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Latest Publication: Abstract | Article


GDA data from DisGeNET v5.0

Disease Browser

Examples: Burkitt Lymphoma, Leopard's Syndrome,
Basal Cell Carcinoma Adenocarcinoma Of Esophagus

How To
  • Filter the disease list by selecting one or more categories that apply to the disease/es you are looking for
  • Start typing the name of the disease you are looking for or scroll down the list until you find it

Categories

Disease categories are defined as in Disease Ontology.
Hover on the name of a category to read its brief description.

Gathering SIGNOR information...

Search by SNP or Gene ID

Examples: RAF1, rs881375
How To
It is possible to perform a search by gene or SNP, in order to reveal their association with diseases. Type in the name of the gene, a UniprotKB ID or a SNP ID you want to search for and click on "Submit".

Multi-Protein Search

Example: P29317, Q06124, P04049, P15056
How To
Enter a user defined list of proteins (UniprotKB ID or Gene ID) to obtain a graph linking the query proteins via causal interactions. Gene set enrichment analysis is also performed to identify enriched disease and pathway annotations.

Accepted delimiters: space, semicolon, comma, tab.

About

DISNOR aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage.

In DISNOR users can explore more than 3,700 disease-pathways, linking approximately 2,600 disease genes. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred “patho-pathways” at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes – either annotated in DisGeNET or user-defined – DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function provides additional information on disease-associated cellular pathways and disease similarity.

Disnor brings the curation quality of Signor into a whole new tool based on protein-disease association data, with the goal of creating physiologically meaningful connections between signaling interaction data and disease information.

Contact Us

For questions, support, bug reports, proposals or any other communication, feel free to contact:

Development

Prisca Lo Surdo
Alberto Calderone

Data curation

Livia Perfetto
Marta Iannuccelli

Coordinator

Prof. Gianni Cesareni

Download Data


Gene-Disease association data

Connect First Neighbors All

Tutorial

Table of Contents

Browse by Disease


Searching by disease

  • This functionality allows the visualization of a Disease network and the identification of related DisGeNET diseases and KEGG pathways whose proteins are enriched in the list of disease-associated gene products.
  • Getting started
    To get started go to the DISNOR home page at http://disnor.uniroma2.it, go to the ‘Disease Browser’ section and click the ‘select a disease’ drop-down menu. It is possible to search by entering a disease name (e.g. ‘Leopard’s Syndrome’). The field also offers autocomplete functionality in order to facilitate searching by name. Alternatively it is possible to scroll-down the provided disease list and select the desired disease (e.g. ‘Leopard’s Syndrome’). The disease list can be filtered by selecting one or more categories: rare disease; syndrome; genetic disease; physical disorder; disease of mental health; disease of cellular proliferation; disease of anatomical entity; disease by infectious agent; disease of metabolism. Once the disease has been selected, press the ‘Draw Network’ button.
    1. Explore the disease page
    2. The disease page in organized in four frames: ‘Disease information’ summarizes disease details, such as name, classification, list of genes associated to the disease according to DisGeNet, and links to NCI Metathesaurus and to MeSH terms associated to the pathology; ‘Relations Viewer’ offers a schematic and detail-rich representation of the causal interactions between the disease associated genes; ‘Complexity Level’ which allows to retrieve and display interactions related to the disease-associated genes (‘seeds’) at different level of complexity; ‘Gene Enrichment Analysis’ allows to compare the overlap between the list of seed genes and the genes associated to all the remaining diseases or to the list of genes associated to pathways (according to KEGG).

    3. Using the graphic visualizer
    4. The graphic visualizer in an interactive graph viewer, where nodes are proteins (or other entities considered in SIGNOR) and edges illustrate the causal relationships between them. The graphic visualizer yields a dynamic, customizable display of the retrieved interactions, whose attributes are summarized with the use of symbols and color codes: direct interactions are displayed as solid lines and indirect ones as dashed lines, while edge color and arrow shape represent the effect (up- or down-regulation): up-regulations are represented as blue arrows, while down-regulations as red ‘T-shaped’ arrows. Nodes also have a color and shape code. The nodes that are used to query the SIGNOR database to retrieve the interactions that form the graph are small dark-green circles, while first neighbours are light green . White circles and blue clover leaves represent protein families and complexes respectively. Yellow squares represent small molecules and chemicals, while phenotypes and stimuli are rectangles.

