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Establishing a statistic model for recognition of steroid hormone response elements
Maria Stepanova1, Feng Lin, Valerie C-L Lin
1Bioinformatics Research Centre, Nanyang Technological University, Nanyang Avenue, 50 Nanyang Drive, Singapore 639798, Singapore. mstepanova@pmail.ntu.edu.sg
Computational Biology and Chemistry
|September 15, 2006
Summary
This study introduces a novel bioinformatics tool for identifying progesterone response elements (PREs), crucial for understanding hormone-regulated gene expression. The developed model achieves high accuracy in predicting these elements in vertebrate genomes.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Hormone-regulated gene expression is critical, but identifying specific hormone response elements (HREs) like progesterone response elements (PREs) remains challenging.
- Existing bioinformatics tools lack the accuracy needed for reliable PRE recognition.
Purpose of the Study:
- To develop a more accurate in silico method for identifying specific HREs, particularly PREs.
- To establish a robust statistic model for predicting hormone primary target genes.
Main Methods:
- A comprehensive survey and comparison of in silico methods were performed.
- A reliable training dataset was constructed from over 150 literature sources of validated response elements.
- Mono- and di-nucleotide Position Weight Matrices and a profile Hidden Markov Model were employed.
- A qualitative scheme was applied to assess the probability of genes being hormone primary targets.
Main Results:
- The study observed di-nucleotide preservation in verified HREs.
- Combining statistic and transition patterns significantly improved model sensitivity and specificity.
- The model achieved 73% sensitivity and a specificity of one prediction per 8.24 kb using over 650 validated elements.
Conclusions:
- The developed model offers a significant advancement in identifying PREs and predicting hormone primary target genes.
- This tool can be applied to human and other vertebrate genomes for analyzing upstream promoters.
- A web-based program and supplementary data are available for academic research.
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