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Finally, the statistical significance of sequence similarity is considered.
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Fourthly, the uses of and techniques for multiple sequence alignment are described. Thirdly, we examine fast, approximate techniques for detecting local similarities. Secondly, we discuss optimal pairwise alignment of sequences using dynamic programming algorithms. First, we describe the use of dot matrix plots to elucidate the structures and features relating a sequence pair. This chapter considers the theory and practice of analyzing sequence similarity as it applies to database searching and sequence alignment.
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The educated application of these automated tools is an essential part of modern molecular biology research. Automated tools can extend human capabilities by orders of magnitude in both speed and accuracy. The volume of molecular sequence data has long since surpassed human information processing capacity for even simple tasks such as searching for related sequences, and with the ever increasing rate at which new sequences are being produced, the need for computer-assisted analysis becomes more and more acute. To effectively realize this potential, however, some understanding of the process of and theoretical basis for sequence comparison is needed as well as a variety of practical tools to access and manipulate the data. Properly approached, molecular sequence data is a rich source of knowledge capable of teaching us much about the structure, function, and evolution of biological macromolecules.