Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/18151
Title: BioStructMap: A Python tool for integration of protein structure and sequence-based features.
Authors: Guy, Andrew J;Irani, Vashti;Richards, Jack S;Ramsland, Paul A
Affiliation: Life Sciences, Burnet Institute, Melbourne, Australia
Departments of Immunology, Monash University, Melbourne, Australia
Departments of Medicine, University of Melbourne, Melbourne, Australia
Infectious Diseases, Monash University, Melbourne, Australia
Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, Australia
Departments of Immunology, Monash University, Melbourne, Australia
Surgery Austin Health, University of Melbourne, Melbourne, Australia
School of Science, RMIT University, Bundoora, Australia
Issue Date: 21-Jun-2018
EDate: 2018-06-21
Citation: Bioinformatics (Oxford, England) 2018; online first: 21 June
Abstract: A sliding window analysis over a protein or genomic sequence is commonly performed, and we present a Python tool, BioStructMap, that extends this concept to three-dimensional (3D) space, allowing the application of a 3D sliding window analysis over a protein structure. BioStructMap is easily extensible, allowing the user to apply custom functions to spatially aggregated data. BioStructMap also allows mapping of underlying genomic sequences to protein structures, allowing the user to perform genetic-based analysis over spatially linked codons-this has applications when selection pressures arise at the level of protein structure. The Python BioStructMap package is available at https://github.com/andrewguy/biostructmap and released under the MIT License. An online server implementing standard functionality is available at https://biostructmap.burnet.edu.au. Supplementary data are available at Bioinformatics online.
URI: http://ahro.austin.org.au/austinjspui/handle/1/18151
DOI: 10.1093/bioinformatics/bty474
ORCID: 0000-0002-2107-2738
PubMed URL: 29931276
Type: Journal Article
Appears in Collections:Journal articles

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