| Literature DB >> 29444205 |
Nathan Mih1,2, Elizabeth Brunk2, Ke Chen2, Edward Catoiu2, Anand Sastry2, Erol Kavvas2, Jonathan M Monk2, Zhen Zhang2, Bernhard O Palsson2.
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.Entities:
Mesh:
Year: 2018 PMID: 29444205 PMCID: PMC6658713 DOI: 10.1093/bioinformatics/bty077
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937