      Proteins, small molecules, stimuli, phenotypes and other entities are spatially organized in four main cellular compartments: the extracellular space, the plasma membrane, the cytoplasm and the nucleus, according to their manual annotation. Nodes can be manually moved by the user to obtain a customizable layout of the entities. Every relationship is linked to a score, based on literature co-occurrences.

      After clicking on a node a pop-up window displays the entity details: name, external accession number (e.g. UniprotAC, PubchemID), chemical inhibitors and link to the related SIGNOR page.

      By clicking on each edge it is possible to obtain details about the interaction: the mechanism (e.g. phosphorylation, binding…), the cell line or organism in which the interaction has been observed, the reference and the sentence supporting the interaction.

      By clicking on symbols on the upper bar of the visualizer it is possible:

      1. take a screenshot of the graph;
      2. download data underlying the graph representation;
      3. show or hide the interaction score;
      4. show or hide the graph legend;
      5. filter the type and the effect of the interactions or include/hide small molecules or chemical compounds;
      6. select an edge score threshold;
      7. select a type layout among ‘compact’ (where nodes are in close proximity), ’moderate’ and ‘relaxed’ (where nodes are more far apart) layouts;

    5. Visualizing the pathway at different level of complexity
    6. The box allows the user to display interactions at different level of complexity using three different query strategies to retrieve interactions from the SIGNOR database: ‘Connect’ (Level 1) searches for signaling interactions involving only any two entities in the query list; ‘First Neighbours’ (Level 2) is a multi-step strategy that initially performs a search in SIGNOR for all interactions involving any of the seed entities, next prunes nodes with degree-one; and ‘All’ (Level 3) allows access to signaling interactions involving any of the seed entities and all the remaining proteins in the SIGNOR network without any further filtering. By default the visualizer displays Level 2 interactions (‘First Neighbours’); if no interaction is retrieved at level 2 then level 3 is shown;

      By clicking on ‘Add physical interactions from Mentha’ the user can include in the graph also protein-protein interactions from the mentha database (http://mentha.uniroma2.it/) involving any seed entities and filtered by score. Only interactions whose reliability score is higher than 0.4 are shown;

      It is also possible to select ‘edit nodes’ to consult and edit the list of the seed entities, by removing or adding nodes.

      By clicking on ‘Download Relations’ it is possible to download all the interaction visualized in the viewer (note: this does not include mentha interactions).

    7. Identify similar diseases
    8. To identify diseases having some degree of similarity with the query disease (i.e Leopard’s Syndrome), go to ‘Disease-Gene Enrichment Analysis’ frame and click the “disease” GO button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of the disease genes of any other disease in DISNOR. NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the 20 diseases that have a higher degree of similarity (a lower p-value of the observed overlap with query gene list) . For each disease pair (the query one [e.g. Leopard’s Syndrome] versus identified one [e.g. Costello Syndrome]) the tool returns, in the ‘Hits’ column, the ratio of shared genes (calculated, for instance, as the number of ‘Costello Syndrome’ associated genes that are also associated to ‘Leopard’s Syndrome’, over the total number of ‘Costello Syndrome’ genes). The ‘p-value’ column shows the p-value calculated with a randomization test.

    9. Identify Pathways most represented in a disease gene list
    10. To identify pathways in KEGG that are enriched in the seed gene list of the query disease (Leopard’s Syndrome), go to the ‘Disease-Gene Enrichment Analysis’ frame and to ‘Pathways’ and click the ‘GO’ button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of pathway genes as annotated in the KEGG database NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the KEGG pathways that are most represented in the query disease gene list. For each pathway, in the ‘Hits’ column, the tool returns the ratio of shared genes (calculated, for instance, as the number of ‘VEGF signaling pathway’ genes that are also present in ‘Leopard’s Syndrome’, over the total number of ‘VEGF signaling pathway’ genes). The ‘p-value’ column provides the p-value calculated with a randomization test.

