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Protocol for constructing glycan biosynthetic networks using glycowork
Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data...
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Published in: | STAR protocols 2024-06, Vol.5 (2), p.102937, Article 102937 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets.
For complete details on the use and execution of this protocol, please refer to Thomès et al.1
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•Steps for environment setup and data preparation•Instructions for building and plotting biosynthetic networks•Steps for pruning networks using evolutionary information•Steps for analysis of network statistics and feature highlighting
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets. |
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ISSN: | 2666-1667 2666-1667 |
DOI: | 10.1016/j.xpro.2024.102937 |