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EnsembleGly specifications


Unique identifier OMICS_19838
Name EnsembleGly
Interface Web user interface
Restrictions to use None
Input data A single or multiple sequences.
Input format FASTA
Computer skills Basic
Stability No
Maintained No


No version available


This tool is not available anymore.

Publication for EnsembleGly

EnsembleGly citations


Differences in Cell Morphometry, Cell Wall Topography and Gp70 Expression Correlate with the Virulence of Sporothrix brasiliensis Clinical Isolates

PLoS One
PMCID: 3792129
PMID: 24116065
DOI: 10.1371/journal.pone.0075656

[…] ungal predictor indicates the lack of a GPI anchor (data not shown). Furthermore, gp70 was investigated for the presence in its sequence of potential glycosylation and phosphorylation sites using the EnsembleGly and NetPhos software, respectively. Interesting differences were observed, as the gp70 of S. brasiliensis shows three putative O-glycosylation sites while only one was predicted for the S. […]


Structure based Comparative Analysis and Prediction of N linked Glycosylation Sites in Evolutionarily Distant Eukaryotes

PMCID: 3914773
PMID: 23459159
DOI: 10.1016/j.gpb.2012.11.003

[…] round 40% are predicted to be glycosylated.There are several other NGS prediction tools currently available. However, none of them use a rule-based method that is dependent on structural information. EnsembleGly , a sequence-based method using ensembles of support vector machine classifiers, has 94% accuracy; NetNGlyc (http://www.cbs.dtu.dk/services/NetNGlyc/) uses artificial neural networks that […]


Bioinformatics and molecular modeling in glycobiology

PMCID: 2912727
PMID: 20364395
DOI: 10.1007/s00018-010-0352-4

[…] nOYang [] all use neural networks for the prediction of glycosylation sites; big-Pi [] employs scoring functions based on amino acid properties; GPI-SOM [] uses a Kohonen map; CKSAAP_OGlySite [], and EnsembleGly [] use a Support Vector Machine based approach; and GPP [], the currently best performing predication tool, uses a hybrid combinatorial and statistical learning approach based on random fo […]


Identification of two GH18 chitinase family genes and their use as targets for detection of the crayfish plague oomycete Aphanomyces astaci

BMC Microbiol
PMCID: 2751781
PMID: 19719847
DOI: 10.1186/1471-2180-9-184

[…] N-myristoylation and cell attachment were identified by a protein pattern search against the Prosite database http://www.expasy.org/prosite/; []). O-, N-, and C-glycosylated sites were predicted with EnsembleGly - a web server for prediction of O-, N-, and C-linked glycosylation sites with ensemble learning []. […]


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EnsembleGly institution(s)
Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA, USA; Center for Computational Intelligence, Learning, and Discovery, Iowa State University, Ames, IA, USA; Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA; Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA
EnsembleGly funding source(s)
Supported in part by a grant from the National Institutes of Health (GM066387).

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