Abstract
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.
Highlights
Compressing data, of necessity, involves understanding the way information is structured and, if possible, the mechanism by which the information was generated or is destined to be used
In this work we look at how the tools and theoretical constructs, which have been so useful in the development of data compression algorithms, can be used to understand biologically important molecules which can be represented as sequences, such as DNA, RNA, and proteins
We briefly review compression algorithms developed for compressing biological sequences, our main focus is on how conceptual tools in the data compression repertoire have been used in the field of bioinformatics
Summary
Compressing data, of necessity, involves understanding the way information is structured and, if possible, the mechanism by which the information was generated or is destined to be used. The conceptual tools developed in the field of source coding that have guided the development of data compression algorithms are useful instruments for the analysis of how information is organized in general, and in biological systems in particular. In this work we look at how the tools and theoretical constructs, which have been so useful in the development of data compression algorithms, can be used to understand biologically important molecules which can be represented as sequences, such as DNA, RNA, and proteins.
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