REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Supek, Fran; Bošnjak, Matko; Škunca, Nives; Šmuc, Tomislav

doi:10.1371/journal.pone.0021800

Cited by 5,481 publications

(5,200 citation statements)

References 25 publications

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“…We used the PANTHER overrepresentation test 78 (release 20160715) against 20,972 human genes as background to test for enrichment in GO biological processes using the GO database release 2017-02-28. Significant GO IDs (Bonferroni adjusted P < 0.05) were selected for analysis with REVIGO 79 to group similar ontological terms, which yielded 22 over-represented GO IDs. d , GO analysis of a more stringent set of protein-coding non-eGenes.…”

Section: Extended Datamentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

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Nhgri³

et al. 2017

Nature

3,571

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Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

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Section: Extended Datamentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

Fund

Nhgri³

et al. 2017

Nature

3,571

2,412

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“…Second, to identify classes of genes with a higher frequency of mutations in gaps, we looked for over-representation of the 132 genes with variants in coding gaps, while using the 2,252 gap-filled genes as background. GO terms with a p-value smaller than 0.01 were visualized using REViGO [Supek et al, 2011]. Code for the gap analysis can be acquired here https://github.com/LewisLabUCSD/assembly_gaps.…”

Section: Methodsmentioning

confidence: 99%

A reference genome of the Chinese hamster based on a hybrid assembly strategy

Rupp

MacDonald

et al. 2018

Biotech & Bioengineering

102

119

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Accurate and complete genome sequences are essential in biotechnology to facilitate genome-based cell engineering efforts. The current genome assemblies for Cricetulus griseus, the Chinese hamster, are fragmented and replete with gap sequences and misassemblies, consistent with most short-read based assemblies. Here, we completely resequenced C. griseus using Single Molecule Real Time (SMRT) sequencing and merged this with Illumina-based assemblies. This generated a more contiguous and complete genome assembly than either technology alone, reducing the number of scaffolds by >28-fold, with 90% of the sequence in the 122 longest scaffolds. Most genes are now found in single scaffolds, including up- and downstream regulatory elements, enabling improved study of noncoding regions. With >95% of the gap sequence filled, important CHO cell mutations have been detected in draft assembly gaps. This new assembly will be an invaluable resource for continued basic and pharmaceutical research.

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“…Gene Ontology enrichment analysis was performed on ranked list of proteins using log‐transformed P ‐values or specificity scores using GOrilla (Eden et al , 2009) followed by GO term redundancy reduction performed by REVIGO (Supek et al , 2011). …”

Section: Methodsmentioning

confidence: 99%

Landscape of nuclear transport receptor cargo specificity

Mackmull

Klaus

Heinze

et al. 2017

Molecular Systems Biology

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Nuclear transport receptors (NTRs) recognize localization signals of cargos to facilitate their passage across the central channel of nuclear pore complexes (NPCs). About 30 different NTRs constitute different transport pathways in humans and bind to a multitude of different cargos. The exact cargo spectrum of the majority of NTRs, their specificity and even the extent to which active nucleocytoplasmic transport contributes to protein localization remains understudied because of the transient nature of these interactions and the wide dynamic range of cargo concentrations. To systematically map cargo–NTR relationships in situ, we used proximity ligation coupled to mass spectrometry (BioID). We systematically fused the engineered biotin ligase BirA* to 16 NTRs. We estimate that a considerable fraction of the human proteome is subject to active nuclear transport. We quantified the specificity and redundancy in NTR interactions and identified transport pathways for cargos. We extended the BioID method by the direct identification of biotinylation sites. This approach enabled us to identify interaction interfaces and to discriminate direct versus piggyback transport mechanisms. Data are available via ProteomeXchange with identifier PXD007976.

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REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Cited by 5,481 publications

References 25 publications

Genetic effects on gene expression across human tissues

Genetic effects on gene expression across human tissues

A reference genome of the Chinese hamster based on a hybrid assembly strategy

Landscape of nuclear transport receptor cargo specificity

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