ssbio: a Python framework for structural systems biology

Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thou...

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Veröffentlicht in:Bioinformatics Jg. 34; H. 12; S. 2155 - 2157
Hauptverfasser: Mih, Nathan, Brunk, Elizabeth, Chen, Ke, Catoiu, Edward, Sastry, Anand, Kavvas, Erol, Monk, Jonathan M, Zhang, Zhen, Palsson, Bernhard O
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 15.06.2018
Schlagworte:
Applications Notes
Computational Biology - methods
Models, Biological
Protein Conformation
Software
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Zusammenfassung:Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.
Bibliographie:ObjectType-Article-1
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bty077