Detecting Regulatory Motifs in Coexpressed Genes Using Phylogenetic Information
Author Information
Author(s): Pieter Monsieurs, Thijs Gert, Abeer A Fadda, Sigrid CJ De Keersmaecker, Jozef Vanderleyden, Bart De Moor, Kathleen Marchal
Primary Institution: K.U. Leuven
Hypothesis
Can phylogenetic footprinting improve the detection of regulatory motifs in noisy gene expression data?
Conclusion
The developed methodology can reliably identify multiple regulatory motifs lacking a high degree of overrepresentation in a set of coexpressed genes by using both coexpression and phylogenetic information.
Supporting Evidence
- The methodology was tested on known regulatory motifs and biological data sets.
- It showed robustness against noise in gene expression data.
- Compared to other algorithms, it had a lower false positive rate.
- It successfully identified motifs in two biological data sets.
- Phylogenetic information improved motif detection in noisy environments.
Takeaway
This study created a new method to find important patterns in genes that work together, even when the data is messy. It uses information from related species to help find these patterns.
Methodology
The methodology involves identifying conserved regions in orthologous intergenic regions, aligning these regions, clustering them based on similarity, and screening input sequences with detected regulatory motif models.
Limitations
The methodology may miss motifs in genes without available orthologous information.
Digital Object Identifier (DOI)
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