MICROARRAYS AND BIOINFORMATICS OPPORTUNITIES

Jeyakumar Natarajan is a Reader at Dept, of Bioinformatics, Bharathiar University, Coimbatore, India. His Ph.D. is in biomedical informatics from University of Ulster, United Kingdom where he is worked on developing data mining and text mining systems for protein-protein interactions and robust analysis of microarray data. He also holds post- doctoral work at Northwestern Medical School, Northwestern-University, Chicago, US. His research area is the intersection of computer science, biology, and computational linguistics. His current research activities focused on data mining, text mining and machine learning methods for microarray data analysis and interpretation and other high-throughput data from genomics and proteomics. His other research interests include information retrieval, web mining, bio-ontologies, and ontology mining in bioinformatics. Jeyakumar is a frequent invited speaker on the above topics in various universities and research institutions across India.

Gene expression microarrays are now established as a standard tool in biomedical research. Microarrays involve the identification as well as qualitative and quantitative comparison of gene expressed under different conditions, and elucidation of their properties and functions in a large-scale high-throughput format. However to turn these data into biological insight or interpretation of the collected data remains a key challenge in modern biology. As large numbers of genes are involved in such studies conventional methods are unable to deal with these vast amount of data. On the other hand, such processing poses considerable algorithmic and computational challenges

The objective of this tutorial is to provide general bioinformaticians with an introduction of microarrays and its various bioinformatics opportunities. It will first give an overview microarray technology, various types microarrays (e.g. cDNA, Affymetrix, Agilent, etc.) and publicly available microarray resources (e.g. GEO, Array Express etc.) and their usefulness into biomedical research. The second part of the tutorial will state unique bioinformatics opportunities in microarray data analysis in a number of areas, ranging data pro-processing processing tasks to data mining. The gene selection can be a challenging issue as the microarray data is skewed with a large number of genes in one dimension and a few samples in the other dimension. Gene selection algorithms used for the identification of differentially expressed genes. Classification is known as supervised learning and used to determine unknown class labels. Classification methods help to identify disease types and sub-types and select best treatment. Clustering is known as unsupervised learning and used to group similar data items. Clustering techniques used to find functional classes for similarly expressed genes and to refine existing ones. The tutorial will close with an outlook of current hot research topics in such as gene enrichment analysis, text mining and BioNLP methods for microarray data interpretation.