Department of Chemistry and Molecular Biology and Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.
Department of Biostatistics, Harvard School of Public Health, Boston, 02115 MA, USA.
Glycobiology. 2021 Nov 18;31(10):1240-1244. doi: 10.1093/glycob/cwab067.
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/.
尽管聚糖对于生物过程至关重要,但现有的分析方式使得计算背景有限的研究人员难以将这些多样化的碳水化合物纳入工作流程中。在这里,我们介绍了 glycowork,这是一个面向终端用户的用于聚糖相关数据科学和机器学习的开源 Python 包。glycowork 包含了一些功能,例如自动注释聚糖基序,并通过热图和统计富集分析它们的分布。我们还提供了可视化方法、与存储数据库交互的例程、训练好的机器学习模型以及学习到的聚糖表示。我们设想 glycowork 可以从聚糖数据集中提取更多的见解,并通过分析各种生物背景下的聚糖基序的工作流程来证明这一点。glycowork 可以在 https://github.com/BojarLab/glycowork/ 上免费访问。