NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-09-01
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| Series: | Biology Direct |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13062-020-00268-1 |
| _version_ | 1828862401309573120 |
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| author | Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong |
| author_facet | Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong |
| author_sort | Stefano Perna |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T03:23:08Z |
| format | Article |
| id | doaj.art-327f0697ead14375abf691059df5a367 |
| institution | Directory Open Access Journal |
| issn | 1745-6150 |
| language | English |
| last_indexed | 2024-12-13T03:23:08Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Biology Direct |
| spelling | doaj.art-327f0697ead14375abf691059df5a3672022-12-22T00:01:19ZengBMCBiology Direct1745-61502020-09-0115111810.1186/s13062-020-00268-1NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction informationStefano Perna0Pietro Pinoli1Stefano Ceri2Limsoon Wong3Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di MilanoDipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di MilanoDipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di MilanoNational University of SingaporeAbstract 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.http://link.springer.com/article/10.1186/s13062-020-00268-1Transcription factorsInteraction classificationProtein−protein interactionsTF-TF competitionData-driven analysis |
| spellingShingle | Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information Biology Direct Transcription factors Interaction classification Protein−protein interactions TF-TF competition Data-driven analysis |
| title | NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information |
| title_full | NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information |
| title_fullStr | NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information |
| title_full_unstemmed | NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information |
| title_short | NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information |
| title_sort | nautica classifying transcription factor interactions by positional and protein protein interaction information |
| topic | Transcription factors Interaction classification Protein−protein interactions TF-TF competition Data-driven analysis |
| url | http://link.springer.com/article/10.1186/s13062-020-00268-1 |
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