Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates
2007
Identifying Statistical Dependence in Genomic Sequences
publication
Evidence: moderate
Author Information
Author(s): Aktulga Hasan Metin, Kontoyiannis Ioannis, Lyznik L Alex, Szpankowski Lukasz, Grama Ananth Y, Szpankowski Wojciech
Primary Institution: Purdue University
Hypothesis
Can mutual information estimates effectively identify statistical dependencies in genomic sequences?
Conclusion
The study successfully demonstrates a methodology for identifying significant dependencies in genomic sequences using mutual information.
Supporting Evidence
- The methodology developed is based on mutual information to find dependencies in genomic sequences.
- Significant dependencies were found between the untranslated region in zmSRp32 and its alternatively spliced exons.
- The approach was applied to data from the FBI's CODIS for discovering short tandem repeats.
Takeaway
This study shows how to find connections in DNA and RNA that might help us understand how genes work together.
Methodology
The study uses information-theoretic tools to identify statistical correlations in biomolecules.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website