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Getting Started with glymotif

glymotif.Rmd

What is a Glycan Motif? 🧬

Imagine you’re looking at a complex glycan structure—those intricate branched molecules that decorate your cells. Hidden within these molecular architectures are recurring patterns called “motifs.” Think of them as the molecular equivalent of architectural motifs: recognizable design elements that appear across different buildings (or in this case, different glycans).

A glycan motif is simply a substructure that appears in multiple glycans. (Don’t confuse this with protein motifs—we’re talking about carbohydrates here! 🍭) Some famous examples include the N-glycan core, Lewis X antigen, and the Tn antigen.

Why Should You Care? 🤔

Here’s where it gets exciting: these motifs aren’t just decorative—they’re functional. They determine how cells interact, how pathogens bind, and how your immune system recognizes friend from foe.

This package, glymotif, is your computational microscope 🔬 for advanced glycan motif analysis. It helps you answer two fundamental questions:

  • Does this glycan contain a specific motif?
  • How many times does this motif appear?

The best part? ✨ Everything works with vectors of glycans, so you can analyze hundreds or thousands at once.

Important note: This package builds on the powerful glyrepr package. If you haven’t used it before, we highly recommend checking out its introduction first.

A Quick Challenge 🧩

Let’s start with a visual puzzle. Can you tell if the glycan on the left contains the motif on the right?

If you said “yes,” congratulations—you have a keen eye! 👀 But what if I gave you 500 glycans and 20 motifs to check? That’s where glymotif becomes indispensable.

Let’s see it in action using IUPAC-condensed notation (the standard text format for glycans in the glycoverse ecosystem). If this notation looks unfamiliar, don’t worry—check out this helpful guide first.

glycans <- c(
  "Neu5Ac(a2-3)Gal(b1-3)[Fuc(a1-6)]GlcNAc(b1-3)Gal(b1-3)GalNAc(b1-",
  "Neu5Ac(a2-?)Gal(b1-3)[Fuc(a1-6)]GlcNAc(b1-",
  "Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc(b1-",
  "Gal(b1-3)GalNAc(b1-",
  "Neu5Ac9Ac(a2-3)Gal(b1-4)GlcNAc(b1-"
)
motif <- "Neu5Ac(a2-3)Gal(b1-3)[Fuc(a1-6)]GlcNAc(b1-"
have_motif(glycans, motif)
#> [1]  TRUE FALSE FALSE FALSE FALSE

Pretty neat, right? 😎

Your Toolkit: Four Essential Functions 🛠️

glymotif provides four core functions that work together like a well-designed instrument panel:

  • have_motif(): Returns TRUE/FALSE for each glycan—does it contain the motif?
  • count_motif(): Returns numbers—how many times does the motif appear?
  • have_motifs(): The plural version—checks multiple motifs at once, returns a matrix
  • count_motifs(): Counts multiple motifs simultaneously, returns a matrix

Why the Plural Functions? 🤷‍♀️

You might wonder: “Why not just use have_motif() in a loop?” Great question! 💭 There are two compelling reasons:

1. Predictable output format 📊 Just like the purrr package has different map functions for different return types, our functions guarantee consistent outputs. The singular functions return vectors; the plural functions return matrices. No surprises, no wrestling with data types.

2. Optimized performance ⚡ The plural functions are specifically optimized for multiple motifs. They’re significantly faster than looping or using purrr::map() because they avoid redundant computations.

Seeing Them in Action

Let’s define some motifs to work with:

motifs <- c(
  "Neu5Ac(a2-3)Gal(b1-3)[Fuc(a1-6)]GlcNAc(b1-",
  "Fuc(a1-",
  "Gal(b1-3)GalNAc(b1-"
)

All functions follow the same pattern:

have_motif(glycans, motif)
#> [1]  TRUE FALSE FALSE FALSE FALSE
unname(have_motifs(glycans, motifs))  # Removing names for cleaner display
#>       [,1]  [,2]  [,3]
#> [1,]  TRUE  TRUE  TRUE
#> [2,] FALSE  TRUE FALSE
#> [3,] FALSE  TRUE FALSE
#> [4,] FALSE FALSE  TRUE
#> [5,] FALSE FALSE FALSE

Pro tip: 💡 You don’t need to memorize complex IUPAC strings! Use predefined motif names instead:

all_motifs()[1:10]
#>  [1] "Blood group H (type 2) - Lewis y" "i antigen"                       
#>  [3] "LacdiNAc"                         "GT2"                             
#>  [5] "Blood group B (type 1) - Lewis b" "LcGg4"                           
#>  [7] "Sialosyl paragloboside"           "Sialyl Lewis x"                  
#>  [9] "A antigen (type 3)"               "Type 1 LN2"
have_motif(glycans, "Type 2 LN2")
#> [1] FALSE FALSE FALSE FALSE FALSE

The Art and Science of Motif Matching 🎨🔬

Now we enter the fascinating complexity of motif recognition. You might think: “It’s just pattern matching, right?” Well, not quite. 🤨

Real-world glycan data is beautifully messy:

  • Missing linkage information: Sometimes we only know “there’s a link” but not its exact type
  • Generic monosaccharides: Mass spectrometry might only tell us “Hex” instead of “Glucose”
  • Chemical modifications: Sulfation, acetylation, and other decorations add complexity
  • Alignment constraints: Some motifs only “count” when they appear in specific locations

Consider the Tn antigen—it’s just a single GalNAc residue. But it shouldn’t match every GalNAc in a complex N-glycan, should it? Context matters.

Similarly, an O-glycan core motif should only be recognized at the reducing end, not buried in the middle of a structure.

glymotif handles all these complexities through its sophisticated matching engine. The algorithm considers structural context, chemical modifications, and biological relevance to make intelligent matching decisions.

What’s next?

Standing on the Shoulders of Giants 🏔️

This work wouldn’t be possible without the inspiration and groundwork laid by several excellent projects:

  • glycowork: A comprehensive Python toolkit for glycan analysis 🐍
  • GlyCompare: Advanced glycan comparison algorithms 🔬

We’re proud to contribute to this growing ecosystem of computational glycobiology tools! 🌱