Start with glymotif: find a familiar motif | A motif alone does not tell the whole story | From a motif to a search pattern | Meet your first collection of glycans | Start with one residue | Connect two residues | Add a linkage when it matters | Ask whether a residue is terminal | Describe a branch | Require a branch to be absent | Allow a choice of residues | Count and retrieve what you found | Put it together: sialylated LacNAc without a local Fuc branch | A few practical points | When you need a little more | Repeated units | Save a pattern for reuse

What is a pattern? | Keep structure notation and pattern notation separate | One residue, several choices, or any residue | Build a connected path | Specify a linkage only as precisely as you need | Restrict where the match occurs | Describe branches on a shared residue | Require a branch to be absent | Three different meanings of “not” | Optional residues and repeated units | Prefer longer or shorter repeats | Check neighbours without including them (advanced) | Give part of a match a name (advanced) | Build and check your own pattern | Interpret the results in structural terms

The Challenge | What is glyanno? | Build the annotation cache | Workflow: M/z -> Composition -> Structure | Enhancing Compositions and Structures | De novo enhancement of N-glycans | Bidirectional Conversion

trace_biosynthesis() | trace_biosynthesis_virtual() | Hybrid methods

Parsing glycan text | Supported formats | Part 1: Automatic parsing | Part 2: IUPAC notation | IUPAC-condensed | IUPAC-short | IUPAC-extended | IUPAC-compact | GlyCAM IUPAC | Part 3: Database and exchange formats | GlycoCT | WURCS | KCF | Linear Code | LINUCS | Part 4: Software-specific formats | GlycoWorkbench | pGlyco | StrucGP | Handling parsing failures

Explore the data | Use a consistent interface | Work with the results | Available analyses

Overview | Unique Structure Optimization | The Problem | The Solution | Memory Savings | The smap Family | Structure-Aware Mapping | The smap Toolkit | Basic Examples | Beyond Basic smap() | Performance | Additional Patterns | Working with Complex Functions | Combining Multiple Metrics | Memory-Efficient Filtering | When to Use smap Functions | Example: Custom Motif Detection | Summary | Session Information

Glycan Composition Vectors | Creating Glycan Composition Vectors | Two Types of Monosaccharide Names | Inspecting Glycan Composition Vectors | Manipulating Glycan Composition Vectors | Working with Tibbles | Missing Values and Names | Glycan Structure Vectors | Creating Glycan Structure Vectors | Inspecting Glycan Structure Vectors | Structure Levels | Manipulating Glycan Structure Vectors | What's Next?

Your First Taste of Glycan Engineering | Tracing Glycan Origins | Predicting Glycan Products | Multi-Step Product Growth | The Fine Print | Species and Scope | Inclusive Candidate Calls | Sulfate Is the Supported Substituent | Biosynthetic Starting Points | Technical Notes

Glycans as Graphs | What Is Stored in Memory? | Extracting the Graph | Graph Components | Vertices | Edges | Graph-Level Attributes | Floating Parts | Floating Substituents | Working with the Graph | Using igraph | Using smap Functions | Motif Analysis with glymotif | Summary

Welcome to the Wild World of Data Preprocessing! 🧬 | Meet Our Star Player: Real Glycoproteomics Data 🌟 | The Magic Wand: One Function to Rule Them All ✨ | Still Like Magic, but With More Control? | Taking the Scenic Route: Step-by-Step Preprocessing 🚶‍♀️ | Step 1: Normalization – Getting Everyone on the Same Page 📏 | Step 2: Variable Filtering – Saying Goodbye to the Unreliable 🧹 | Step 3: Imputation – Filling in the Blanks Intelligently 🔮 | Step 4: Aggregation – From Peptides to Glycoforms 🔄 | Step 5: Batch Effect Correction – Harmonizing Your Orchestra 🎼 | Step 6: Protein Expression Adjustment – Separating Signal from Noise 🎯 | 🎉 Mission Accomplished! | Advanced Usage: Flexible Grouping with Custom Vectors 🔧 | Quality Control (QC) 📊 | What's Next?

