Computational Identification of Alzheimer's Disease Specific
Transcription Factors using Microarray Gene Expression Data |
Vishalini Krishnamurthy, Nicy Sweety Issac and Jeyakumar Natarajan* |
| Data and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore 641046, India |
| *Corresponding author: |
Dr. N. Jeyakumar, Data and Text Mining Laboratory,
Department of Bioinformatics, Bharathiar University,
Coimbatore
641046, India,
E-mail: n.jeyakumar@yahoo.co.in |
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| Received November 05, 2009; Accepted December 18, 2009; Published
December 20, 2009 |
| Citation: Krishnamurthy V, Issac NS, Natarajan J (2009) Computational
Identification of Alzheimer’s Disease Specific Transcription Factors using
Microarray Gene Expression Data. J Proteomics Bioinform 2: 505- 508. doi:10.4172/jpb.1000113 |
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| Copyright: ©2009 Krishnamurthy V, et al. This is an open-access article
distributed under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction
in any medium, provided the original author and source are credited. |
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| Abstract |
| Alzheimer’s disease is the most common form of dementia
affecting millions of older people world wide. Identification
of transcriptional factor binding sites of disease
specific co-expressed genes and the possible transcriptional
regulation of the genes will lead to a better understanding
of complex diseases such as Alzheimer’s disease.
However, the regulatory mechanisms driving these
changes, in particular the networks of transcription factors
involved, is not fully understood to date. The computational
identification of conserved TFBS in the regulatory
regions of hundreds of genes at a time especially suited
for microarray gene expression datasets. We report clusters
of co-expressed genes and the identification of conserved
TFBSs using microarray gene expression data sets.
We investigated microarray gene expression data from
Gene Expression Omnibus (GEO) specific to Alzheimer’s
disease. The dataset consists of 14 normal and 14
Alzheimer disease samples. Differential expression analysis
results 240 differentially expressed genes which are
more significant. Hierarchical clustering of these significance
genes shows eight clusters of co-expressed genes.
The detection of over-represented transcription factor binding
sites in the promoters regions of co- expressed genes
reveals transcription factor binding site classes ZEB1,
MZF1 1-4, ZNF354C, ELF5 and SPIB in upstream of
human promoter and responsible for apoptosis. |
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