StonPy: a tool to parse and query collections of SBGN maps in a graph database.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 03 2023
Historique:
received: 23 08 2022
revised: 14 01 2023
pubmed: 11 3 2023
medline: 21 3 2023
entrez: 10 3 2023
Statut: ppublish

Résumé

The systems biology graphical notation (SBGN) has become the de facto standard for the graphical representation of molecular maps. Having rapid and easy access to the content of large collections of maps is necessary to perform semantic or graph-based analysis of these resources. To this end, we propose StonPy, a new tool to store and query SBGN maps in a Neo4j graph database. StonPy notably includes a data model that takes into account all three SBGN languages and a completion module to automatically build valid SBGN maps from query results. StonPy is built as a library that can be integrated into other software and offers a command-line interface that allows users to easily perform all operations. StonPy is implemented in Python 3 under a GPLv3 license. Its code and complete documentation are freely available from https://github.com/adrienrougny/stonpy. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 36897014
pii: 7075543
doi: 10.1093/bioinformatics/btad100
pmc: PMC10017094
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIGMS NIH HHS
ID : P41 GM103504
Pays : United States

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press.

Références

J Integr Bioinform. 2015 Sep 04;12(2):264
pubmed: 26528562
J Integr Bioinform. 2015 Sep 04;12(2):265
pubmed: 26528563
J Integr Bioinform. 2019 Jun 13;16(2):
pubmed: 31199769
Nat Biotechnol. 2009 Aug;27(8):735-41
pubmed: 19668183
J Integr Bioinform. 2020 Jun 22;17(2-3):
pubmed: 32568733
Nucleic Acids Res. 2019 Jan 8;47(D1):D419-D426
pubmed: 30407594
PLoS Comput Biol. 2018 Jan 29;14(1):e1005968
pubmed: 29377902
Brief Bioinform. 2021 Sep 2;22(5):
pubmed: 33834185
BMC Bioinformatics. 2016 Dec 5;17(1):494
pubmed: 27919219
Oncogenesis. 2015 Jul 20;4:e160
pubmed: 26192618
Brief Bioinform. 2021 Sep 2;22(5):
pubmed: 33758926

Auteurs

Adrien Rougny (A)

Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.
Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Tokyo 169-8555, Japan.

Irina Balaur (I)

Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.

Augustin Luna (A)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.

Alexander Mazein (A)

Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Cephalometry Humans Anatomic Landmarks Software Internet

Classifications MeSH