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Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell

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Practical Aspects of Declarative Languages (PADL 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5418))

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Abstract

Analysis of biological data often involves large data sets and computationally expensive algorithms. Databases of biological data continue to grow, leading to an increasing demand for improved algorithms and data structures. Despite having many advantages over more traditional indexing structures, the Bloom filter is almost unused in bioinformatics. Here we present a robust and efficient Bloom filter implementation in Haskell, and implement a simple bioinformatics application for indexing and matching sequence data. We use this to index the chromosomes that make up the human genome, and map all available gene sequences to it. Our experiences with developing and tuning our application suggest that for bioinformatics applications, Haskell offers a compelling combination of rapid development, quality assurance, and high performance.

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Author information

Authors and Affiliations

  1. Institute of Marine Research, Bergen, Norway

    Ketil Malde

  2. Serpentine Green Design, San Francisco, USA

    Bryan O’Sullivan

Authors
  1. Ketil Malde
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  2. Bryan O’Sullivan
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Editor information

Editors and Affiliations

  1. University of Kansas, 2001 Eaton Hall, 1520 W. 15th St., KS 66045-7621, Lawrence, USA

    Andy Gill

  2. Universidade Nova de Lisboa, CENTRIA, PO Box 325, WV 25443, Sheperdstown, USA

    Terrance Swift

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© 2008 Springer-Verlag Berlin Heidelberg

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Malde, K., O’Sullivan, B. (2008). Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell. In: Gill, A., Swift, T. (eds) Practical Aspects of Declarative Languages. PADL 2009. Lecture Notes in Computer Science, vol 5418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92995-6_13

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  • DOI: https://doi.org/10.1007/978-3-540-92995-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92994-9

  • Online ISBN: 978-3-540-92995-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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