Get Started with glyread

Glycomics and glycoproteomics data find their home in GlycomicSE and GlycoproteomicSE objects from glyexp—a tidy, structured format designed specifically for glycobiology workflows. Working with glycopeptide identification tools like pGlyco3 or MSFragger-Glyco? You can seamlessly import your results into GlycomicSE and GlycoproteomicSE objects with just a few lines of code using glyread.

One function, two files — that’s it

Getting started is straightforward. First, pick the function that matches your identification software. glyread currently plays nicely with these popular tools:

  • Byonic (quantification with Byologic): read_byonic_byologic()
  • Byonic (quantification with pGlycoQuant): read_byonic_pglycoquant()
  • Peaks GlycanFinder: read_glycan_finder()
  • Glyco-Decipher: read_glyco_decipher()
  • MSFragger-Glyco: read_msfragger()
  • pGlyco3 (built-in quantification): read_pglyco3()
  • pGlyco3 (quantification with pGlycoQuant): read_pglyco3_pglycoquant()
  • StrucGP (no quantification): read_strucgp()

Next, gather your two input files.

File 1: Results file This is the output from your identification software. Each read_*() function expects a specific file format, so check the function documentation to ensure you’re selecting the right one.

File 2: Sample information (CSV) A simple two-column table that tells glyread about your experimental design:

  • sample: Sample names as they appear in your results file (order is flexible)
  • group: Experimental conditions, treatments, or groupings (this is the recommended column name, but you have some flexibility here)

Pro tip: Quality control samples should be labeled “QC” in the group column — this helps downstream analysis recognize them appropriately.

Load your data

With your files ready, importing data is a one-liner. Here’s a practical example: suppose you used pGlyco3 for identification and pGlycoQuant for quantification, with results in pglyco3_result.csv and sample details in samples.csv:

se <- read_pglyco3_pglycoquant("pglyco3_result.csv", sample_info = "samples.csv")

That’s it — your data is now ready for analysis in a GlycoproteomicSE object.

What’s next?

Each read_*() function has its own quirks and options — file format variations, optional parameters, and output customizations. Check the function-specific documentation with ?read_pglyco3 (or whichever function matches your workflow) to fine-tune your data import.

Happy glyco-analyzing! 🍬