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Title: NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information
Authors: Perna, S.
Pinoli, P.
Ceri, S.
Wong, L. 
Keywords: Data-driven analysis
Interaction classification
Protein-protein interactions
TF-TF competition
Transcription factors
Issue Date: 2020
Publisher: BioMed Central Ltd
Citation: Perna, S., Pinoli, P., Ceri, S., Wong, L. (2020). NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information. Biology Direct 15 (1) : 13. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: 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. © 2020 The Author(s).
Source Title: Biology Direct
ISSN: 17456150
DOI: 10.1186/s13062-020-00268-1
Rights: Attribution 4.0 International
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