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MetaRanker Server ver. 2.0

Prioritize the entire protein-coding part of the human genome based on heterogeneous user-specified data sets

MetaRanker options

A name that will help you identify this query

Genome-wide association
Choose File (optional)
Candidate gene interaction

Chromosomal regions


Gene expression


  • Layers may be used in any combination.
  • After having submited your analysis, you will be redirect to the results page, which you can bookmark and revisit later.
  • The running time of the GWAS layer, and text-mining layer is up to 1 hour.
  • Only text files may be uploaded (except the genome-wide association study (GWAS) layer, which also accepts compressed files). Excel, and Word and other non-text file formats are not supported.
  • You must choose the gene ID nomenclature used in all input files uploaded to MetaRanker; Ensembl gene IDs, Hugo gene symbols and Entrez gene IDs are supported.

Genome-wide association data layer

The input file should consist of at least two columns:
  • Single nucleotide polymorphisms (SNP) rsIDs.
  • Association p-values from the GWAS summary statistics.
Please specifiy the columns containing the SNP rsIDs, and association p-values. Also, please specify whether the file contains any header, which delimiters it uses, and whether it is a compresed file (speeds up upload to MetaRanker). Please note that SNPs with non-valid p-values will be discarded, and that 'NA' should be used for missing data in the p-value column.

  rs23694	0.1239
  rs83232	1.9e-7
  rs22331	0.9989

Upload of a SNP to gene mapping file is optional. The file should consist of exactly two columns and no header. The first column should specify the rsID, and the second column should specify the associated gene.

Example (shown for Ensembl gene ID nomenclature)

  rs23694	ENSG00000232602
  rs23694	ENSG00000199602
  rs22331	ENSG00000198421
Example data set
Summary statistics from the GIANT consortium's height GWAS (Lango-Allen et al.) can be downloaded at the BROAD Institute's website.

Candidate gene interaction layer

Please specify a file containing phenotype-specific susceptiblity genes (one per line), or paste the genes into the formular field (one per line). Use the gene nomenclature that matches your previous selection.

Example (shown for Hugo Gene Symbol gene nomenclature)
Example data set
OMIM height genes from Lango-Allen et al. can be downloaded in Ensembl gene format or in gene symbol format.

Chromosomal regions layer

This file should contain regions that are associated with the trait under investigation. Please specify which human genome build that the chromsomal region(s) are reported in. Commas and 'chr' prefixes are optional.

Example 1

Example 2

Phenotype-similarity layer

Please choose the phenotype keywords that, most closely, are resembling your phenotype of interest:
  • They are co-mentioned with one of the keywords. In this case please use the 'Logical OR relationship' option.
  • They are co-mentioned with all the keywords. In this case please use the 'Logical AND relationship' option.
Body height
Growth disorders

Gene expression layer

This file should contain at least two columns:
  • Gene IDs in the nomenclature previously selected.
  • Values for analysis. E.g. differential expression p-values, expression values, or fold-change values. In case of differential expression p-values, ascending order sorting should be enabled, while descending order should be used for expression values, or other derived expression sets in which larger values indicate stronger expression.
When uploading the data, please specify the column numbers of the above two columns, whether the file contains any header, and which delimiters it uses.

Differential expression example (shown for Ensembl gene ID nomenclature)
  ENSG00000422813	1.6e-5
  ENSG00000122601	0.0639
  ENSG00000522999	0.8933
Fold change example (shown for gene symbol nomenclature)
  MC4R	2.8
  BDNF	1.1
  POMC	0.8933
Example datasets
  • Simulated gene expression data with a differential p-value and fold change column can be downloaded in Ensembl gene format and gene symbol format.
  • Please note that the file is comma-separated, and contains a header,
  • Please specify the gene nomenclature column that matches your general MetaRanker nomenclature setting.
  • Ascending sorting should be used in case the differential expression p-value column is subjected to the analysis, and descending sorting should be used if the fold change column is used instead.

