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Discovering regulatory elements in non-coding sequences by analysis of spaced dyads
J van Helden1, A F Rios, J Collado-Vides
1Unité de Conformation des Macromolécules Biologiques, Université Libre de Bruxelles, CP 160/16, 50 av. F. D. Roosevelt, B-1050 Bruxelles, Belgium. jvanheld@ucmb.ulb.ac.be
Nucleic Acids Research
|March 29, 2000
Summary
We developed dyad analysis to discover gene regulatory elements. This method identifies conserved trinucleotide pairs in upstream sequences, aiding in the discovery of transcription factor binding sites.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Microarray technologies generate vast data on gene transcriptional responses across various experimental conditions.
- Clustering genes with similar responses can suggest transcription factor regulons, but many factors remain uncharacterized.
Purpose of the Study:
- To develop a novel computational method for discovering cis-acting regulatory elements from unaligned gene upstream sequences.
- To decipher the underlying mechanisms of common transcriptional responses within gene sets.
Main Methods:
- Developed 'dyad analysis,' a method detecting conserved trinucleotide pairs spaced by a fixed-width non-conserved region.
- Assessed the statistical significance of spaced trinucleotide pair occurrences.
- Applied dyad and single-word analyses to gene clusters from DNA chip experiments.
Main Results:
- Dyad analysis efficiently detects binding sites for C(6)Zn(2)binuclear cluster proteins and other transcription factors.
- The method successfully identified regulatory patterns in gene clusters from DNA chip data.
- Demonstrated the efficacy of dyad and single-word analyses in discovering regulatory elements.
Conclusions:
- Dyad analysis offers a powerful and efficient approach for identifying cis-regulatory elements.
- Combined with single-word analysis, it facilitates the discovery of new regulatory sites for unknown transcription factors.
- This computational tool aids in understanding gene regulation based on transcriptional response data.