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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
Published on: June 27, 2020
Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription
1Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan.
BMC Systems Biology
|August 18, 2012
Summary
This study introduces a novel method to refine gene targets identified through transcription factor knockout microarrays (TFKMs). By integrating TF binding network data, it filters false positives and generates testable hypotheses for gene regulation mechanisms.
Area of Science:
- Systems Biology
- Bioinformatics
- Genomics
Background:
- Transcription factor knockout microarrays (TFKMs) identify gene targets but can include false positives.
- Statistical analysis of TFKMs reveals differentially expressed genes but not underlying regulatory mechanisms.
Purpose of the Study:
- To develop a method for filtering false positives from TFKMs.
- To extract biologically interpretable TF knockout targets.
- To generate testable hypotheses on TF regulatory mechanisms.
Main Methods:
- Constructed a TF binding network using ChIP-chip data from the YEASTRACT database.
- Developed a path search algorithm to identify connections between knocked-out TFs and their targets within the network.
- Defined 'biologically interpretable' targets as those with an identified path from the TF.
Main Results:
- Successfully filtered false positives from initial TF knockout targets.
- Extracted biologically interpretable TF knockout targets with identified regulatory paths.
- Refined targets showed improved functional enrichment, expression coherence, and protein-protein interactions compared to original targets.
Conclusions:
- Jointly analyzing TFKMs and ChIP-chip data enables extraction of interpretable TF targets and regulatory mechanism hypotheses.
- Identified paths serve as experimentally testable hypotheses for TF regulation.
- Generated hypotheses have been experimentally validated, demonstrating the power of integrating diverse data sources.