Custom layer

This file should contain at least two columns:
  • Gene IDs in the nomenclature previously selected.
  • Gene-based scores, for instance p-values from sequence kernel association tests.
In case lower scores denotes higher likelihood of association ascending sorting should be used. In the other case, in which higher scores denotes higher likelihood of association, descending sorting should be used. Please specify whether genes that are absent in the list of uploaded genes, should be:
  • Scored worse score than lowest scored input genes. This option should be used in case all genes in the human genome have been tested and missingness means no association.
  • Should obtain the median score of the input genes. This option should be used in case missingness does not imply that the given gene is not associated with the trait.
Please specify the columns numbers the above two columns, whether the file contains any header, and which delimiters it uses.

Example datasets
Skeletal growth genes from Mouse Genome Database (tab-delimited to fit custom layer): Differentially expression growth plate genes from Lui et al. (tab-delimited to fit custom layer):

Rurnning times

Genome-wide association data layer Candidate gene interaction layer Chromosomal regions layer Phenotype-similarity layer Gene expression layer Custom layer Running time
+ - - - - - 30 minutes
- + - - - - 1-5 minutes
- - + - - - 1-5 minutes
- - - + - - 30 minutes
- - - - + - 1-5 minutes
- - - - - + 1-5 minutes

The running time of analyses based on various layers corresponds to the running time of the slowest layer.

Submit the analysis

Click on the "Submit" button. Please be patient as it may take some time to upload all datasets. You will automatically be redirected to the results page.

Progress report

Visual illustration of the progression of the anaysis. In the following example, two layers were included in the analysis, and have finished computing.

Results table

A table showing the information contributing to the ranking of all genes subjected to the analysis. The table will hold information on the rank of each gene, its score, its permutation-based p-value, and several layer-specific columns. The table can be downloaded as tab-delimited-, or csv-delimited format. Please note that the detail of the table automatically is adjusted based on the view-port (size) of the browser (All columns will always be available in the download files).
The rank of the gene.
Gene ID
The Hugo Gene Nomenclature gene symbol.
Ensembl Gene ID
The Ensembl gene ID.
MetaRanker Score
The final score of the gene. The smaller the better.
Permutation P-value
The empirical p-value of the gene.
Layer-specific scores
The score of the gene within the given layer. The smaller the better.
GWAS layer-specific output
Output from the GWAS layer (See below)
The GWAS layer results in the following output columns:
Minimum SNP ID
The rsID of the SNP with the lowest association p-value mapped to the given gene.
Minimum SNP P-value
The association p-value of the best-associated SNP for the given gene.
Effective tests
The number of independent SNPs for the given gene (calculated based on linkage disequilibrium information).
No. of mapped SNPs
The number of SNPs mapped to the given gene.
In the below example only the chromosomal interval layer and gene expression layer were used in the analysis.

Results network diagram

A network diagram illustrating how the top 20 prioritized gene products interact with each other and other direct protein-protein interaction partners. High-confidence protein-protein interactions from the InWeb data base (Lage et al. Nature Biotechnology 2007) are used to connect gene products. Gene products are color-coded and shaped based on whether they are top 20 prioritized genes (blue circles) or direct interaction partners (green squares).

Navigating the diagram:
  • Use the scroll button on the mouse, or track pad, to zoom in and out of the diagram.
  • Re-arrange nodes by clicking on the given node and moving it to its new location while keeping the mouse button pressed.
  • Move the entire network by clicking on the white canvas and moving the mouse pointer while keeping the button pressed.

Example network diagram

MetaRanker 2.0: a web server for prioritization of genetic variation data

Tune H Pers, Piotr Dworzynski, Cecilia Engel Thomas, Kasper Lage, Søren Brunak
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein-protein interactions, large-scale text-mining data, copy number variation data, and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at

Custom Layer

Total Progress: Waiting

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