)
# Construct BSFDataSet object from pre-defined binding sites
bds.comb = BSFDataSetFromBigWig(ranges = bindingSites.complex, meta = colData)7.5.4 Using the blacklist regions
In the case that the binding site definition was performed using a particularly high gene-wise level filter or if differential testing should be performed only on a subset of all defined binding sites, we recommend to use this information for the background generation by making use of the blacklist argument. This ensures that counts from all ranges that were at some point in the analysis identified as either a peak or a binding site will be removed from the background-bin.
8 Session info
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] dplyr_1.1.4 forcats_1.0.0
## [3] BindingSiteFinder_2.5.2 ComplexHeatmap_2.23.0
## [5] tidyr_1.3.1 ggplot2_3.5.1
## [7] rtracklayer_1.67.0 GenomicAlignments_1.43.0
## [9] Rsamtools_2.23.0 Biostrings_2.75.1
## [11] XVector_0.47.0 SummarizedExperiment_1.37.0
## [13] Biobase_2.67.0 MatrixGenerics_1.19.0
## [15] matrixStats_1.4.1 GenomicRanges_1.59.1
## [17] GenomeInfoDb_1.43.1 IRanges_2.41.1
## [19] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [21] generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] BiocIO_1.17.1 bitops_1.0-9 filelock_1.0.3
## [4] tibble_3.2.1 polyclip_1.10-7 XML_3.99-0.17
## [7] rpart_4.1.23 lifecycle_1.0.4 httr2_1.0.6
## [10] doParallel_1.0.17 lattice_0.22-6 ensembldb_2.31.0
## [13] MASS_7.3-61 ggdist_3.3.2 backports_1.5.0
## [16] magrittr_2.0.3 Hmisc_5.2-0 sass_0.4.9
## [19] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [22] Gviz_1.51.0 DBI_1.2.3 RColorBrewer_1.1-3
## [25] abind_1.4-8 zlibbioc_1.53.0 purrr_1.0.2
## [28] AnnotationFilter_1.31.0 biovizBase_1.55.0 RCurl_1.98-1.16
## [31] nnet_7.3-19 VariantAnnotation_1.53.0 tweenr_2.0.3
## [34] rappdirs_0.3.3 circlize_0.4.16 GenomeInfoDbData_1.2.13
## [37] svglite_2.1.3 codetools_0.2-20 DelayedArray_0.33.2
## [40] xml2_1.3.6 ggforce_0.4.2 tidyselect_1.2.1
## [43] shape_1.4.6.1 UCSC.utils_1.3.0 farver_2.1.2
## [46] viridis_0.6.5 BiocFileCache_2.15.0 base64enc_0.1-3
## [49] jsonlite_1.8.9 GetoptLong_1.0.5 Formula_1.2-5
## [52] iterators_1.0.14 systemfonts_1.1.0 foreach_1.5.2
## [55] tools_4.5.0 progress_1.2.3 Rcpp_1.0.13-1
## [58] glue_1.8.0 gridExtra_2.3 SparseArray_1.7.2
## [61] DESeq2_1.47.1 xfun_0.49 distributional_0.5.0
## [64] withr_3.0.2 BiocManager_1.30.25 fastmap_1.2.0
## [67] GGally_2.2.1 latticeExtra_0.6-30 fansi_1.0.6
## [70] digest_0.6.37 R6_2.5.1 colorspace_2.1-1
## [73] Cairo_1.6-2 jpeg_0.1-10 dichromat_2.0-0.1
## [76] biomaRt_2.63.0 RSQLite_2.3.8 utf8_1.2.4
## [79] data.table_1.16.2 prettyunits_1.2.0 httr_1.4.7
## [82] htmlwidgets_1.6.4 S4Arrays_1.7.1 ggstats_0.7.0
## [85] pkgconfig_2.0.3 gtable_0.3.6 blob_1.2.4
## [88] htmltools_0.5.8.1 bookdown_0.41 ProtGenerics_1.39.0
## [91] clue_0.3-66 scales_1.3.0 kableExtra_1.4.0
## [94] png_0.1-8 knitr_1.49 rstudioapi_0.17.1
## [97] rjson_0.2.23 checkmate_2.3.2 curl_6.0.1
## [100] cachem_1.1.0 GlobalOptions_0.1.2 stringr_1.5.1
## [103] vipor_0.4.7 parallel_4.5.0 foreign_0.8-87
## [106] AnnotationDbi_1.69.0 ggrastr_1.0.2 restfulr_0.0.15
## [109] pillar_1.9.0 vctrs_0.6.5 dbplyr_2.5.0
## [112] cluster_2.1.6 beeswarm_0.4.0 htmlTable_2.4.3
## [115] evaluate_1.0.1 tinytex_0.54 GenomicFeatures_1.59.1
## [118] magick_2.8.5 locfit_1.5-9.10 cli_3.6.3
## [121] compiler_4.5.0 rlang_1.1.4 crayon_1.5.3
## [124] labeling_0.4.3 interp_1.1-6 ggbeeswarm_0.7.2
## [127] plyr_1.8.9 stringi_1.8.4 viridisLite_0.4.2
## [130] deldir_2.0-4 BiocParallel_1.41.0 munsell_0.5.1
## [133] lazyeval_0.2.2 Matrix_1.7-1 BSgenome_1.75.0
## [136] hms_1.1.3 bit64_4.5.2 KEGGREST_1.47.0
## [139] memoise_2.0.1 bslib_0.8.0 bit_4.5.0Bibliography
Busch, Anke, Mirko Brüggemann, Stefanie Ebersberger, and Kathi Zarnack. 2020. “iCLIP Data Analysis: A Complete Pipeline from Sequencing Reads to RBP Binding Sites.” Methods 178 (June): 49–62. https://doi.org/10.1016/j.ymeth.2019.11.008.
Krakau, Sabrina, Hugues Richard, and Annalisa Marsico. 2017. “PureCLIP: Capturing Target-Specific ProteinRNA Interaction Footprints from Single-Nucleotide CLIP-Seq Data.” Genome Biology 18 (1). https://doi.org/10.1186/s13059-017-1364-2.