GlimmerHMM protocols

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GlimmerHMM specifications

Information


Unique identifier OMICS_07773
Name GlimmerHMM
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline M. Pertea <>

Publications for GlimmerHMM

GlimmerHMM in pipelines

 (3)
2015
PMCID: 4728381
PMID: 26729172
DOI: 10.3390/genes7010001

[…] having a density of peaks across gene body equal or higher than 10 ppkm (peaks per kilobase of transcript per million of peaks). coordinates of protein coding exons for each gene were predicted with glimmerhmm-3.0.4 []. ppkm is analogous to rpkm (reads per kilobase per million) measure that is used in rna sequencing. ppkm measure normalizes counts of peaks to the length of protein-coding dna […]

2014
PMCID: 4159001
PMID: 25062916
DOI: 10.1093/gbe/evu148

[…] genome. augustus () was run by using the generated hint file and input parameters: –strand=both; –genemodel=partial; –extrinsiccfgfile=extrinsic.e.xnt.cfg. another set of genes was generated using glimmerhmm () according to the manual. the trainglimmerhmm module was used to train the glimmerhmm with the u. maydis gene set. in the final step of glimmerhmm annotations, glimmerhmm was run […]

2014
PMCID: 4202335
PMID: 25364804
DOI: 10.1093/gbe/evu199

[…] []; supplementary table s2, supplementary material online). unique sequence not present in s288c was identified using custom perl scripts. gene prediction on non-s288c sequence was performed using glimmerhmm (v3.0.2; []) trained on s. cerevisiae transcripts. additional details are available in supplementary methods, supplementary material online. genome sequencing data for each strain […]


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GlimmerHMM in publications

 (111)
PMCID: 5915607
PMID: 29691383
DOI: 10.1038/s41467-018-03423-5

[…] and splice junctions; cufflinks (version 2.1.1) was then used to assemble the mapped reads into gene models (cufflinks-set). augustus (version 2.5.5), geneid (version), genescan (version 1.0), glimmerhmm (version 3.0.1), and snap (version) were also used to predict coding regions in the repeat-masked genome. of these, augustus, snap and glimmerhmm were trained by pasa-h-set gene models. […]

PMCID: 5924555
PMID: 29659530
DOI: 10.3390/genes9040213

[…] exonerate v2.2 [] to filter the genome sequences and the corresponding query proteins and search for accurately spliced alignments. for de novo annotation, augustus v3.2.1 [], geneid v1.4.4 [], and glimmerhmm v3.0.3 [] were used to predict genes within the genome on the basis of a human training set. next, we used evidencemodeler v1.1.1 [] to integrate homologs and de novo predicted genes […]

PMCID: 5905365
PMID: 29617765
DOI: 10.1093/gigascience/giy031

[…] rrid:scr_008417), the parameter set as “-uniquegeneid true –noinframestop = true –gff3 on –genemodel complete –strand both” []; genscan (genscan, rrid:scr_012902), with default parameter []; glimmerhmm_3.0.2 (glimmerhmm, rrid:scr_002654), the parameter set as " -f -g" []; and snap (the default parameter) []. all evidences of the gene model were integrated using […]

PMCID: 5852033
PMID: 29540755
DOI: 10.1038/s41598-018-22816-6

[…] from the nuclear assembly., genes were predicted and annotated by combining calls from multiple methods to obtain the best consensus model for a given locus. these included ab initio predictions (glimmerhmm, augustus, snap, genemark-es), homologous inference (genewise, tblastn), and gene model consolidation programs (evidencemodeler),. for the protein coding-gene name assignment we combined […]

PMCID: 5791967
PMID: 29385175
DOI: 10.1371/journal.pone.0189947

[…] performed by using the newbler assembler software version 1.1.02.15., the genome assembly was annotated using a combined approach. gene prediction was carried out using genemark-es, augustus, and glimmerhmm. genemark-es predictions were trained using est validated open reading frame (orf) predictions (see below) and ab initio runs. augustus and glimmer predictions were trained using datasets […]


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GlimmerHMM institution(s)
Bioinformatics Department, The Institute for Genomic Research, Chinakville, MD, USA

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