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https://github.com/BojarLab/glycowork

Package for processing and analyzing glycans and their role in biology.
https://github.com/BojarLab/glycowork
bioinformatics computational-biology data-science glycans glycobiology machine-learning molecular-biology open-source python
Last synced: 10 days ago
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Package for processing and analyzing glycans and their role in biology.
Host: GitHub
URL: https://github.com/BojarLab/glycowork
Owner: BojarLab
License: mit
Created: 2021-01-07T20:22:02.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2024-04-23T05:25:37.000Z (9 months ago)
Last Synced: 2024-04-23T09:47:44.109Z (9 months ago)
Topics: bioinformatics, computational-biology, data-science, glycans, glycobiology, machine-learning, molecular-biology, open-source, python
Language: Jupyter Notebook
Homepage: https://Bojarlab.github.io/glycowork
Size: 675 MB
Stars: 51
Watchers: 6
Forks: 11
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project

README

        # glycowork

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[![HitCount](https://hits.dwyl.com/BojarLab/glycowork.svg?style=flat-square)](http://hits.dwyl.com/BojarLab/glycowork)![PyPI -

Downloads](https://img.shields.io/pypi/dm/glycowork?color=brightgreen.png)[![contributions

welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/BojarLab/glycowork/issues)[![DOI](https://zenodo.org/badge/327716604.svg)](https://zenodo.org/doi/10.5281/zenodo.10039202)



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:** 

**Contribute:** Interested in contributing? Read our [Contribution

Guidelines](https://github.com/BojarLab/glycowork/blob/master/CONTRIBUTING.md)

**Citation:** If `glycowork` adds value to your project, please cite

[Thomes et al.,

2021](https://academic.oup.com/glycob/advance-article/doi/10.1093/glycob/cwab067/6311240)

## Install

Not familiar with Python? Try our no-code, graphical user

interface (`glycoworkGUI.exe`, can be downloaded at the bottom of the

latest [Release](https://github.com/BojarLab/glycowork/releases) 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](https://onlinelibrary.wiley.com/doi/10.1002/advs.202103807)

- **`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](https://pubmed.ncbi.nlm.nih.gov/34133929/)

- **`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](./05_examples.ipynb) A non-exhaustive

list includes:

- using trained AI models for prediction [–\> Learn

  more](./02_ml.ipynb#inference)

- training your own AI models [–\> Learn

  more](./02_ml.ipynb#model_training)

- motif enrichment analyses [–\> Learn more](./03_motif.ipynb#analysis)

- differential glycomics expression analysis [–\> Learn

  more](./03_motif.ipynb#analysis)

- annotating motifs in glycans [–\> Learn

  more](./03_motif.ipynb#annotate)

- drawing publication-quality glycan figures [–\> Learn

  more](./03_motif.ipynb#draw)

- finding out whether & where glycans are describing the same sequence

  [–\> Learn more](./03_motif.ipynb#graph)

- *m/z* to composition to structure to motif mappings [–\> Learn

  more](./03_motif.ipynb#tokenization)

- mass calculation [–\> Learn more](./03_motif.ipynb#tokenization)

- visualizing motif distribution / glycan similarities / sequence

  properties [–\> Learn more](./03_motif.ipynb#analysis)

- constructing and analyzing biosynthetic networks [–\> Learn

  more](./04_network.ipynb#biosynthesis)

``` python

#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")

```

![](index_files/figure-commonmark/cell-3-output-1.svg)

``` python

#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 |

``` python

#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)']

``` python

#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