Gene prediction in metagenomic fragments: a large scale machine learning approach.

ABSTRACT:

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

SUBMITTER: Hoff KJ 

PROVIDER: S-EPMC2409338 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature