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07:55
Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Published on: May 31, 2011
Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation
F P Roth1, J D Hughes, P W Estep
1Harvard University Graduate Biophysics Program and Harvard Medical School Department of Genetics, Boston, MA 02115, USA.
Nature Biotechnology
|October 27, 1998
Summary
This study introduces a method to find gene regulatory motifs by analyzing DNA sequences of responsive genes. The approach successfully identified known motifs and coregulated genes in yeast systems.
Area of Science:
- Computational Biology
- Molecular Genetics
- Systems Biology
Background:
- Identifying gene regulatory elements is crucial for understanding cellular responses.
- Whole-genome mRNA quantitation provides a global view of gene expression changes.
- Analyzing non-coding DNA upstream of responsive genes can reveal regulatory mechanisms.
Purpose of the Study:
- To develop and test a strategy for identifying regulatory DNA motifs and coregulated genes.
- To apply this strategy to well-characterized regulatory systems in Saccharomyces cerevisiae.
Main Methods:
- Whole-genome mRNA quantitation to identify responsive genes.
- Bioinformatic analysis of upstream non-coding DNA sequences to find conserved motifs.
- Comparison of identified motifs and gene sets with known regulatory elements and genes.
Main Results:
- Successfully identified the Gal4 binding site and galactose-induced genes in the galactose response system.
- Discovered the cell-cycle activation motif and genes encoding nucleosomal proteins in the heat shock system.
- Identified all four relevant DNA motifs and most known a- and alpha-specific genes in the mating type system.
Conclusions:
- The developed strategy is effective for discovering regulatory motifs and coregulated gene sets.
- This approach offers a powerful tool for dissecting gene regulation across different biological systems.
- The findings highlight the utility of integrating gene expression data with sequence analysis for regulatory discovery.
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