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NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information

Authors
  • Perna, Stefano1
  • Pinoli, Pietro1
  • Ceri, Stefano1
  • Wong, Limsoon2
  • 1 Politecnico di Milano, Via Giuseppe Ponzio 34/5, Milan, 20133, Italy , Milan (Italy)
  • 2 National University of Singapore, Singapore, Singapore , Singapore (Singapore)
Type
Published Article
Journal
Biology Direct
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Sep 16, 2020
Volume
15
Issue
1
Identifiers
DOI: 10.1186/s13062-020-00268-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundInferring 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.ResultsIn 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.ConclusionsNAUTICA 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.ReviewersThis article was reviewed by Zoltán Hegedüs and Endre Barta.

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