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BMCBI
2008

Gene prediction in metagenomic fragments: A large scale machine learning approach

8 years 4 months ago
Gene prediction in metagenomic fragments: A large scale machine learning approach
Background: Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positiv...
Katharina J. Hoff, Maike Tech, Thomas Lingner, Rol
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors Katharina J. Hoff, Maike Tech, Thomas Lingner, Rolf Daniel, Burkhard Morgenstern, Peter Meinicke
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