Draw one glycan | Basic options | show_linkage | orient | red_end annotation | Styles | fuc_orient | red_end in a style | red_end_length | red_end_size | edge_linewidth and node_linewidth | node_size | colors | Reusing styles | Bundled styles | Node highlighting | Save one cartoon | Export many cartoons

Label a heatmap with glycans | Use glycans as column labels | Control the annotation appearance | Keep labels aligned with the data

geom_glycan() | scale_x_glycan() / scale_y_glycan() | guide_glycan() | geom_node_glycan()

Required Components | Step 1: Sample Information (Column Data) | Step 2: Variable Information (Row Data) | Step 3: Expression Matrix | Matching Requirements | Step 4: Creating the Experiment

Two Flavors: _col() and _row() | Virtual Index Columns | Complete Function Reference | Core Data Manipulation Functions | Advanced Slicing Functions | Joining Functions | Function-by-Function Examples | Selection | Arrangement | Mutation | Slicing | Renaming | Joining | When dplyr-Style Functions Cannot Help | Why Doesn't glyexp Implement All dplyr Functions? | Common Pitfalls and How to Avoid Them | Pitfall 1: Using glyexp Functions on Non-SE Objects | Pitfall 2: Forgetting the Synchronization | Pitfall 3: Trying to Remove Index Columns | Pitfall 4: Mismatched Operations | Performance Considerations | Philosophy Behind the Design

For those familiar with SummarizedExperiment | Getting Started with glyexp | The Expression Matrix | Variable Information | Sample Information | Rownames and colnames | Data Manipulation with glyexp | Background and Design Principles | What's Next?

What is a Glycan Motif? | Why Motifs Matter | A Quick Challenge | Your Toolkit: Four Essential Functions | Why the Plural Functions? | Seeing Them in Action | Motif Matching Rules | Handling Structural Ambiguity | How glymotif Handles Structural Ambiguity | Working Around Ambiguity | Database Motif Detection | Dynamic Motif Detection | extract_motif() | extract_branch_motif() | dynamic_motifs() and branch_motifs() | What's Next? | Related Projects

Approach 1: Protein-Centric Enrichment Analysis | Over Representation Analysis (ORA) | Gene Set Enrichment Analysis (GSEA) | Approach 2: Glycan-Centric Enrichment Analysis

Custom Traits | Trait Factories | prop() | ratio() | wmean() | total() | wsum() | Using Custom Traits | Validating Trait Definitions | Custom Meta-Properties | Defining Custom Meta-Properties with Functions | Defining Custom Meta-Properties with Columns | Using make_trait() | Exercise Solutions

Important Notes Before You Start | Prerequisites | Data Types | Your First Analysis | Site-Specific Derived Traits in Glycoproteomics | Understanding Meta-Properties | get_meta_properties() | add_meta_properties() | Meta-Property Functions | Working with Structural Ambiguity | Generic vs. Specific Monosaccharides | Handling Detailed Structures | Extending Functionality | What's Next?

What is motif quantification? | The quantify_motifs() function | Absolute vs. relative motif quantification | Absolute motif quantification | Relative motif quantification | Choosing the right approach | Connection to derived traits

Getting Started | Cost and Efficiency | Designing Pipelines with inquire_blueprint() | Refining Your Analysis | AI-Enhanced Reporting | Complete AI Workflow

Analyze your data with a single line of code | Save all results with one line of code | One line to generate a report | About naming | Blueprints

One function, two files — that's it | Load your data | What's next?

How Motif Matching Works | Defining Our Terms | Why Not Just Use str_detect()? | Matching Rules | Rule 1: Branching Logic | Rule 2: Linkage Flexibility | Rule 3: Monosaccharide Resolution | Rule 4: Chemical Modifications | Rule 5: Alignment Constraints | Rule 6: Reducing End Anomers | Why This Complexity Matters | Ready for More?

Step functions | Step dependencies | The on parameter | Branches | Summary

The Dual Nature of glyvis | A Philosophy on Aesthetics