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Published on: June 21, 2016
Classification and predictive modeling of liver X receptor response elements
1Discovery Informatics, Eli Lilly and Company, Greenfield, Indiana 46140, USA. gvarga@lilly.com
Background:
The liver X receptor (LXR), a transcription factor that forms a heterodimer with the retinoid X receptor, plays a key role in the transcriptional regulation of many important genes implicated in prevalent metabolic diseases. In spite of numerous studies, a complete list of LXR direct target genes remains elusive. To complement experimental approaches, computational prediction can be used to help build such a list because all LXR target genes are expected to carry the response elements (LXREs) in their promoter or enhancer regions. In practice, however, such a prediction has been hampered by the inaccuracies of currently available predictive models of LXREs. We report on a novel computational application for the highly accurate prediction of LXREs in DNA sequences.
Methods:
We first conducted a comprehensive review of experimentally determined LXR target genes and collected all known LXREs. Subsequently, all such sites were classified using various computational methods based on sequence similarity to identify multiple subtypes. A library of Hidden Markov Models (LXRE.HMM) was developed to represent all subtypes and to enable the promoter scanning of LXR target genes.
Results And Conclusion:
Our model outperformed the widely used LXRE model in MatInspector in identifying the LXREs for all known LXR direct target genes at the experimentally verified positions. As a result, this new approach will make the genomewide prediction of LXR target genes feasible.
Insights
A new computational tool accurately predicts liver X receptor response elements (LXREs), aiding in identifying target genes for metabolic diseases. This advance enables genomewide prediction of LXR target genes.
Area of Science:
- Molecular Biology
- Genomics
- Computational Biology
Background:
- Liver X receptor (LXR) is crucial for regulating genes involved in metabolic diseases.
- Identifying all direct LXR target genes remains a challenge.
- Computational prediction of LXR response elements (LXREs) is hindered by inaccurate models.
Purpose of the Study:
- To develop a novel computational application for highly accurate prediction of LXREs.
- To improve the identification of LXR direct target genes.
Main Methods:
- Comprehensive review of experimentally determined LXR target genes and known LXREs.
- Classification of LXREs into subtypes using computational methods based on sequence similarity.
- Development of a Hidden Markov Model (HMM) library (LXRE.HMM) for promoter scanning.
Main Results:
- The developed LXRE.HMM model demonstrated superior performance compared to the widely used MatInspector model.
- The model accurately identified LXREs at experimentally verified positions for known LXR direct target genes.
Conclusions:
- The novel computational approach significantly enhances the accuracy of LXRE prediction.
- This method makes genomewide prediction of LXR target genes feasible, advancing metabolic disease research.
