Metascape statistics

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Citations per year

Number of citations per year for the bioinformatics software tool Metascape
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Tool usage distribution map

This map represents all the scientific publications referring to Metascape per scientific context
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Associated diseases

This word cloud represents Metascape usage per disease context
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Popular tool citations

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Protocols

Metascape specifications

Information


Unique identifier OMICS_13553
Name Metascape
Interface Web user interface
Restrictions to use None
Input data A multiple gene list.
Input format TXT, XLS, CSV
Computer skills Basic
Stability Stable
Maintained Yes

Taxon


  • Invertebrates
    • Caenorhabditis elegans
    • Drosophila melanogaster
    • Plasmodium falciparum
  • Plants and Fungi
    • Arabidopsis thaliana
    • Saccharomyces cerevisiae
  • Primates
    • Homo sapiens
  • Rodents
    • Mus musculus
    • Rattus norvegicus
  • Vertebrates
    • Danio rerio

Documentation


Publication for Metascape

Metascape citations

 (34)
library_books

Alternative polyadenylation drives genome to phenome information detours in the AMPKα1 and AMPKα2 knockout mice

2018
Sci Rep
PMCID: 5915415
PMID: 29691479
DOI: 10.1038/s41598-018-24683-7

[…] rons, 3′UTR, 5′UTR, TSS (transcription start site, by default defined from −1kb to +100 bp) and intergenic regions. The protein coding genes associated with the DE-APSs were then used as input in the Metascape program and pathways were enriched using “GO Biological Processes”. Protein-protein interaction networks were identified using the STRING database (https://string-db.org/) and visualized usi […]

library_books

Enhancer variants reveal a conserved transcription factor network governed by PU.1 during osteoclast differentiation

2018
PMCID: 5874256
PMID: 29619268
DOI: 10.1038/s41413-018-0011-1

[…] Non-RUNX2-Enh-BMD-SNPs; Fig. ) from these six groups. Annotation of the genes associated with these loci was done by nearest neighbor analysis and subsequent functional enrichment was performed using Metascape. Functional enrichment analysis of the RUNX2-Enh-BMD-SNP genes revealed OB differentiation and negative regulation of cell migration as the top ranking biological processes as well as I-SMAD […]

library_books

Single cell RNA seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis

2018
Genome Biol
PMCID: 5853091
PMID: 29540203
DOI: 10.1186/s13059-018-1416-2

[…] old change ≥ 2 or ≤ 0.5 and a power ≥ 0.4 were selected. The pheatmap package in R was used to plot heatmaps. Violin plots were generated using Seurat. Network enrichment analysis was performed using Metascape [] (http://metascape.org/). The identified TFs and DEGs are listed in Additional file .For Fig. , we selected all the TFs that regulated at least one of Epcam, Vim, Cdh1, Cdh2, and Fn1, as i […]

library_books

Analysis of cardiomyocyte clonal expansion during mouse heart development and injury

2018
Nat Commun
PMCID: 5821855
PMID: 29467410
DOI: 10.1038/s41467-018-02891-z

[…] sfying an abs(log(average expression difference)) >0.5 and p < 0.01 were considered statistically significant. Gene Ontology enrichments among cluster enriched, differential genes were computed using Metascape (http://www.metascape.org) (Fig. ). RStudio (https://www.rstudio.com/) was used to run custom R scripts to perform the analyses described above. Generally, ggplot2 and pheatmap packages were […]

library_books

Combined cistrome and transcriptome analysis of SKI in AML cells identifies SKI as a co repressor for RUNX1

2018
Nucleic Acids Res
PMCID: 5909421
PMID: 29471413
DOI: 10.1093/nar/gky119

[…] tially regulated genes were calculated using DESeq2 (). For differentially regulated genes a threshold (FC ≥ 2 or FC ≤ –2, P-val < 0.05) was followed. To analyze the biological significance of genes, Metascape (http://metascape.org) () was used for gene ontology analysis. In expression based gene binning (Figure ), first we removed all the genes having no expression in HL60 CTR-1 and KO-3 cells in […]

library_books

Single cell full length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs

2018
Nat Commun
PMCID: 5809388
PMID: 29434199
DOI: 10.1038/s41467-018-02866-0

[…] g “aheatmap” function in the “NMF” R package (version 0.20.6). For visualization, the raw values were smoothed by fitting the GAM. Functional enrichment analyses were performed for each cluster using Metascape (http://metascape.org). Heat map representations of the genomic coverage of the RamDA-seq data were generated using Millefy (https://github.com/yuifu/millefy). […]


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Metascape institution(s)
Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute of Medical Virology, University of Zurich, Zurich, Switzerland; Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA; Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA; Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA; Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA; Department of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA; University of Texas Southwestern Medical Center, Dallas, TX, USA; Columbia University, Department of Systems Biology and Department of Microbiology and Immunology, New York, NY, USA; Massachusetts General Hospital, Charlestown, MA, USA; Max Planck Institute for Infection Biology, Berlin, Germany; University of California, San Francisco, San Francisco, CA, USA; Host-Pathogen Interactions, Paul-Ehrlich-Institut, Germany; German Center for Infection Research, Langen, Germany; Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Metascape funding source(s)
Supported by NIAID research grant U19 AI106754 and supported by a grant from the Swiss National Science Foundation (31003A_135278), a doctoral grant from the AXA Research Fund and the NIH P50 GM085764 and by a grant (1R01AI091786) from the National Institute of Allergy and Infectious Diseases of the NIH, the Burroughs Wellcome Fund, and the Bill and Melinda Gates Foundation.

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