Browsing by Subject "Microarray data analysis"

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Open Access
Analysis of differentially expressed genes in bipolar disorder: transcriptomic signature of adolescence and young adulthood in working memory-relared area
(2020-11) Şen, Rabia
Bipolar disorder (BD) is a heritable severe illness. One of the indications of BD is working memory (WM) impairment which is a heritable cognitive trait. The aim of the current study is to identify the transcriptomic level developmental biomarkers of BD in WM-related brain regions. We based our analysis on adolescence and young adulthood (AYA), the critical period for both BD and cognitive development. We have chosen 4 publicly available microarray datasets from Gene Omnibus database for which one is derived from healthy controls and three from bipolar disorder patients. We compared different developmental periods of the brains of normal subjects to determine healthy brain development at the transcriptomic level. After applying the same method to detect bipolar development to show differences between BD and healthy brains. We followed these comparisons in two steps; on gene-level analysis and geneset level analysis. Next, we identified common genes and pathways from the results of different analyses. As a result of this comparison, while six genes were identified differentially expressed, we observed 5 Gene Ontology (GO) genesets shown different regulation patterns in bipolar and healthy brains. The literature review has been shown that the significant biological pathways might be influenced by the treatment.
Open Access
Pathway activity inference using microarray data
(Bilkent Center for Bioinformatics (BCBI), 2004) Babur, Özgün; Demir, Emek; Ayaz, Aslı; Doğrusöz, Uğur; Sakarya, Onur
Motivation: Microarray technology provides cell-scale expression data; however, analyzing this data is notoriously difficult. It is becoming clear that system-oriented methods are needed in order to best interpret this data. Combining microarray expression data with previously built pathway models may provide useful insight about the cellular machinery and reveal mechanisms that govern diseases. Given a qualitative state - transition model of the cellular network and an expression profile of RNA molecules, we would like to infer possible differential activity of the other molecules such as proteins on this network. Results: In this paper an efficient algorithm using a new approach is proposed to attack this problem. Using the regulation relations on the network, we determine possible scenarios that might lead to the expression profile, and qualitatively infer the activity differences of the molecules between test and control samples. Availability: This new analysis method has been implemented as part of a microarray data analysis component within PATIKA (Pathway Analysis Tool for Integration and Knowledge Acquisition), which is a software environment for pathway storage, integration and analysis. Facilities for easy analysis and visualization of the results is also provided. Contact: http://www.patika.org.