Primer on Medical Genomics Part III: Microarray Experiments and Data Analysis

• Published in: Mayo Clinic proceedings Vol. 77; no. 9; pp. 927 - 940
• Main Authors: Tefferi, Ayalew, Bolander, Mark E., Ansell, Stephen M., Wieben, Eric D., Spelsberg, Thomas C.
• Format: Journal Article
• Language: English
• Published: Rochester, MN Mayo Medical Ventures 01.09.2002; Elsevier Limited
• Subjects: Biological and medical sciences; Breast Neoplasms - genetics; Cluster Analysis; Female; Gene Expression Profiling; Genetic Linkage; Genetic Techniques; Genetics, Medical; Genomics; Human Genome Project; Humans; Investigative techniques, diagnostic techniques (general aspects); Leukemia, Myeloid - genetics; Lymphoma, B-Cell - genetics; Medical sciences; Miscellaneous. Technology; Models, Genetic; Molecular Biology; Oligonucleotide Array Sequence Analysis; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Polymerase Chain Reaction; Polymorphism, Genetic; Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics; Sequence Analysis, DNA; Human; Technology; DNA chip; Genetics; Exploration; Oligonucleotide; Molecular biology; Gene expression; Genome; Non-programmatic
• ISSN: 0025-6196
• DOI: 10.1016/S0025-6196(11)62260-X

Genomics has been defined as the comprehensive study of whole sets of genes, gene products, and their interactions as opposed to the study of single genes or proteins. Microarray technology is one of many novel tools that are allowing global and high-throughput analysis of genes and gene products. In addition to an introduction on underlying principles, the current review focuses on the use of both complementary DNA and oligodeoxynucleotide microarrays in gene expression analysis. Genome-wide experiments generate a massive amount of data points that require systematic methods of analysis to extract biologically useful information. Accordingly, the current educational communication discusses different methods of data analysis, including supervised and unsupervised clustering algorithms. Illustrative clinical examples show clinical applications, including (1) identification of candidate genes or pathological pathways (ie, elucidation of pathogenesis); (2) identification of "new" molecular classes of diseases that may be relevant in disease reclassification, prognostication, and treatment selection (ie, class discovery); and (3) use of expression profiles of known disease classes to predict diagnosis and classification of unknown samples (ie, class prediction). The current review should serve as an introduction to the subject for clinician investigators, physicians and medical scientists in training, practicing clinicians, and other students of medicine.