Gene prediction in metagenomic sequences using Glimmer augmented by phylogenetic classification and clustering
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docs BioPython manual add May 10, 2012
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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

See manual.pdf for instructions on installation and running Glimmer-MG.

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).