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Related Experiment Videos

Transcription factor binding element detection using functional clustering of mutant expression data.

Gengxin Chen1, Naoya Hata, Michael Q Zhang

  • 1Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.

Nucleic Acids Research
|April 30, 2004
PubMed
Summary
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This study introduces a novel method to identify transcription factor binding motifs using gene expression data from mutant organisms. The approach effectively detects motifs, aiding in understanding gene regulation and function.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene mutation is crucial for understanding gene function.
  • High-throughput technologies like DNA microarrays monitor genome-wide gene expression changes in mutants.

Purpose of the Study:

  • To develop a simple method for detecting transcription-factor-binding motifs.
  • To utilize microarray expression data from transcription factor deletion mutants.

Main Methods:

  • Clustering differentially expressed genes based on functional annotations (e.g., Gene Ontology).
  • Analyzing eight microarray datasets from the Rosetta Compendium.
  • Utilizing chromatin immunoprecipitation (ChIP) data.

Main Results:

Related Experiment Videos

  • Successfully detected canonical binding motifs for at least four transcription factors.
  • Predicted a variant Swi4 binding motif and recovered a core motif for Arg80.
  • Demonstrated the method's applicability across various datasets.

Conclusions:

  • The developed approach is effective for identifying transcription factor binding motifs.
  • The method is adaptable to various molecular biology techniques (e.g., knockout, RNAi).
  • Functional clustering provides insights into transcription factor roles.