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Classification and predictive modeling of liver X receptor response elements.

Gabor Varga¹, Chen Su

¹Discovery Informatics, Eli Lilly and Company, Greenfield, Indiana 46140, USA. gvarga@lilly.com

Biodrugs : Clinical Immunotherapeutics, Biopharmaceuticals and Gene Therapy

|April 4, 2007

Summary

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.

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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.