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Genomics II: Bacteria, Viruses and Metabolic Pathways
Title
Genomics II: Bacteria, Viruses and Metabolic Pathways
Editor
iConcept Press
Price
USD$139.00
ISBN
978-1-480254-145
Clicks
22865

Chapter 4

Genomics II: Bacteria, Viruses and Metabolic Pathways

High-throughput Gene Expression Analysis Concepts and Applications

by Arsen Arakelyan, Levon Aslanyan and Anna Boyajyan

Viewed: 3755

Abstract

High-throughput gene expression analysis is one of the fundamental approaches for understanding multiple cellular and molecular processes underlying health and disease. This approach is widely used not only in basic biomedical research, but also in clinical medicine, biotechnological and pharmaceutical applications. However, bringing technological breakthrough in biological data acquisition, high-throughput approach has also entailed challenges in related data analysis and interpretation. Two main aspects that attract a lot of attention include the use of proper algorithms for identification of differentially expressed genes and correct biological interpretation of results obtained. Though several new algorithms have been introduced, there is still a need to improve the existing and develop new algorithms for analysis of high-throughput gene expression data. Microarray gene expression assay is the most commonly used technique for genome analysis, although new approaches have recently been introduced. To obtain reliable results, large sample size is needed. However, due to the high costs of microarray experiments and problems to ensure enough samples, researchers often follow to commonly accepted guideline, where the minimum number of variants is 5. Thus, a survey conducted by us in Gene Expression Omnibus, demonstrated that in half of the 20000 gene expression experiments a sample size is less than 10, and an average number of probes per chip is equal to 18000. In other words, the number of variables (genes) in the database far exceeds the number of variants (samples), which is called in mathematics “high dimensional low sample size data”. To convert this type of data into meaningful results, a special analysis is required. In this chapter we discuss current advances and limitations in modern high-throughput gene expression analysis and introduce several most popular algorithms and software for microarray and RNA-sequencing analysis and interpretation. In addition, a new algorithm developed by us for high dimensional low sample size microarray gene expression data analysis combined with in-depth functional pathway analysis is presented. This algorithm is also suitable for high-level analysis of large-scale data obtained by application of next-generation sequencing and transcriptome analysis, and other modern post-genomic sequencing technologies.

Author Details

Arsen Arakelyan
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Armenia
Levon Aslanyan
Laboratory of Discrete Modeling, Analysis and Recognition, Institute for Informatics and Automation Problems, National Academy of Sciences, Armenia
Anna Boyajyan
Laboratory of Macromolecular Complexes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia, Armenia, Armenia

Citation

Arsen Arakelyan, Levon Aslanyan and Anna Boyajyan. High-throughput Gene Expression Analysis Concepts and Applications. In Genomics II: Bacteria, Viruses and Metabolic Pathways. ISBN:978-1-480254-145. iConcept Press. 0000.

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