🧬 Foundations of Genomic Data Handling in R – Post 4: IRanges Link to heading

🚀 What is IRanges and Why Should You Care? Link to heading

If you’re working with genomic intervals in R — whether it’s genes, exons, or ChIP-Seq peaks — the IRanges class is where it all begins. It’s the foundation for more complex structures like GRanges and SummarizedExperiment, and it powers nearly every interval-based operation in Bioconductor.

IRanges represents integer intervals — typically as genomic ranges — in a memory-efficient, vectorized format. At its core, each range consists of:

  • A start position
  • An end position
  • A width (derived as end - start + 1)

⚙️ Creating IRanges Objects Link to heading

You can construct an IRanges object using the IRanges() function from the IRanges package:

library(IRanges)

ir <- IRanges(start = c(1, 4, 10), end = c(3, 7, 12))
ir

Output:

IRanges of length 3
    start end width
[1]     1   3     3
[2]     4   7     4
[3]    10  12     3

These ranges could represent, for example, genomic features, coverage blocks, or transcription factor binding sites.

🛠 Accessor Functions Link to heading

You can retrieve individual components of the ranges with:

start(ir)   # Start positions
end(ir)     # End positions
width(ir)   # Widths of the ranges
rev(ir)     # Reversed IRanges

These simple accessors enable flexible manipulation and exploration of ranges.

🔍 Core IRanges Operations Link to heading

IRanges is incredibly powerful when performing set-like operations on ranges.

🔄 reduce() Link to heading

Combines overlapping or adjacent ranges into one:

reduce(ir)

✂️ disjoin() Link to heading

Splits ranges into the smallest disjoint pieces:

disjoin(ir)

🔎 findOverlaps() Link to heading

Finds all overlapping intervals between two IRanges:

query <- IRanges(start = c(2, 8), end = c(5, 11))
findOverlaps(query, ir)

🔢 countOverlaps() Link to heading

Counts how many overlaps each query range has:

countOverlaps(query, ir)

💡 IRanges + Rle = Power Link to heading

In Post 2, we covered the Rle class, which is frequently used in tandem with IRanges to represent things like:

  • Coverage vectors
  • Masking regions
  • Strand-specific read counts

This synergy allows efficient manipulation of large-scale data with memory-saving tricks.

coverage(ir)  # Returns an Rle vector

📊 Real-World Example: Overlapping Peaks and Genes Link to heading

Imagine you have: - A BED file of ChIP-Seq peaks - A GTF file of gene annotations

You can convert both to IRanges and use:

findOverlaps(peaks_ir, genes_ir)

And just like that, you know which genes are near your peaks 🔍

This logic scales — even across millions of genomic intervals.

🔮 Why IRanges Matters Link to heading

  • 📐 Compact and elegant representation of intervals
  • ⚡ Enables fast, vectorized overlap calculations
  • 🧱 Foundation of GRanges, GRangesList, and interval trees
  • 🔗 Essential for genomic operations like coverage, tiling, masking, annotation, and interval querying

Whether you’re building pipelines, developing packages, or exploring your own genomic data, IRanges is indispensable.

🧬 What’s Next? Link to heading

Next up: GRanges — where we bring chromosomes and strands into play. This will bridge your understanding of intervals with full genomic coordinates.

Stay tuned! 🚀

💬 How Are You Using IRanges? Link to heading

Have you used IRanges in your own workflows? Are there tricks you’ve learned or challenges you’ve faced? Drop your experiences below 👇

#Bioinformatics #RStats #IRanges #GenomicData #Bioconductor #ComputationalBiology #Transcriptomics #NGS #Genomics #DataStructures #GRanges #PeakCalling #ChIPSeq