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Data Compression Concepts and Algorithms and Their Applications to Bioinformatics

Department of Electrical Engineering, University of Nebraska-Lincoln, NE 68588-0511, USA
Author to whom correspondence should be addressed.
Entropy 2010, 12(1), 34-52;
Received: 4 December 2009 / Accepted: 17 December 2009 / Published: 29 December 2009
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Data compression at its base is concerned with how information is organized in data. Understanding this organization can lead to efficient ways of representing the information and hence data compression. In this paper we review the ways in which ideas and approaches fundamental to the theory and practice of data compression have been used in the area of bioinformatics. We look at how basic theoretical ideas from data compression, such as the notions of entropy, mutual information, and complexity have been used for analyzing biological sequences in order to discover hidden patterns, infer phylogenetic relationships between organisms and study viral populations. Finally, we look at how inferred grammars for biological sequences have been used to uncover structure in biological sequences. View Full-Text
Keywords: bioinformatics; data compression; information theory bioinformatics; data compression; information theory

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Nalbantoglu, Ö.U.; Russell, D.J.; Sayood, K. Data Compression Concepts and Algorithms and Their Applications to Bioinformatics. Entropy 2010, 12, 34-52.

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