Glimmer-MG is a system for finding genes in environmental shotgun DNA sequences. Glimmer-MG (Gene Locator and Interpolated Markov ModelER - MetaGenomics) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA. The IMM approach, described in our Nucleic Acids Research paper on Glimmer 1.0 and in our subsequent paper on Glimmer 2.0 , uses a combination of Markov models from 1st through 8th-order, weighting each model according to its predictive power. Glimmer uses 3-periodic nonhomogenous Markov models in its IMMs. Glimmer-MG addresses the challenges of metagenomics gene prediction. Prediction model training is the main reason Glimmer3 cannot be applied to metagenomics sequences. Rather than rely on GC% to find evolutionary relative genomes for training, Glimmer-MG instead finds phylogenetic classifications using Phymm and parameterizes gene prediction models using those classifications. Glimmer-MG also clusters the sequences using Scimm, which groups together sequences that are likely from the same organism. Analogous to iterative schemes that are useful for whole genomes, Glimmer-MG retrains prediction models within each cluster on the initial gene predictions before making a final set of predictions. To account for fragmented genes, Glimmer-MG incorporates a model for gene length, in which partial genes are carefully handled. Finally, Glimmer-MG can predict insertions and deletions in the sequence by branching into a different frame at low quality base calls such as homopolymer runs in 454 sequences. See manual.pdf for instructions on installation and running Glimmer-MG. Reference: Kelley DR, Liu B, Delcher A, Pop M, Salzberg S. Gene prediction with Glimmer on metagenomic sequences augmented by phylogenetic classification and clustering. Nucleic Acids Research, 40:1 e9 (2012). Website: http://www.cbcb.umd.edu/software/glimmer-mg
davek44/Glimmer-MG
Gene prediction in metagenomic sequences using Glimmer augmented by phylogenetic classification and clustering
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