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Accelerating the Neighbor-Joining Algorithm Using the Adaptive Bucket Data Structure

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Bioinformatics Research and Applications (ISBRA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4983))

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Abstract

The complexity of the neighbor joining method is determined by the complexity of the search for an optimal pair (”neighbors to join”) performed globally at each iteration. Accelerating the neighbor-joining method requires performing a smarter search for an optimal pair of neighbors, avoiding re-evaluation of all possible pairs of points at each iteration.

We developed an acceleration technique for the neighbor-joining method that significantly decreases complexity for important applications without any change in the neighbor-joining method. This technique utilizes the bucket data structure. The pairs of nodes are arranged in buckets according to values of the goal function δ ij  = u i  + u j  − d ij . Buckets are adaptively re-arranged after each neighbor-joining step. While the pairs of nodes in the top bucket are re-evaluated at every iteration, pairs in lower buckets are accessed more rarely, when the algorithm determines that the elements of the bucket need to be re-evaluated based on new values of δ ij . As a result, only a small portion of candidate pairs of nodes is examined at each iteration.

The algorithm is cache efficient, since the bucket data structures are able to exploit locality and adjust to cache properties.

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Authors and Affiliations

  1. National Center for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, MD, 20894, USA

    Leonid Zaslavsky & Tatiana A. Tatusova

Authors
  1. Leonid Zaslavsky
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  2. Tatiana A. Tatusova
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Ion Măndoiu Raj Sunderraman Alexander Zelikovsky

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Zaslavsky, L., Tatusova, T.A. (2008). Accelerating the Neighbor-Joining Algorithm Using the Adaptive Bucket Data Structure. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_12

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  • DOI: https://doi.org/10.1007/978-3-540-79450-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79449-3

  • Online ISBN: 978-3-540-79450-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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