Gene prediction in metagenomic libraries using the self organising map and high performance computing techniques

Nigel McCoy, Shaun Mahony, Aaron Golden

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationDistributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006, Proceedings
PublisherSpringer Verlag
Pages99-109
Number of pages11
ISBN (Print)3540698418, 9783540698418
DOIs
StatePublished - Jan 1 2007
EventDistributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006 - Eilat, Israel
Duration: Jan 21 2007Jan 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4360 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherDistributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006
CountryIsrael
CityEilat
Period1/21/071/21/07

Keywords

  • HPC
  • MPI
  • Metagenomics
  • Self organising map

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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