Glycan Analysis and Glycoinformatics Library for Python

Glycobiology is the study of the biological functions, properties, and structures of carbohydrate biomolecules, also called glycans. These large, tree-like molecules are complex, having a wide variety of building blocks as well as modifications and substitutions on those building blocks.

Much in the same way other bioinformatics libraries provide ways to represent DNA, RNA, or Protein sequences, this library attempts to provide a representation of glycans. Much of the variation found in the building blocks of these structures, monosaccharides, are caused by substitutions of functional groups on a common core structure.

1. GlycoCT{condensed} (i/o)
2. GlycoCT{XML} (i)
3. GlycoMinds Linear Code (i/o)
4. IUPAC Three Letter Code (i/o)

2. Traverse structures with common algorithms like breadth-first and depth-first, or use node-level information to choose a customized path.
3. Operate on monosaccharide and substituents as nodes and bonds as edges.
4. Add, remove, and modify these structures to alter glycan properties.
5. Identify substructures and motifs, classifying glycans.
6. Score structural similarities with one of several ordering and comparator methods.
7. Plot tree structures with Matplotlib, rendering against any viable backend using configurable symbol nomenclature, such as Consortium for Functional Glycomics (CFG) or IUPAC text. Specialized SVG labeling for better web-interactivity.

3. Calculate the mass of a native or derivatized glycan.
4. Generate glycosidic and cross ring cleavage fragments for a collection of glycan structures for performing MS/MS database search.
5. Perform substructure similarity searches with exact ordering or topological comparison and exact or fuzzy per-residue matching to classify a structure as an N-linked glycan.
6. Annotate MS spectra with glycan structures, labeling which peaks matched a database entry.