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Aligning DNA sequences to minimize the change in protein

Extended Abstract
  • Yufang Hua
  • Tao Jiang
  • Bin Wu
Session V
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1448)

Abstract

We study an alignment model for coding DNA sequences recently proposed by J. Hein that takes into account both DNA and protein information, and attempts to minimize the total amount of evolution at both DNA and protein levels. Assuming that the gap penalty function is affine, we design a quadratic time dynamic programming algorithm for the model. Although the algorithm theoretically solves an open question of Hein, its running time is impractical because of the large constant factor embedded in the quadratic time complexity function. We therefore consider a mild simplification of Hein's model and present a much more efficient algorithm for the simplified model. The algorithms have been implemented and tested on both real and simulated sequences, and it is found that they produce almost identical alignments in most cases.

Keywords

Recurrence Equation Dynamic Programming Algorithm Optimal Alignment Simulated Sequence Quadratic Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Yufang Hua
    • 1
  • Tao Jiang
    • 2
  • Bin Wu
    • 2
  1. 1.Dept 659IBM CanadaTorontoCanada
  2. 2.Department of Computer ScienceMcMaster UniversityHamiltonCanada

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