Gene Prediction in Metagenomic Libraries Using the Self Organising Map and High Performance Computing Techniques

  • Nigel McCoy
  • Shaun Mahony
  • Aaron Golden
Conference paper

DOI: 10.1007/978-3-540-69968-2_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4360)
Cite this paper as:
McCoy N., Mahony S., Golden A. (2007) Gene Prediction in Metagenomic Libraries Using the Self Organising Map and High Performance Computing Techniques. In: Dubitzky W., Schuster A., Sloot P.M.A., Schroeder M., Romberg M. (eds) Distributed, High-Performance and Grid Computing in Computational Biology. Lecture Notes in Computer Science, vol 4360. Springer, Berlin, Heidelberg

Abstract

This paper describes a novel approach for annotating metagenomic libraries obtained from environmental samples utilising the self organising map (SOM) neural network formalism. A parallel implementation of the SOM is presented and its particular usefulness in metagenomic annotation highlighted. The benefits of the parallel algorithm and performance increases are explained, the latest results from annotation on an artificially generated metagenomic library presented and the viability of this approach for implementation on existing metagenomic libraries is assessed.

Keywords

Self Organising Map Metagenomics HPC MPI 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Nigel McCoy
    • 1
  • Shaun Mahony
    • 2
  • Aaron Golden
    • 1
  1. 1.National University of Ireland, GalwayIreland
  2. 2.Dept of Computational Biology, University of Pittsburgh, Pittsburgh, PAUSA

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