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.
- Ubiquitous in biology
- Integral to protein and lipid function
- Relevant to human diseases
Analyzing glycans is complicated due to their non-linear structures and
enormous diversity. But that’s where glycowork comes in.
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, WURCS, GLYCAM, and GlycoWorkBench.
- 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
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; on Mac you
might need to use "glycowork[draw]"), 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]
Glycowork currently contains the following main datasets that are
freely available to everyone:
df_glycan- contains ~50,500 unique glycan sequences, including labels such as ~39,500 species associations, ~19,000 tissue associations, and ~2,500 disease associations
glycan_binding- contains >580,000 protein-glycan binding interactions, from 1,465 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
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:
- using trained AI models for prediction –> Learn more
- training your own AI models –> Learn more
- motif enrichment analyses –> Learn more
- differential glycomics expression analysis –> Learn more
- annotating motifs in glycans –> Learn more
- drawing publication-quality glycan figures –> Learn more
- finding out whether & where glycans are describing the same sequence –> Learn more
- m/z to composition to structure to motif mappings –> Learn more
- mass calculation –> Learn more
- visualizing motif distribution / glycan similarities / sequence properties –> Learn more
- constructing and analyzing biosynthetic networks –> Learn more
#drawing publication-quality glycan figures
from glycowork 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'], condense=True)| Internal_LewisX | SialylLewisX | Terminal_LewisA | H_antigen_type2 | Chitobiose | Trimannosylcore | Terminal_LacNAc_type1 | Internal_LacNAc_type2 | Terminal_LacNAc_type2 | Terminal_LacdiNAc_type2 | core_fucose | core_fucose(a1-3) | Nglycan_complex | Nglycan_complex2 | M3FX | 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 | Terminal_Neu5Ac(a2-3) | Terminal_Gal(b1-3) | Terminal_Man(a1-3) | Terminal_Gal(b1-4) | Terminal_GalNAc(b1-4) | Terminal_Fuc(a1-3) | Terminal_Man(a1-6) | Terminal_GalNAcOS(b1-4) | Terminal_Fuc(a1-4) | Terminal_Xyl(b1-2) | Terminal_Fuc(a1-6) | Terminal_Fuc(a1-2) | Terminal_Man(a1-?) | Terminal_Fuc(a1-?) | Terminal_Gal(b1-?) | Terminal_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 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 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 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 1 | 0 |
| Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 0 | 1 | 1 | 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 | 1 | 0 | 0 | 0 | 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 | 1 | 1 | 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 | 1 | 0 | 0 | 0 | 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 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 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 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 | 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 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 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 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 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(a1-3/4)[Gal(b1-3/4)]GlcNAc', termini_list = ['t', 'i', 'f']))
print(subgraph_isomorphism('Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
'dHex(a1-?)[Hex(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 GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][Man(a1-6)]... 0
2 GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[Man(a1-6)]... 0
3 GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Ma... 0
4 GlcNAc(b1-2)Man(a1-6)[Man(a1-3)][GlcNAc(b1-4)]... 0
5 Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][GlcNAc(b1-4)]... 0
6 GlcNAc(?1-?)Man(a1-3)[GlcNAc(b1-?)Man(a1-6)]Ma... 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