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Exhaustive Peptide Searching Using Relations

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Data Management. Data, Data Everywhere (BNCOD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4587))

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Abstract

We present a new robust solution to short peptide searching, tested on a relational platform, with a set of biological queries. Our algorithm is appropriate for large scale scientific data analysis, and has been tested with 1.4 GB of amino-acids. Protein sequences are indexed as short overlapping string windows, and stored in a relation. To find approximate matches, we use a neighbourhood generation algorithm. The words in the neighbourhood are then fetched and stored in a relation. We measure execution time and compare the matches found to those delivered by BLAST. We report some performance gains in exact matching and searching within edit distance 1, and very significant quality improvements over heuristics, as we guarantee to deliver all relevant matches.

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Authors and Affiliations

  1. Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland

    Ela Hunt

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  1. Ela Hunt
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Richard Cooper Jessie Kennedy

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© 2007 Springer-Verlag Berlin Heidelberg

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Hunt, E. (2007). Exhaustive Peptide Searching Using Relations. In: Cooper, R., Kennedy, J. (eds) Data Management. Data, Data Everywhere. BNCOD 2007. Lecture Notes in Computer Science, vol 4587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73390-4_3

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  • DOI: https://doi.org/10.1007/978-3-540-73390-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73389-8

  • Online ISBN: 978-3-540-73390-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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