# Bioconductor Bindingsitefinder

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-bindingsitefinder -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-bindingsitefinder -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-bindingsitefinder -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-bindingsitefinder -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# BindingSiteFinder

## Workflows

### Standard Workflow

```r
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

```r
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))
)

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/transcriptomics/bindingsitefinder/SKILL.md