GeneRanker: Characterization of Gene Sets

The gene upload option allows keywords from various namespaces. Supported are

• Entrez Gene IDs (e.g. 30818)
• gene symbols/names (e.g. KCNIP3)
• transcript accession numbers (e.g. NM_001034914, ENST00000360990 or AK315437)
• Affymetrix probe set IDs (e.g. 231774_at)

//label1  label2        label3          label4
90634   -0.13666667     -0.25666666     -0.280000001
5371    1.04384613      1.229230762     0.777692258
23657   0.059999999     0.039999999     0.159999996
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"Inflammation in H.sapiens"

The example data set is from a microarray analysis of Systemic Inflammation in Humans (Calvano et al (2005) Nature 437,1032-7; PMID: 16136080).

Gene expression changes relative to t=0 are displayed at 5 timepoints (2,4,6,9 and 24 hours) after inoculation with bacterial endotoxin.

Here you can select which annotation data sets shall be used for the analysis. The following annotation types are available:

• Signal Transduction Pathways (canonical):
  Gene associations with over 400 canonical signal transduction pathways collected from the following sources:
  
  • NCI-nature PathwayInteractionDatabase including the BioCarta-donated subset
    Carl F. Schaefer, Kira Anthony, Shiva Krupa, Jeffrey Buchoff, Matthew Day, Timo Hannay & Kenneth H. Buetow. PID: The Pathway Interaction Database. Nucleic Acids Res. 37, D674-9 (2009)
    
  • The Cancer Cell Map
    from the Memorial Sloan-Kettering Cancer Center, available under the Creative Commons License.
  All canonical pathways are derived from Homo sapiens. Therefore "Signal Transduction Pathways (canonical)" can only be selected if "Homo sapiens" has been chosen as organism or the mapping from the input genes on the orthologous human genes has been activated.
• Signal Transduction Pathways (Genomatix Literature Mining):
  Signal Transduction Pathway Associations are obtained by Genomatix with a proprietary literature data mining algorithm based on all available PubMed abstracts. Individual gene to pathway associations found on sentence level in the scientific literature were filtered for significance to avoid random matches. The significant associations were used for pathway annotations within large gene sets. For more background on our literature data mining see LitInspector.
• Molecular Functions (GO):
  The ontology 'molecular function' from the Gene Ontology Consortium
• Cellular Components (GO):
  The ontology 'cellular component' from the Gene Ontology Consortium
• Biological Processes (GO):
  The ontology 'biological process' from the Gene Ontology Consortium
• Diseases (Genomatix Literature Mining):
  Genomatix has assigned genes to diseases with the help of a proprietary literature data mining algorithm based on all available PubMed abstracts. Individual gene to disease associations found on sentence level in the scientific literature were filtered for significance to avoid random matches. The significant associations were used for disease annotations within large gene sets. For more background on our literature data mining see LitInspector. Disease names and synonyms are based on MeSH terms and the NCI thesaurus.
• Diseases (MeSH):
  Genomatix has assigned genes to diseases with the help of a proprietary literature data mining algorithm based on all available PubMed abstracts and their corresponding MeSH (Medical Subject Headings). For more background on our literature data mining see LitInspector.
• Tissues (Genomatix Literature Mining):
  Genomatix has assigned genes to tissues with the help of a proprietary literature data mining algorithm based on all available PubMed abstracts. Individual gene to tissue associations found on sentence level in the scientific literature were filtered for significance to avoid random matches. The significant associations were used for tissue annotations within large gene sets. For more background on our literature data mining see LitInspector. Tissue names and synonyms are based on MeSH terms and the NCI thesaurus.
• Tissues (UniGene):
  Genomatix has assigned UniGene tissue names to a hierarchical tissue ontology. Thus the GeneRanker concept can be applied to Unigene expression data, and groups of genes with significant coexpression profiles can be identified.
• Co-cited genes (Genomatix Literature Mining):
  Genomatix identified gene to gene associations with the help of a proprietary literature data mining algorithm based on all available PubMed abstracts. Individual gene to gene associations found on sentence level in the scientific literature were filtered for significance to avoid random matches. The significant associations were used for the identification of possible key genes within large gene sets. New genes which were not contained within the input list of genes are marked with an asterisk "*". For more background on our literature data mining see LitInspector.
• Co-cited Transcription Factors (TFs) (Genomatix Literature Mining):
  Genomatix identified gene to transcription factor associations with the help of a proprietary literature data mining algorithm based on all available PubMed abstracts. Individual gene to TF associations found on sentence level in the scientific literature were filtered for significance to avoid random matches. The significant associations were used for the identification of possible key TFs within large gene sets. New transcription factor genes which were not contained within the input list of genes are marked with an asterisk "*". For more background on our literature data mining see LitInspector.

From this drop down box you can select a threshold for the p-value.

Here is a short description of the p-value concept: Let q be the number of genes in the input set; Let m be the number of genes from the input set having annotation A assigned; Then the p-value is the probability (using Fisher's Exact Test) of finding at least m genes in a input list of length q having annotation A (under the assumption that belonging to the input list is independent of having this annotation).

These parameters are hidden by default. You can use the next to the section header to reveal them!

From this drop down box you can select the threshold for the adjusted p-value.

GeneRanker estimates an adjusted p-value from the results of 1,000 simulated null hypothesis queries. From these simulations we directly estimate the probability of obtaining at least one false positive for any desired threshold in the hypothesis-wise p-value. However, the computation of the adjusted p-value may take some time, depending on how large your input gene list is and how many annotation terms the selected annotation type contains. Therefore the computation of the adjusted p-value is deactivated per default. If you need an adjusted p-value for your analysis then just tick the check box on the left side of this parameter.

For a detailed description of the adjusted p-value please refer to the paper mentioned in the introduction.

These parameters are hidden by default. You can use the next to the section header to reveal them!

• Show result directly in browser window
  In this option the URL of the result is directly shown in your browser window.

  Warning: Please use this option only for analyses which can be performed in a short time.
  If the analysis takes longer than the timeout of the webserver, the connection will be terminated and you will receive an error message (e.g. "The document contained no data."). In this case, the results will not be available, please restart the analysis using the option below "Send the URL of the result to".

• Send the URL of the result via email
  In this option an email with the URL of the results will be sent to the user provided email address, when the analysis is finished.

The results will be available for a limited time on our server. For details of how long your results will be kept please see the result-email. After that period they will be deleted unless protected in the project management!