glycowork 1.4.0

Package for processing and analyzing glycans

glycowork

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 ~50,500 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

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:

• 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.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	Terminal_Neu5Ac(a2-3)	Terminal_Gal(b1-3)	Terminal_Fuc(a1-3)	Terminal_GalNAcOS(b1-4)	Terminal_Man(a1-3)	Terminal_Fuc(a1-4)	Terminal_Xyl(b1-2)	Terminal_GalNAc(b1-4)	Terminal_Fuc(a1-6)	Terminal_Gal(b1-4)	Terminal_Fuc(a1-2)	Terminal_Man(a1-6)	Terminal_Fuc(a1-?)	Terminal_Man(a1-?)	Terminal_GlcNAc(b1-?)	Terminal_Gal(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	1	1	1	0	0	1	0	0	1	0	0	0	3	0	0	1
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	1	0	0	0	0	0	0	1	0	2	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	1	0	0	0	0	0	0	1	0	2	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	1	0	0	0	1	0	0	0	0	0	1	0	2	0
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	0	0	0	0	1	1	1	0	2	0	0	1
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	0	0	0	1	0	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-?)[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   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