Glimmer-MG statistics

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Citations per year

Number of citations per year for the bioinformatics software tool Glimmer-MG
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Tool usage distribution map

This map represents all the scientific publications referring to Glimmer-MG per scientific context
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Protocols

Glimmer-MG specifications

Information


Unique identifier OMICS_01487
Name Glimmer-MG
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 0.3.2
Stability Stable
Maintained Yes

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Versioning


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Documentation


Maintainers


  • person_outline David Kelley
  • person_outline David Kelley

Additional information


Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland, College Park, MD, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Broad Institute, Cambridge, MA, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine Baltimore, MD, USA

Publication for Glimmer-MG

Glimmer-MG citations

 (16)
library_books

Human Microbiome Acquisition and Bioinformatic Challenges in Metagenomic Studies

2018
Int J Mol Sci
PMCID: 5855605
PMID: 29382070
DOI: 10.3390/ijms19020383

[…] embled reads for functional prediction. Briefly, a functional analysis is based on genes prediction to infer their probable functions. Different methods, such as FragGeneScan [], MetaGeneMark [], and Glimmer-MG [], have been developed and optimized for this aim. Once the genes have been identified, specific databases can be used for functional predictions, such as IMG database [], MetaRef [], dbCA […]

library_books

Gene Prediction in Metagenomic Fragments with Deep Learning

2017
Biomed Res Int
PMCID: 5698827
PMID: 29250541
DOI: 10.1155/2017/4740354

[…] d to find the genes with previously known homologous proteins and cannot predict novel genes. The model-based methods, such as MetaGeneAnnotator [], MetaGene [], MetaGeneMark [], FragGeneScan [], and Glimmer-MG [], used either the higher-order Markov chain models or the hidden Markov chain models to identify genes in metagenomics. However, the main limitation of such Markov chain models is that th […]

library_books

Draft Genome Sequence of Klebsiella pneumoniae OK8, a Multidrug Resistant Mouse and Human Pathogen

2017
Genome Announc
PMCID: 5597776
PMID: 28912335
DOI: 10.1128/genomeA.01018-17

[…] ipeline (), Edena (), MaSuRCA (), and SPAdes (). We found 5 contigs for the bacterial sample, and all contigs were merged using CISA (). Sequence annotation was carried out with the contigs using the Glimmer-MG program (). The predicted open reading frames (ORFs) were annotated using our in-house pipeline, CANoPI (Contig Annotator Pipeline). The genome size was found to be 5,768,520 bp, with 57.28 […]

call_split

Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils

2017
PLoS One
PMCID: 5540551
PMID: 28767679
DOI: 10.1371/journal.pone.0181826
call_split See protocol

[…] based on length and quality. De novo assembly of the data was accomplished using the CLC Genomics Workbench version 4.0 [] using the default parameters.Contigs were analyzed using the gene predictor Glimmer MG (Gene Locator and Interpolated Markov Modeler—Metagenomics) [], a platform for predicting genes from environmental DNA sequences. This algorithm selects the best of all possible combination […]

library_books

Genomic Evidence that Methanotrophic Endosymbionts Likely Provide Deep Sea Bathymodiolus Mussels with a Sterol Intermediate in Cholesterol Biosynthesis

2017
Genome Biol Evol
PMCID: 5421315
PMID: 28453654
DOI: 10.1093/gbe/evx082

[…] ompleteness and 1.02% contamination. Thus, the composite genome was almost complete. Coding regions in the composite genome of endosymbionts were identified using a combination of MetaGeneMark () and Glimmer-MG (). The deduced amino acid sequences were subjected to a BLASTP search against an NCBI nonredundant (nr) protein database. Functional assignments were manually conducted by homology searche […]

library_books

A Review of Bioinformatics Tools for Bio Prospecting from Metagenomic Sequence Data

2017
Front Genet
PMCID: 5337752
PMID: 28321234
DOI: 10.3389/fgene.2017.00023

[…] rediction compared to MetaGeneAnnotator and MetaGene (, a precursor to MetaGeneAnnotator) on simulated data.Glimmer-MG () is an extension of the popular bacterial gene-prediction software Glimmer (). Glimmer-MG starts by clustering data which likely belong to the same organism, using Phymm (); uncategorized data are then clustered using Scimm (). Gene models, based on HMMs, are trained within each […]


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Glimmer-MG institution(s)
Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland, College Park, MD, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Broad Institute, Cambridge, MA, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine Baltimore, MD, USA
Glimmer-MG funding source(s)
Supported by National Institute of Health grants (R01-LM083873 and R01-HG006677).

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