Browse by Gene


What are the diseases that have been genetically linked to a gene?

  • This functionality allows to retrieve of a list of Diseases to which the query gene is genetically associated in the DisGeNet database.
  • Getting Started

    To get started go to the DISNOR home page at http://disnor.uniroma2.it, go to ‘Search by SNP or GENE ID’ search field. Enter a gene name (e.g. ‘PTPN11’). Alternatively it is possible to insert the UniprotKB ID of the corresponding protein (e.g. ‘Q06124’). Press the ‘Submit’ button.

    1. Filtering the information
    2. DISNOR, returns a pop-up window which is organized as a Table. The first column lists all the SNP ID of the genetic variants that are associated to the query gene (in this case PTPN11). By clicking on the refSNP ID (e.g. rs11066301), it is possible to access the information on the genetic variant as annotated at dbSNP.

      Finally the third column lists the diseases that have been genetically linked to the query gene. clicking a disease (i.e Leopard’s Syndrome), it is possible to recall the disease pathway page of the disease (see next step).

    3. Exploring the disease page
    4. The disease page in organized in four frames: ‘Disease information’ summarizes disease details, such as name, classification, list of genes associated to the disease according to DisGeNet, and links to NCI Metathesaurus and to MeSH terms associated to the pathology; ‘Relations Viewer’ offers a schematic and detail-rich representation of the causal interactions between the disease associated genes; ‘Complexity Level’ which allows to retrieve and display interactions related to the disease-associated genes (‘seeds’) at different level of complexity; ‘Disease-Gene Enrichment Analysis’ allows to compare the overlap between the list of seed genes and the genes associated to all the remaining diseases or to the list of genes associated to pathways (according to KEGG).

    5. Using the graphic visualizer
    6. The graphic visualizer in an interactive graph viewer, where nodes are proteins (or other entities considered in SIGNOR) and edges illustrate the causal relationships between them. The graphic visualizer yields a dynamic, customizable display of the retrieved interactions, whose attributes are summarized with the use of symbols and color codes: direct interactions are displayed as solid lines and indirect ones as dashed lines, while edge color and arrow shape represent the effect (up- or down-regulation): up-regulations are represented as blue arrows, while down-regulations as red ‘T-shaped’ arrows. Nodes also have a color and shape code. The nodes that are used to query the SIGNOR database to retrieve the interactions that form the graph are small dark-green circles, while first neighbours are light green . White circles and blue clover leaves represent protein families and complexes respectively. Yellow squares represent small molecules and chemicals, while phenotypes and stimuli are rectangles.

      Proteins, small molecules, stimuli, phenotypes and other entities are spatially organized in four main cellular compartments: the extracellular space, the plasma membrane, the cytoplasm and the nucleus, according to their manual annotation. Nodes can be manually moved by the user to obtain a customizable layout of the entities. Every relationship is linked to a score, based on literature co-occurrences.

      After clicking on a node a pop-up window displays the entity details: name, external accession number (e.g. UniprotAC, PubchemID), chemical inhibitors and link to the related SIGNOR page.

      By clicking on each edge it is possible to obtain details about the interaction: the mechanism (e.g. phosphorylation, binding…), the cell line or organism in which the interaction has been observed, the reference and the sentence supporting the interaction.

      By clicking on symbols on the upper bar of the visualizer it is possible:

      1. take a screenshot of the graph;
      2. download data underlying the graph representation;
      3. show or hide the interaction score;
      4. show or hide the graph legend;
      5. filter the type and the effect of the interactions or include/hide small molecules or chemical compounds;
      6. select an edge score threshold;
      7. select a type layout among ‘compact’ (where nodes are in close proximity), ’moderate’ and ‘relaxed’ (where nodes are more far apart) layouts;

    7. Visualizing the pathway at different level of complexity
    8. The box allows the user to display interactions at different level of complexity using three different query strategies to retrieve interactions from the SIGNOR database: ‘Connect’ (Level 1) searches for signaling interactions involving only any two entities in the query list; ‘First Neighbours’ (Level 2) is a multi-step strategy that initially performs a search in SIGNOR for all interactions involving any of the seed entities, next prunes nodes with degree-one; and ‘All’ (Level 3) allows access to signaling interactions involving any of the seed entities and all the remaining proteins in the SIGNOR network without any further filtering. By default the visualizer displays Level 2 interactions (‘First Neighbours’); if no interaction is retrieved at level 2 then level 3 is shown;

