Gene Prediction with Glimmer for Metagenomic Sequences Augmented by Classification and Clustering

TitleGene Prediction with Glimmer for Metagenomic Sequences Augmented by Classification and Clustering
Publication TypeJournal Articles
Year of Publication2012
AuthorsKelley DR, Liu B, Delcher AL, Pop M., Salzberg SL
JournalNucleic Acids ResearchNucl. Acids Res.Nucleic Acids ResearchNucl. Acids Res.
Volume40
Type of Article10.1093/nar/gkr1067
ISBN Number0305-1048, 1362-4962
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.