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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Published on: May 31, 2011
MORPH: probabilistic alignment combined with hidden Markov models of cis-regulatory modules
1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America. sinhas@cs.uiuc.edu
Plos Computational Biology
|November 14, 2007
Summary
This study introduces MORPH, a novel framework for analyzing cis-regulatory modules (CRMs) by integrating sequence alignment with binding site predictions. This approach offers more accurate insights into CRM evolution across species.
Area of Science:
- Genomics
- Evolutionary Biology
- Bioinformatics
Background:
- Cis-regulatory modules (CRMs) control gene transcription essential for development.
- Cross-species sequence comparison aids CRM prediction and evolution studies.
- Existing methods use standard alignments, ignoring binding site information.
Purpose of the Study:
- To develop a unified probabilistic framework (MORPH) for robust CRM analysis.
- To integrate sequence alignment with binding site predictions for improved accuracy.
- To enable more reliable analysis of cis-regulatory evolution.
Main Methods:
- Developed the MORPH probabilistic framework.
- Integrated alignment and binding site prediction.
- Summed over all possible sequence alignments to handle ambiguity.
- Tested on orthologous CRMs from Drosophila melanogaster and D. mojavensis.
Main Results:
- MORPH provides more robust CRM analysis than traditional methods.
- The framework overcomes computational artifacts of standard alignment tools.
- It offers a more accurate view of cis-regulatory evolution.
- Demonstrated advantages on moderately diverged Drosophila species.
Conclusions:
- MORPH fills the void in accurate regulatory region alignment for evolutionary studies.
- The framework enables more reliable analysis of cis-regulatory module evolution.
- This work advances the field of cis-regulatory evolution research.
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