StrongestPath: a Cytoscape application for protein-protein interaction analysis.

Cytoscape App Pathway reconstruction Protein–protein interaction network Regulatory pathway Signaling network

Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
29 Jun 2021
Historique:
received: 30 12 2020
accepted: 02 06 2021
entrez: 30 6 2021
pubmed: 1 7 2021
medline: 2 7 2021
Statut: epublish

Résumé

StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, the application is able to process them and identify the cascade of interactions with the highest total confidence score. Given a set of proteins, StrongestPath can extract a set of possible interactions between the input proteins, and expand the network by adding new proteins that have the most interactions with highest total confidence to the current network of proteins. The application can also identify any activating or inhibitory regulatory paths between two distinct sets of transcription factors and target genes. This application can be used on the built-in human and mouse PPI or signaling databases, or any user-provided database for some organism. Our results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in a pathway by finding the strongest path(s) in the PPI or signaling network. Easy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform, and providing a user-friendly graphical interface make StrongestPath an extremely useful application.

Sections du résumé

BACKGROUND BACKGROUND
StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, the application is able to process them and identify the cascade of interactions with the highest total confidence score. Given a set of proteins, StrongestPath can extract a set of possible interactions between the input proteins, and expand the network by adding new proteins that have the most interactions with highest total confidence to the current network of proteins. The application can also identify any activating or inhibitory regulatory paths between two distinct sets of transcription factors and target genes. This application can be used on the built-in human and mouse PPI or signaling databases, or any user-provided database for some organism.
RESULTS RESULTS
Our results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in a pathway by finding the strongest path(s) in the PPI or signaling network.
CONCLUSION CONCLUSIONS
Easy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform, and providing a user-friendly graphical interface make StrongestPath an extremely useful application.

Identifiants

pubmed: 34187355
doi: 10.1186/s12859-021-04230-4
pii: 10.1186/s12859-021-04230-4
pmc: PMC8244221
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

352

Références

Bioinformatics. 2011 Feb 1;27(3):431-2
pubmed: 21149340
Genome Biol. 2010 Jan 12;11(1):R3
pubmed: 20067622
Bioinformatics. 2011 Jun 15;27(12):1739-40
pubmed: 21546393
Database (Oxford). 2015 Dec 26;2015:
pubmed: 26708988
Nucleic Acids Res. 2011 Jan;39(Database issue):D691-7
pubmed: 21067998
BMC Bioinformatics. 2002 Nov 01;3:34
pubmed: 12413400
Nucleic Acids Res. 2017 Jan 4;45(D1):D408-D414
pubmed: 27794551
Genome Res. 2003 Nov;13(11):2498-504
pubmed: 14597658
NPJ Syst Biol Appl. 2016 Mar 03;2:16002
pubmed: 28725467
Database (Oxford). 2011 Sep 29;2011:bar032
pubmed: 21959865
Nucleic Acids Res. 2015 Jan;43(Database issue):D204-12
pubmed: 25348405
Bioinformatics. 2007 Jul 1;23(13):1708-9
pubmed: 17463016
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
F1000Res. 2015 Aug 5;4:484
pubmed: 27781081
Oncogene. 2017 Jul 13;36(28):3943-3956
pubmed: 28288132
Nucleic Acids Res. 2011 Jan;39(Database issue):D561-8
pubmed: 21045058
Nucleic Acids Res. 2018 Jan 4;46(D1):D380-D386
pubmed: 29087512
F1000Res. 2017 Jan 20;6:58
pubmed: 28413614
Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5
pubmed: 17099226

Auteurs

Zaynab Mousavian (Z)

Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran. zmousavian@ut.ac.ir.

Mehran Khodabandeh (M)

School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.

Ali Sharifi-Zarchi (A)

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Department of Stem cells and Developmental Biology at the Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.

Alireza Nadafian (A)

Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.

Alireza Mahmoudi (A)

Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.

Articles similaires

Robotic Surgical Procedures Animals Humans Telemedicine Models, Animal

Odour generalisation and detection dog training.

Lyn Caldicott, Thomas W Pike, Helen E Zulch et al.
1.00
Animals Odorants Dogs Generalization, Psychological Smell
Animals TOR Serine-Threonine Kinases Colorectal Neoplasms Colitis Mice
Animals Tail Swine Behavior, Animal Animal Husbandry

Classifications MeSH