/glycowork

Package for processing and analyzing glycans and their role in biology.

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glycowork

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Glycans are fundamental biological sequences that are as crucial as DNA, RNA, and proteins. As complex carbohydrates forming branched structures, glycans are ubiquitous yet often overlooked in biological research.

Why Glycans are Important

  • Ubiquitous in biology
  • Integral to protein and lipid function
  • Relevant to human diseases

Challenges in Glycan Analysis

Analyzing glycans is complicated due to their non-linear structures and enormous diversity. But that’s where glycowork comes in.

Introducing glycowork: Your Solution for Glycan-Focused Data Science

Glycowork is a Python package specifically designed to simplify glycan sequence processing and analysis. It offers:

  • Functions for glycan analysis
  • Datasets for model training
  • Full support for IUPAC-condensed string representation. Broad support for IUPAC-extended, LinearCode, Oxford, GlycoCT, and WURCS.
  • Powerful graph-based architecture for in-depth analysis

Documentation: https://bojarlab.github.io/glycowork/

Contribute: Interested in contributing? Read our Contribution Guidelines

Citation: If glycowork adds value to your project, please cite Thomes et al., 2021

Install

Not familiar with Python? Try our no-code, graphical user interface (glycoworkGUI.exe, can be downloaded at the bottom of the latest Release page) for accessing some of the most useful glycowork functions!

via pip:
pip install glycowork
import glycowork

alternative:
pip install git+https://github.com/BojarLab/glycowork.git
import glycowork

Note that we have optional extra installs for specialized use (even further instructions can be found in the Examples tab), such as:
deep learning
pip install glycowork[ml]
drawing glycan images with GlycoDraw (see install instructions in the Examples tab)
pip install glycowork[draw]
analyzing atomic/chemical properties of glycans
pip install glycowork[chem]
everything
pip install glycowork[all]

Data & Models

Glycowork currently contains the following main datasets that are freely available to everyone:

  • df_glycan
    • contains ~50,000 unique glycan sequences, including labels such as ~37,500 species associations, ~16,500 tissue associations, and ~1,000 disease associations
  • glycan_binding
    • contains >550,000 protein-glycan binding interactions, from 1,392 unique glycan-binding proteins

Additionally, we store these trained deep learning models for easy usage, which can be retrieved with the prep_model function:

  • LectinOracle
    • can be used to predict glycan-binding specificity of a protein, given its ESM-1b representation; from Lundstrom et al., 2021
  • LectinOracle_flex
    • operates the same as LectinOracle but can directly use the raw protein sequence as input (no ESM-1b representation required)
  • SweetNet
    • a graph convolutional neural network trained to predict species from glycan, can be used to generate learned glycan representations; from Burkholz et al., 2021
  • NSequonPred
    • given the ESM-1b representation of an N-sequon (+/- 20 AA), this model can predict whether the sequon will be glycosylated

How to use

Glycowork currently contains four main modules:

  • glycan_data
    • stores several glycan datasets and contains helper functions
  • ml
    • here are all the functions for training and using machine learning models, including train-test-split, getting glycan representations, etc.
  • motif
    • contains functions for processing & drawing glycan sequences, identifying motifs and features, and analyzing them
  • network
    • contains functions for constructing and analyzing glycan networks (e.g., biosynthetic networks)

Below are some examples of what you can do with glycowork; be sure to check out the other examples in the full documentation for everything that’s there. –> Learn more A non-exhaustive list includes:

#drawing publication-quality glycan figures
from glycowork.motif.draw import GlycoDraw
GlycoDraw("Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Neu5Gc(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)][GlcNAc(b1-4)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc", highlight_motif = "Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc")

