Glimmer-MG protocols

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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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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 in pipeline

2017
PMCID: 5421315
PMID: 28453654
DOI: 10.1093/gbe/evx082

[…] 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 […]


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Glimmer-MG in publications

 (13)
PMCID: 5855605
PMID: 29382070
DOI: 10.3390/ijms19020383

[…] 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 [], […]

PMCID: 5698827
PMID: 29250541
DOI: 10.1155/2017/4740354

[…] 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 […]

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

[…] (), 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, […]

PMCID: 5540551
PMID: 28767679
DOI: 10.1371/journal.pone.0181826

[…] 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 […]

PMCID: 5337752
PMID: 28321234
DOI: 10.3389/fgene.2017.00023

[…] orphelia was shown to demonstrate higher specificity but lower sensitivity in gene prediction 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 (); […]


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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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