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Pattern recognition in genetic sequences by mismatch density

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Abstract

A new development is introduced here in the use of dynamic programming in finding pattern similarities in genetic sequences, as was first done by Needleman and Wunsch (1969). A condition of pattern similarity is defined and an algorithm is given which scans any set of similarities and screens out those which fail to meet the condition. When the set to be scanned contains every pair of segments, one from each of two given sequences of lengthsm andn (i.e. every possible location for a pattern similarity), then it completes the scan in a number of computational steps proportional tom·n, leaving those pairs of segments which satisfy the similarity condition. The algorithm is based on the concept of match density, as suggested by Goad and Kanehisa (1982).

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Literature

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Authors and Affiliations

  1. The Rockefeller University, 1230 York Avenue, 10021, NY, U.S.A.

    Peter H. Sellers

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  1. Peter H. Sellers
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Sellers, P.H. Pattern recognition in genetic sequences by mismatch density. Bltn Mathcal Biology 46, 501–514 (1984). https://doi.org/10.1007/BF02459499

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  • DOI: https://doi.org/10.1007/BF02459499

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