      By clicking on ‘Add physical interactions from Mentha’ the user can include in the graph also protein-protein interactions from the mentha database (http://mentha.uniroma2.it/) involving any seed entities and filtered by score. Only interactions whose reliability score is higher than 0.4 are shown;

      It is also possible to select ‘edit nodes’ to consult and edit the list of the seed entities, by removing or adding nodes.

      By clicking on ‘Download Relations’ it is possible to download all the interaction visualized in the viewer (note: this does not include mentha interactions).

    9. Identify similar diseases
    10. To identify diseases having some degree of similarity with the query disease (i.e Leopard’s Syndrome), go to ‘Disease-Gene Enrichment Analysis’ frame and click the “disease” GO button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of the disease genes of any other disease in DISNOR. NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the 20 diseases that have a higher degree of similarity (a lower p-value of the observed overlap with query gene list) . For each disease pair (the query one [e.g. Leopard’s Syndrome] versus identified one [e.g. Costello Syndrome]) the tool returns, in the ‘Hits’ column, the ratio of shared genes (calculated, for instance, as the number of ‘Costello Syndrome’ associated genes that are also associated to ‘Leopard’s Syndrome’, over the total number of ‘Costello Syndrome’ genes). The ‘p-value’ column shows the p-value calculated with a randomization test.

    11. Identify Pathways most represented in a disease gene list
    12. To identify pathways in KEGG that are enriched in the seed gene list of the query disease (Leopard’s Syndrome), go to the ‘Disease-Gene Enrichment Analysis’ frame and to ‘Pathways’ and click the ‘GO’ button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of pathway genes as annotated in the KEGG database NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the KEGG pathways that are most represented in the query disease gene list. For each pathway, in the ‘Hits’ column, the tool returns the ratio of shared genes (calculated, for instance, as the number of ‘VEGF signaling pathway’ genes that are also present in ‘Leopard’s Syndrome’, over the total number of ‘VEGF signaling pathway’ genes). The ‘p-value’ column provides the p-value calculated with a randomization test.

Browse by SNP


What are the diseases that have been linked to a genetic variant?

  • This functionality allows the visualization of a list of Disease Pathways to which a genetic variant is specifically associated.
  • Getting Started

    To get started go to the DISNOR home page at http://disnor.uniroma2.it, and select the ‘Search by SNP or GENE ID’ search field. Enter a SNP ID (e.g. ‘rs11066301’). Press the ‘Submit’ button.

    image of SNP search
    1. Filtering the information
    2. DISNOR, returns a pop-up window which is organized as a Table. The first column contains the Id of the query genetic variant. By clicking the refSNP ID (e.g. rs11066301), it is possible to access the information related to the genetic variant as annotated at dbSNP.

      The second column contains the gene name of the gene linked to the query genetic variant. The gene name is hyperlinked to the SIGNOR page of the entity (in this case PTPN11).

      image of SNP search result

      The third column provides the list of diseases in which the gene variant was observed to be enriched in GWAS studies. By selecting a disease (e.g. Leopard’s Syndrome), it is possible to recall the disease page in DISNOR (see next step).

    3. Exploring the disease page
    4. The disease page in organized in four frames: ‘Disease information’ summarizes disease details, such as name, classification, list of genes associated to the disease according to DisGeNet, and links to NCI Metathesaurus and to MeSH terms associated to the pathology; ‘Relations Viewer’ offers a schematic and detail-rich representation of the causal interactions between the disease associated genes; ‘Complexity Level’ which allows to retrieve and display interactions related to the disease-associated genes (‘seeds’) at different level of complexity; ‘Gene Enrichment Analysis’ allows to compare the overlap between the list of seed genes and the genes associated to all the remaining diseases or to the list of genes associated to pathways (according to KEGG).

