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

Introducing glycowork: Your Solution for Glycan-Focused Data Science

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, CSDB-linear, GlycoWorkBench, GlyTouCan IDs, KCF, GlySeeker, and more.
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

Or try our web interface for sequence format conversion/cleaning and drawing glycan SNFG structures!

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[ml]"), such as: deep learning pip install glycowork[ml] 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, ~20,000 tissue associations, and ~1,000 disease associations
glycan_binding
- contains >790,000 protein-glycan binding interactions, from >2,000 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 ESMC representation; from Lundstrom et al., 2021
LectinOracle_flex
- operates the same as LectinOracle but can directly use the raw protein sequence as input (no ESMC 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 import GlycoDraw
drawing = 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", suppress=True)

#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 | Internal_LewisA | H_antigen_type2 | Chitobiose | Trimannosylcore | Terminal_LacNAc_type1 | Internal_LacNAc_type2 | Terminal_LacNAc_type2 | Terminal_LacdiNAc_type2 | core_fucose | core_fucose(a1-3) | Fuc | Gal | GalNAc | GalNAcOS | GlcNAc | Man | Neu5Ac | Xyl | Man(b1-4)GlcNAc | GlcNAc(b1-2)Man | Fuc(a1-6)GlcNAc | Fuc(a1-4)GlcNAc | Man(a1-6)Man | Fuc(a1-3)GlcNAc | Neu5Ac(a2-3)Gal | Gal(b1-3)GlcNAc | Xyl(b1-2)Man | GlcNAc(b1-4)GlcNAc | Gal(b1-4)GlcNAc | Fuc(a1-2)Gal | Man(a1-3/6)Man | Man(a1-3)Man | Fuc(a1-3/4/6)GlcNAc | Gal(b1-3/4)GlcNAc | Terminal_Neu5Ac(a2-3) | Terminal_Gal(b1-3) | Terminal_GlcNAc(b1-2) | Terminal_Fuc(a1-3) | Terminal_Fuc(a1-2) | Terminal_Man(a1-6) | Terminal_Man(a1-3) | Terminal_Man(a1-3/6) | Terminal_Fuc(a1-2/3/4/6) | Terminal_Gal(b1-4) | Terminal_Fuc(a1-4) | Terminal_Xyl(b1-2) | Terminal_Gal(b1-3/4) | Terminal_Fuc(a1-6) | |----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----| | 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 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 3 | 2 | 0 | 0 | 4 | 3 | 1 | 0 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 2 | 1 | 3 | 2 | 1 | 1 | 2 | 1 | 0 | 1 | 1 | 2 | 3 | 1 | 1 | 0 | 2 | 1 | | Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | | Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | | GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 4 |

Glycowork

Install / Use

README