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bioconductor-bindingsitefinder

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.

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BindingSiteFinder

Workflows

Standard Workflow

library(BindingSiteFinder)
library(txdbmaker)
library(rtracklayer)

# 1. Import crosslink sites
csFile <- system.file("extdata", "PureCLIP_crosslink_sites_examples.bed", package="BindingSiteFinder")
cs <- rtracklayer::import(con = csFile, format = "BED")

# 2. Create metadata table and construct BSFDataSet
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)
meta <- data.frame(
  id = 1:4,
  condition = factor(rep("WT", 4)),
  clPlus = clipFilesP,
  clMinus = clipFilesM
)
bds <- BSFDataSetFromBigWig(ranges = cs, meta = meta, silent = TRUE)

# 3. Build gene and transcript region annotations
annoDb <- txdbmaker::makeTxDbFromGFF(file = "gencode_v37_annotation.gff3", format = "gff3")
gns <- genes(annoDb)
regions <- GRangesList(
  CDS = cds(annoDb),
  Intron = unlist(intronsByTranscript(annoDb)),
  UTR3 = unlist(threeUTRsByTranscript(annoDb)),
  UTR5 = unlist(fiveUTRsByTranscript(annoDb))
)

# 4. Run BSFind wrapper
bdsOut <- BSFind(
  object = bds,
  anno.genes = gns,
  anno.transcriptRegionList = regions,
  est.subsetChromosome = "chr22",
  veryQuiet = TRUE
)

# 5. Export results
exportToBED(bdsOut, con = "./myBindingSites.bed")

Input/Output Note: Inputs a BSFDataSet and genomic annotations; outputs a refined BSFDataSet with defined binding sites and exports them to a BED file.

Differential Binding Analysis

library(BindingSiteFinder)
library(txdbmaker)

# 1. Load Gene Annotations and Transcript Regions
annoDb <- txdbmaker::makeTxDbFromGFF(file = "gencode_v37_annotation.gff3", format = "gff3")
gns <- genes(annoDb)
regions <- GRangesList(
  CDS = cds(annoDb),
  Intron = unlist(intronsByTranscript(annoDb)),
  UTR3 = unlist(threeUTRsByTranscript(annoDb)),
  UTR5 = unlist(fiveUTRsByTranscript(annoDb))
)

# 2. Define binding sites for a condition
bdsOut <- BSFind(
  object = bds, 
  anno.genes = gns, 
  anno.transcriptRegionList = regions, 
  est.subsetChromosome = "chr22"
)

Input/Output Note: Inputs a BSFDataSet and genomic annotations; outputs a BSFDataSet with defined binding sites prepared for downstream differential testing.

When to Use

To define precise, equally sized RBP binding sites from genome-wide iCLIP coverage and peak calling results (e.g., PureCLIP crosslink sites).
To filter, merge, and check reproducibility of crosslink sites across replicates using BSFind().
To annotate binding sites with genomic features such as genes and transcript regions (CDS, Introns, UTRs) using genes(), cds(), and intronsByTranscript().

When NOT to Use

For analyzing RNA-seq differential expression or splicing directly without iCLIP/CLIP-seq data (use DESeq2 or dexseq instead).
For peak calling directly from BAM files (use peak callers like PureCLIP first, then import results into BindingSiteFinder).

Data Requirements

Crosslink sites as a GRanges object (typically single-nucleotide wide, e.g., imported from PureCLIP BED files).
Replicate-specific coverage data in strand-specific BigWig files (clPlus and clMinus).
Gene annotations as a GRanges object and transcript regions as a GRangesList (e.g., CDS, Introns, 3' UTR, 5' UTR).

Key Parameters

object: A BSFDataSet containing crosslink ranges and metadata.
anno.genes: A GRanges object containing gene annotations.
anno.transcriptRegionList: A GRangesList containing transcript region annotations.
est.subsetChromosome ("chr22"): Character vector specifying which chromosome to use for estimating binding site width.
veryQuiet (FALSE): Logical indicating whether to suppress progress messages.
est.maxBsWidth (29): Numeric specifying the maximum binding site width to test during estimation.

Best Practices

Pre-filter crosslink sites with low scores (e.g., removing the lowest 1% of PureCLIP scores) using pureClipGlobalFilterPlot() to assess the cutoff.
Use estimateBsWidthPlot() to evaluate the optimal binding site width based on the signal-to-flank ratio.
Enforce reproducibility across replicates by setting the nReps parameter (typically N-1 replicates) and verifying with reproducibilityScatterPlot().

Common Pitfalls

Pitfall: Crosslink sites overlapping multiple genes causing inflated site counts. Fix: Set overlaps = "keepSingle" in BSFind() to reduce overlapping loci to a single instance.
Pitfall: Low reproducibility across replicates due to library size differences. Fix: Use reproducibilityFilterPlot() to inspect and adjust the replicate-specific crosslink threshold.

Alternatives

csaw for window-based differential binding analysis of ChIP-seq/ATAC-seq.
macsr for peak calling on enrichment data.
CLIPreg for integrating CLIP-seq with RNA-seq.

Citations

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. doi:10.1016/j.ymeth.2019.11.008.

References

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