Glycowork: A Python package for glycan data science and machine learning

Glycobiology. 2021 Nov 18;31(10):1240-1244. doi: 10.1093/glycob/cwab067.

Abstract

While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source Python package designed for glycan-related data science and machine learning by end users. Glycowork includes functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models and learned glycan representations. We envision that glycowork can extract further insights from glycan datasets and demonstrate this with workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

Keywords: Python; data science; glycobioinformatics; glycobiologymachine learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Science*
  • Databases, Factual
  • Machine Learning*
  • Polysaccharides / chemistry*
  • Software*

Substances

  • Polysaccharides