NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information.
Data-driven analysis
Interaction classification
Protein−protein interactions
TF-TF competition
Transcription factors
Journal
Biology direct
ISSN: 1745-6150
Titre abrégé: Biol Direct
Pays: England
ID NLM: 101258412
Informations de publication
Date de publication:
16 09 2020
16 09 2020
Historique:
received:
19
02
2019
accepted:
25
08
2020
entrez:
17
9
2020
pubmed:
18
9
2020
medline:
12
8
2021
Statut:
epublish
Résumé
Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition. In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors. NAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds. This article was reviewed by Zoltán Hegedüs and Endre Barta.
Sections du résumé
BACKGROUND
Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition.
RESULTS
In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors.
CONCLUSIONS
NAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds.
REVIEWERS
This article was reviewed by Zoltán Hegedüs and Endre Barta.
Identifiants
pubmed: 32938476
doi: 10.1186/s13062-020-00268-1
pii: 10.1186/s13062-020-00268-1
pmc: PMC7493360
doi:
Substances chimiques
Transcription Factors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
13Subventions
Organisme : European Research Council
ID : 693174
Pays : International
Organisme : Ministry of Education - Singapore
ID : T1 251RES1725
Pays : International
Références
Nucleic Acids Res. 2010 Jan;38(Database issue):D497-501
pubmed: 19884131
Nat Struct Mol Biol. 2013 Aug;20(8):1008-14
pubmed: 23851461
Bioinformatics. 2019 Feb 15;35(4):711-719
pubmed: 30084962
J Biol Chem. 1998 Aug 7;273(32):20175-9
pubmed: 9685363
Mol Cell Biol. 1999 Aug;19(8):5504-11
pubmed: 10409740
Nucleic Acids Res. 2017 Jan 4;45(D1):D369-D379
pubmed: 27980099
Mol Cancer Res. 2011 Dec;9(12):1587-607
pubmed: 21940756
BMC Genomics. 2014 Mar 19;15:208
pubmed: 24640962
Mol Cell Biol. 2001 Feb;21(3):691-702
pubmed: 11154257
Nat Cell Biol. 2011 Nov 20;13(12):1395-405
pubmed: 22101514
Bioinformatics. 2014 Sep 1;30(17):i415-21
pubmed: 25161228
Nucleic Acids Res. 2011 Jul;39(Web Server issue):W391-9
pubmed: 21602269
Nucleic Acids Res. 2011 Jul;39(12):e78
pubmed: 21470963
J Biol Chem. 2006 May 5;281(18):12495-505
pubmed: 16540471
Comput Struct Biotechnol J. 2014 Sep 03;11(18):22-7
pubmed: 25379140
Bioinformatics. 2009 Aug 1;25(15):1891-7
pubmed: 19435747
Genomics Proteomics Bioinformatics. 2018 Oct;16(5):342-353
pubmed: 30578913
PLoS One. 2018 Jul 17;13(7):e0199771
pubmed: 30016330
Science. 1999 Oct 15;286(5439):509-12
pubmed: 10521342
J Biol Chem. 1996 Sep 27;271(39):23999-4004
pubmed: 8798634
Genes Dev. 2010 Dec 15;24(24):2812-22
pubmed: 21159821