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 FALSEPretty 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:
-
First argument: your glycans (as IUPAC strings or a
glyrepr::glycan_structure()object) -
Second argument: your motif(s) (IUPAC strings, a
glyrepr::glycan_structure()object, or predefined motif names)
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 FALSEPro 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 FALSECaution: If you are using predefined motif names, you should be aware that all of the built-in motifs have “intact” structure level. See the “Handling Structural Ambiguity” section below for more details.
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.
Handling Structural Ambiguity 🤔
Real-world glycan data often comes with structural ambiguity. Mass spectrometry might only tell us “HexNAc” instead of “GlcNAc”, or linkage analysis might yield “a1-?” instead of “a1-6”. These uncertainties are common in experimental glycomics and glycoproteomics.
How glymotif Handles Structural Ambiguity
glymotif handles these ambiguities with a fundamental
principle: A glycan cannot be more ambiguous than the motif it’s
being matched against.
# Ambiguous linkages won't match specific ones
have_motif("Gal(??-?)GalNAc(??-", "Gal(a1-6)GalNAc(a1-")
#> [1] FALSE
# Generic monosaccharides won't match specific ones
have_motif("Hex(a1-6)HexNAc(a1-", "Gal(a1-6)GalNAc(a1-")
#> [1] FALSEThis behavior is intentional, not a bug. ✨ True motif identification requires confidence: structural possibilities alone aren’t sufficient evidence.
Working Around Ambiguity
If you’re getting unexpected FALSE results with
have_motif() (especially when using built-in motifs with
ambiguous glycans), the first thing you should do is to check the
structure level of the glycan and the motif. You can use
glyrepr::get_structure_level() to help you with this
task.
# get_structure_level() expects a glycan structure vector
get_structure_level(as_glycan_structure(c("Gal(??-?)GalNAc(??-", "Gal(a1-6)GalNAc(a1-")))
#> [1] "topological" "intact"here are two strategies:
1. Ignore linkage information when linkages are unreliable:
have_motif("Gal(??-?)GalNAc(??-", "Gal(a1-6)GalNAc(a1-", ignore_linkages = TRUE)
#> [1] TRUE2. Convert motifs to generic forms to match the generic monosaccharides of your data:
motif <- glyparse::auto_parse("Gal(a1-6)GalNAc(a1-") # First, create a `glycan_structure()`
motif <- glyrepr::convert_to_generic(motif) # Then, convert to generic
have_motif("Hex(a1-6)HexNAc(a1-", motif)
#> [1] TRUE⚠️ Important: When using these workarounds, interpret your results with appropriate caution. You’re trading specificity for coverage.
Dynamic Motif Detection
While matching against a database of known motifs is powerful, sometimes you want to discover what motifs are actually present in your specific dataset, even those not in the database. This is where dynamic motif detection comes in.
Instead of asking “Is motif A here?”, we ask “What motifs are here?”.
extract_motif()
extract_motif() allows you to detect all motifs appears
in a set of glycans. Take a simple O-glycan for example:
extract_motif("Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-")
#> <glycan_structure[6]>
#> [1] Gal(b1-
#> [2] GlcNAc(b1-
#> [3] GalNAc(a1-
#> [4] Gal(b1-3)GalNAc(a1-
#> [5] GlcNAc(b1-6)GalNAc(a1-
#> [6] Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-
#> # Unique structures: 6This function works vectorizedly, and only a unique set of motifs will be returned.
extract_motif(c(
"Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-",
"Gal(b1-3)GalNAc(a1-"
))
#> <glycan_structure[6]>
#> [1] Gal(b1-
#> [2] GlcNAc(b1-
#> [3] GalNAc(a1-
#> [4] Gal(b1-3)GalNAc(a1-
#> [5] GlcNAc(b1-6)GalNAc(a1-
#> [6] Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-
#> # Unique structures: 6As you can imagine, the number of possible dynamic motifs in a large
glycan can be very large. Therefore, extract_motif() has a
max_size parameter restricting the size of motifs to be
extracted. By default, max_size = 3, this restricts the
motifs to be extracted to those with at most 3 monosaccharides.
extract_motif("Glc(a1-2)Glc(a1-2)Glc(a1-2)Glc(a1-")
#> <glycan_structure[3]>
#> [1] Glc(a1-
#> [2] Glc(a1-2)Glc(a1-
#> [3] Glc(a1-2)Glc(a1-2)Glc(a1-
#> # Unique structures: 3You can increase the max_size to extract larger
motifs.
extract_motif("Glc(a1-2)Glc(a1-2)Glc(a1-2)Glc(a1-", max_size = 4)
#> <glycan_structure[4]>
#> [1] Glc(a1-
#> [2] Glc(a1-2)Glc(a1-
#> [3] Glc(a1-2)Glc(a1-2)Glc(a1-
#> [4] Glc(a1-2)Glc(a1-2)Glc(a1-2)Glc(a1-
#> # Unique structures: 4However, increase it progressively with caution, as the computation time can increase exponentially.
extract_branch_motif()
extract_motif() works well with O-glycans, which are
very versatile and not very large. However, using it on N-glycans might
be less effective and less meaningful, as the core pattern of an
N-glycan is very restricted by the biosynthesis rules. The only
diversity comes from the antennae. Therefore, we provide
extract_branch_motif() to extract only the branching
motifs.
glycans <- c(
"Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(a1-4)GlcNAc(b1-",
"Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(a1-4)GlcNAc(b1-",
"Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(a1-4)GlcNAc(b1-"
)
extract_branch_motif(glycans)
#> <glycan_structure[4]>
#> [1] Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-
#> [2] Gal(b1-4)GlcNAc(b1-
#> [3] Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-
#> [4] GlcNAc(b1-
#> # Unique structures: 4What’s Next?
- Want to known all the details about motif matching rules? Here
- Working with
glyexp::experiment()? Here
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! 🌱