Transcription Factor-Interaction Prediction and Classification

Participants: Stefano Perna, Pietro Pinoli, Stefano Ceri, Wong Limsoon

Overview

Transcriptional regulation is critical to the cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor (TF) from different families, binding together and attaching to the DNA as a single complex, but only a fraction of the regulation partners of each transcription factor is currently known. It is thus of interest to biologists to analyse data extracted from TF-DNA binding experiments (e.g. ChIP-Seq) to infer the mechanisms that drive transcriptional regulation.

In this project, we develop techniques for predicting the physical interaction between transcription factors, as well as for predicting the nature of their interactions (i.e. co-operative, competitive, or others).

Selected Publications

• Stefano Perna, Arif Canakoglu, Pietro Pinoli, Stefano Ceri, Limsoon Wong. Implementing a transcription factor interaction prediction system using the GenoMetric Query Language. Data Mining for Systems Biology: Methods and Protocols, edited by Hiroshi Mamitsuka, Chapter 6, pages 63--81, Springer, July 2018.

• Stefano Perna, Pietro Pinoli, Stefano Ceri, Limsoon Wong. TICA: Transcription interaction and coregulation analyser. Genomics, Proteomics, & Bioinformatics, 16(5):342--353, October 2018. PDF

• Stefano Perna, Pietro Pinoli, Stefano Ceri, Limsoon Wong. NAUTICA: Classifying transcription factor interactions by positional and protein-protein interaction information. Biology Direct, 15:13, September 2020. PDF

• Stefano Perna, Pietro Pinoli, Stefano Ceri, Limsoon Wong. A comparative analysis of ENCODE and Cistrome in the context of TF binding signal. BMC Genomics, 25(Suppl 3):817, August 2024. PDF

Dissertations

• Stefano Perna, "Data-driven techniques for knowledge discovery in regulomics", PhD thesis, Politecnico di Milano, Italy, 2019

Acknowledgements