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BayGO specifications

Information


Unique identifier OMICS_18896
Name BayGO
Interface Web user interface
Restrictions to use None
Input data A list of genes.
Programming languages Perl, R
Computer skills Basic
Stability Stable
Maintained No

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Publication for BayGO

BayGO in pipelines

 (3)
2018
PMCID: 5802734
PMID: 29410524
DOI: 10.1038/s41598-018-20738-x

[…] functionally categorized using gene ontology (go) terms assigned using the blast2go algorithm,. highly represented categories under each condition were determined by enrichment analysis using the baygo algorithm., a transcriptome-wide survey of as in differentially expressed genes after exposure of t. rubrum to uda was conducted using analysis of exon skipping and intron retention. […]

2016
PMCID: 4754417
PMID: 26909077
DOI: 10.3389/fmicb.2016.00175

[…] expressed transcripts were annotated using blast2go (conesa et al., ). functional enrichment analysis of differentially expressed genes based on gene ontology (go) terms was performed using the baygo algorithm (vêncio et al., ). go terms significantly enriched, i.e., p ≤ 0.05 were further analyzed, using graphpad prism v 5.00 software., to reconstruct the regulatory network of δxyr1/qm9414 […]

2012
PMCID: 3329418
PMID: 22300537
DOI: 10.1186/1471-2105-13-23

[…] test, which need to employ some type of smoothing (e.g., laplace or add one smoothing). bayesian methods that perform enrichment analysis using a prior probability distribution include mgsa and the baygo framework []. although these bayesian methods enable the enrichment analysis of sparse data, their lack of support for inter-instance relationships, non-hierarchical class relationships […]


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BayGO in publications

 (7)
PMCID: 5802734
PMID: 29410524
DOI: 10.1038/s41598-018-20738-x

[…] our understanding of the molecular mechanisms responsive to uda. considering the physiological context, we examined the functional distributions of the modulated genes using the blast2go and baygo software programs. the categories of genes that were differentially expressed at 3 h vs. 0 h, and 12 h vs. 0 h are shown in fig. , with only the over-represented genes shown. this analysis […]

PMCID: 4974619
PMID: 27492542
DOI: 10.1038/srep31098

[…] rna-seq libraries were generated and sequenced on one lane of an illumina highseq 2500 (illumina, ca). enrichment of gene ontology terms of differentially expressed transcripts was performed with baygo using 500 simulations and a p-value cutoff of 0.05., how to cite this article: gouran, h. et al. a secreted protease prta controls cell growth, biofilm formation and pathogenicity in xylella […]

PMCID: 4864841
PMID: 27226768
DOI: 10.2174/1389202917666151116212901

[…] 0.05 were established as thresholds. samples were normalized using median log deviation implemented in the deseq package. functional categorization was performed with gene ontology (go) terms using baygo software [], adopting a p-value of ≤ 0.05 as the criterion for significantly enriched categories. raw sequence data and count data for all samples are available at gene expression omnibus (geo; […]

PMCID: 4754417
PMID: 26909077
DOI: 10.3389/fmicb.2016.00175

[…] expressed transcripts were annotated using blast2go (conesa et al., ). functional enrichment analysis of differentially expressed genes based on gene ontology (go) terms was performed using the baygo algorithm (vêncio et al., ). go terms significantly enriched, i.e., p ≤ 0.05 were further analyzed, using graphpad prism v 5.00 software., to reconstruct the regulatory network of δxyr1/qm9414 […]

PMCID: 4243288
PMID: 25573029
DOI: 10.1186/1471-2164-15-S7-S1

[…] of the molecular mechanism by which t. rubrum senses and responds to stress caused by acriflavine, we examined the functional distribution of the modulated genes using the blast2go [] and baygo [] tools. genes with 1.5 log2-fold change were analyzed and their gene ontology (go) functions are presented in figure . this analysis indicated that the genes modulated by acriflavine […]


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BayGO institution(s)
BIOINFO-USP Núcleo de Pesquisas em Bioinformática, Universidade de São Paulo, São Paulo, Brazil; Instituto Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein, São Paulo, Brazil; Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, Brazil; Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil
BayGO funding source(s)
Supported by a doctoral fellowship from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and by Conselho Nacional de Desenvolvimento Cientí- fico e Tecnológico (CNPq).

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