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Nucleic Acids Res. 2012 Jan;40(1):e9. doi: 10.1093/nar/gkr1067. Epub 2011 Nov 18.

Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering.

Author information

  • 1Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, 3115 Biomolecular Sciences Building 296, University of Maryland, College Park, MD 20742, USA. dakelley@umiacs.umd.edu

Abstract

Environmental shotgun sequencing (or metagenomics) is widely used to survey the communities of microbial organisms that live in many diverse ecosystems, such as the human body. Finding the protein-coding genes within the sequences is an important step for assessing the functional capacity of a metagenome. In this work, we developed a metagenomics gene prediction system Glimmer-MG that achieves significantly greater accuracy than previous systems via novel approaches to a number of important prediction subtasks. First, we introduce the use of phylogenetic classifications of the sequences to model parameterization. We also cluster the sequences, grouping together those that likely originated from the same organism. Analogous to iterative schemes that are useful for whole genomes, we retrain our models within each cluster on the initial gene predictions before making final predictions. Finally, we model both insertion/deletion and substitution sequencing errors using a different approach than previous software, allowing Glimmer-MG to change coding frame or pass through stop codons by predicting an error. In a comparison among multiple gene finding methods, Glimmer-MG makes the most sensitive and precise predictions on simulated and real metagenomes for all read lengths and error rates tested.

PMID:
22102569
[PubMed - indexed for MEDLINE]
PMCID:
PMC3245904
Free PMC Article

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