#get motifs, graph features, and sequence features of a set of glycan sequences to train models or analyze glycan properties
glycans = ["Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc",
           "Ma3(Ma6)Mb4GNb4GN;N",
           "α-D-Manp-(1→3)[α-D-Manp-(1→6)]-β-D-Manp-(1→4)-β-D-GlcpNAc-(1→4)-β-D-GlcpNAc-(1→",
           "F(3)XA2",
           "WURCS=2.0/5,11,10/[a2122h-1b_1-5_2*NCC/3=O][a1122h-1b_1-5][a1122h-1a_1-5][a2112h-1b_1-5][a1221m-1a_1-5]/1-1-2-3-1-4-3-1-4-5-5/a4-b1_a6-k1_b4-c1_c3-d1_c6-g1_d2-e1_e4-f1_g2-h1_h4-i1_i2-j1",
           """RES
1b:b-dglc-HEX-1:5
2s:n-acetyl
3b:b-dglc-HEX-1:5
4s:n-acetyl
5b:b-dman-HEX-1:5
6b:a-dman-HEX-1:5
7b:b-dglc-HEX-1:5
8s:n-acetyl
9b:b-dgal-HEX-1:5
10s:sulfate
11s:n-acetyl
12b:a-dman-HEX-1:5
13b:b-dglc-HEX-1:5
14s:n-acetyl
15b:b-dgal-HEX-1:5
16s:n-acetyl
LIN
1:1d(2+1)2n
2:1o(4+1)3d
3:3d(2+1)4n
4:3o(4+1)5d
5:5o(3+1)6d
6:6o(2+1)7d
7:7d(2+1)8n
8:7o(4+1)9d
9:9o(-1+1)10n
10:9d(2+1)11n
11:5o(6+1)12d
12:12o(2+1)13d
13:13d(2+1)14n
14:13o(4+1)15d
15:15d(2+1)16n"""]
from glycowork.motif.annotate import annotate_dataset
out = annotate_dataset(glycans, feature_set = ['known', 'terminal', 'exhaustive'])
Terminal_LewisX Internal_LewisX LewisY SialylLewisX SulfoSialylLewisX Terminal_LewisA Internal_LewisA LewisB SialylLewisA SulfoLewisA H_type2 H_type1 A_antigen B_antigen Galili_antigen GloboH Gb5 Gb4 Gb3 3SGb3 8DSGb3 3SGb4 8DSGb4 6DSGb4 3SGb5 8DSGb5 6DSGb5 6DSGb5_2 6SGb3 8DSGb3_2 6SGb4 8DSGb4_2 6SGb5 8DSGb5_2 66DSGb5 Forssman_antigen iGb3 I_antigen i_antigen PI_antigen Chitobiose Trimannosylcore Internal_LacNAc_type1 Terminal_LacNAc_type1 Internal_LacNAc_type2 Terminal_LacNAc_type2 Internal_LacdiNAc_type1 Terminal_LacdiNAc_type1 Internal_LacdiNAc_type2 Terminal_LacdiNAc_type2 bisectingGlcNAc VIM PolyLacNAc Ganglio_Series Lacto_Series(LewisC) NeoLacto_Series betaGlucan KeratanSulfate Hyluronan Mollu_series Arthro_series Cellulose_like Chondroitin_4S GPI_anchor Isoglobo_series LewisD Globo_series Sda SDA Muco_series Heparin Peptidoglycan Dermatansulfate CAD Lactosylceramide Lactotriaosylceramide LexLex GM3 H_type3 GM2 GM1 cisGM1 VIM2 GD3 GD1a GD2 GD1b SDLex Nglycolyl_GM2 Fuc_LN3 GT1b GD1 GD1a_2 LcGg4 GT3 Disialyl_T_antigen GT1a GT2 GT1c 2Fuc_GM1 GQ1c O_linked_mannose GT1aa GQ1b HNK1 GQ1ba O_mannose_Lex 2Fuc_GD1b Sialopentaosylceramide Sulfogangliotetraosylceramide B-GM1 GQ1aa bisSulfo-Lewis x para-Forssman core_fucose core_fucose(a1-3) GP1c B-GD1b GP1ca Isoglobotetraosylceramide polySia high_mannose Gala_series LPS_core Nglycan_complex Nglycan_complex2 Oglycan_core1 Oglycan_core2 Oglycan_core3 Oglycan_core4 Oglycan_core5 Oglycan_core6 Oglycan_core7 Xylogalacturonan Sialosylparagloboside LDNF OFuc Arabinogalactan_type2 EGF_repeat Nglycan_hybrid Arabinan Xyloglucan Acharan_Sulfate M3FX M3X 1-6betaGalactan Arabinogalactan_type1 Galactomannan Tetraantennary_Nglycan Mucin_elongated_core2 Fucoidan Alginate FG XX Difucosylated_core GalFuc_core Fuc Gal GalNAc GalNAcOS GlcNAc Man Neu5Ac Xyl Fuc(a1-2)Gal Fuc(a1-3)GlcNAc Fuc(a1-4)GlcNAc Fuc(a1-6)GlcNAc Fuc(a1-?)GlcNAc Gal(b1-3)GlcNAc Gal(b1-4)GlcNAc Gal(b1-?)GlcNAc GalNAc(b1-4)GlcNAc GalNAcOS(b1-4)GlcNAc GlcNAc(b1-2)Man GlcNAc(b1-4)GlcNAc GlcNAc(b1-?)Man Man(a1-3)Man Man(a1-6)Man Man(a1-?)Man Man(b1-4)GlcNAc Neu5Ac(a2-3)Gal Xyl(b1-2)Man GalNAc(b1-4) Neu5Ac(a2-3) Gal(b1-3) GalNAcOS(b1-4) Fuc(a1-2) Man(a1-6) Man(a1-3) Fuc(a1-6) Xyl(b1-2) Gal(b1-4) Fuc(a1-3) Fuc(a1-4) Man(a1-?) Gal(b1-?) Fuc(a1-?) GlcNAc(b1-?)
Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 4 3 1 0 0 1 1 1 3 1 1 2 0 0 2 1 2 1 1 2 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 0 1 3 0
Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 2 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 2 0 0 0
Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 2 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 2 0 0 0
GlcNAc(b1-?)Man(a1-3)[GlcNAc(b1-?)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 4 3 0 1 0 1 0 0 1 0 0 0 0 0 0 1 2 1 1 2 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 2
Fuc(a1-2)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[Gal(b1-4)GlcNAc(b1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 4 3 0 0 1 0 0 1 1 0 2 2 0 0 2 1 2 1 1 2 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 2 0
GalNAcOS(b1-4)GlcNAc(b1-2)Man(a1-3)[GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 4 3 0 0 0 0 0 0 0 0 0 0 1 1 2 1 2 1 1 2 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 
#using graphs, you can easily check whether a glycan contains a specific motif; how about internal Lewis A/X motifs?
from glycowork.motif.graph import subgraph_isomorphism
print(subgraph_isomorphism('Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
                     'Fuc(a1-?)[Gal(b1-?)]GlcNAc', termini_list = ['terminal', 'internal', 'flexible']))
print(subgraph_isomorphism('Neu5Ac(a2-3)Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
                     'Fuc(a1-?)[Gal(b1-?)]GlcNAc', termini_list = ['t', 'i', 'f']))
print(subgraph_isomorphism('Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
                     'Fuc(a1-?)[Gal(b1-?)]GlcNAc', termini_list = ['t', 'i', 'f']))

#or you could find the terminal epitopes of a glycan
from glycowork.motif.annotate import get_terminal_structures
print("\nTerminal structures:")
print(get_terminal_structures('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'))
True
True
False

Terminal structures:
['Man(a1-3)', 'Man(a1-6)', 'Fuc(a1-6)']

#given a composition, find matching glycan structures in SugarBase; specific for glycan classes and taxonomy
from glycowork.motif.tokenization import compositions_to_structures
print(compositions_to_structures([{'Hex':3, 'HexNAc':4}], glycan_class = 'N'))

#or we could calculate the mass of this composition
from glycowork.motif.tokenization import composition_to_mass
print("\nMass of the composition Hex3HexNAc4")
print(composition_to_mass({'Hex':3, 'HexNAc':4}))
print(composition_to_mass("H3N4"))
print(composition_to_mass("Hex3HexNAc4"))
0 compositions could not be matched. Run with verbose = True to see which compositions.
                                               glycan  abundance
0   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Ma...          0
1   GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Ma...          0
2   GlcNAc(b1-2)Man(a1-6)[Man(a1-3)][GlcNAc(b1-4)]...          0
3   Man(a1-3)[GlcNAc(b1-4)][GlcNAc(b1-2)Man(a1-6)]...          0
4   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][Man(a1-6)]...          0
5   GlcNAc(?1-?)Man(a1-3)[GlcNAc(b1-?)Man(a1-6)]Ma...          0
6   GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[Man(a1-6)]...          0
7   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-6)Man(a1-6)]Ma...          0
8   GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-6)Man(a1-6)]Ma...          0
9   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][G...          0
10  GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)[GlcNAc(b1-4...          0
11  GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[GlcNAc(b1-...          0
12  GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Ma...          0
13  Man(a1-3)[GlcNAc(b1-2)[GlcNAc(b1-6)]Man(a1-6)]...          0
14  GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Ma...          0

Mass of the composition Hex3HexNAc4
1316.4865545999999
1316.4865545999999
1316.4865545999999