Whole transcriptome cerebral cortex gene analysis in Alzheimerâ€™s diseases
Anitha P. Muttagi1
Seema J. Patel1
1 GM Institute of Technology, Department of Biotechnology, Davanagere, India
2 Division of Bioinformatics, Scientific Bio-Minds, Bangalore, India
Computational Molecular Biology, 2016, Vol. 6, No. 2 doi: 10.5376/cmb.2016.06.0002
Received: 25 Dec., 2015 Accepted: 21 Mar., 2016 Published: 29 Mar., 2016
© 2016 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License
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Preferred citation for this article:
Pooja S., Muttagi A.P., Patel S.J., Gurumurthy H., and Prashantha C.N., 2016, Whole transcriptome cerebral cortex gene analysis in Alzheimer’s diseases, Computational Molecular Biology, 6(2): 1-9
Neurodegenerative disorder such as Alzheimer's is most common form of memory loss observed on aging population. The genetic factors are strongly influenced by risk of accumulation of β-amyloid protein, apolipoprotein (APOE4), intracellular neurofibrillary tangles of hyperphosphorylated tau proteins that form plaques in the extracellular region of brain. There is no clear method to diagnosis Alzheimer’s disease in the earliest stages, but there are few treatments available to reduce the symptoms but there is no clear cure for the disease. A Genetic markers, inflammatory markers and blood based protein markers that could predict onset on disease prognosis. There is a need to identify markers from the entire genome that influenced by interlink with gene-gene, gene-protein and gene-environment interactions. Different transcriptional factors such as amyloid precursor protein gene (Aβ) and the presenilin 1 and 2 genes are major risk for Alzheimer’s disease. Using computational techniques to identify susceptible genetic factors from functional genomics and proteomics to understand transcriptional coregulation and transcription factor binding sites which potentially contribute to Alzheimer’s disease.
Alzheimerâ€™s disease; Transcriptional analysis; Microarray; Biostatistics
Computational Molecular Biology
• Volume 6