    5. Using the graphic visualizer
    6. The graphic visualizer in an interactive graph viewer, where nodes are proteins (or other entities considered in SIGNOR) and edges illustrate the causal relationships between them. The graphic visualizer yields a dynamic, customizable display of the retrieved interactions, whose attributes are summarized with the use of symbols and color codes: direct interactions are displayed as solid lines and indirect ones as dashed lines, while edge color and arrow shape represent the effect (up- or down-regulation): up-regulations are represented as blue arrows, while down-regulations as red ‘T-shaped’ arrows. Nodes also have a color and shape code. The nodes that are used to query the SIGNOR database to retrieve the interactions that form the graph are small dark-green circles, while first neighbours are light green . White circles and blue clover leaves represent protein families and complexes respectively. Yellow squares represent small molecules and chemicals, while phenotypes and stimuli are rectangles.

      Proteins, small molecules, stimuli, phenotypes and other entities are spatially organized in four main cellular compartments: the extracellular space, the plasma membrane, the cytoplasm and the nucleus, according to their manual annotation. Nodes can be manually moved by the user to obtain a customizable layout of the entities. Every relationship is linked to a score, based on literature co-occurrences.

      After clicking on a node a pop-up window displays the entity details: name, external accession number (e.g. UniprotAC, PubchemID), chemical inhibitors and link to the related SIGNOR page.

      By clicking on each edge it is possible to obtain details about the interaction: the mechanism (e.g. phosphorylation, binding…), the cell line or organism in which the interaction has been observed, the reference and the sentence supporting the interaction.

      By clicking on symbols on the upper bar of the visualizer it is possible:

      1. take a screenshot of the graph;
      2. download data underlying the graph representation;
      3. show or hide the interaction score;
      4. show or hide the graph legend;
      5. filter the type and the effect of the interactions or include/hide small molecules or chemical compounds;
      6. select an edge score threshold;
      7. select a type layout among ‘compact’ (where nodes are in close proximity), ’moderate’ and ‘relaxed’ (where nodes are more far apart) layouts;

    7. Visualizing the pathway at different level of complexity
    8. The box allows the user to display interactions at different level of complexity using three different query strategies to retrieve interactions from the SIGNOR database: ‘Connect’ (Level 1) searches for signaling interactions involving only any two entities in the query list; ‘First Neighbours’ (Level 2) is a multi-step strategy that initially performs a search in SIGNOR for all interactions involving any of the seed entities, next prunes nodes with degree-one; and ‘All’ (Level 3) allows access to signaling interactions involving any of the seed entities and all the remaining proteins in the SIGNOR network without any further filtering. By default the visualizer displays Level 2 interactions (‘First Neighbours’); if no interaction is retrieved at level 2 then level 3 is shown;

      By clicking on ‘Add physical interactions from Mentha’ the user can include in the graph also protein-protein interactions from the mentha database (http://mentha.uniroma2.it/) involving any seed entities and filtered by score. Only interactions whose reliability score is higher than 0.4 are shown;

      It is also possible to select ‘edit nodes’ to consult and edit the list of the seed entities, by removing or adding nodes.

      By clicking on ‘Download Relations’ it is possible to download all the interaction visualized in the viewer (note: this does not include mentha interactions).

    9. Identify similar diseases
    10. To identify diseases having some degree of similarity with the query disease (i.e Leopard’s Syndrome), go to ‘Disease-Gene Enrichment Analysis’ frame and click the “disease” GO button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of the disease genes of any other disease in DISNOR. NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the 20 diseases that have a higher degree of similarity (a lower p-value of the observed overlap with query gene list) . For each disease pair (the query one [e.g. Leopard’s Syndrome] versus identified one [e.g. Costello Syndrome]) the tool returns, in the ‘Hits’ column, the ratio of shared genes (calculated, for instance, as the number of ‘Costello Syndrome’ associated genes that are also associated to ‘Leopard’s Syndrome’, over the total number of ‘Costello Syndrome’ genes). The ‘p-value’ column shows the p-value calculated with a randomization test.

    11. Identify Pathways most represented in a disease gene list
    12. To identify pathways in KEGG that are enriched in the seed gene list of the query disease (Leopard’s Syndrome), go to the ‘Disease-Gene Enrichment Analysis’ frame and to ‘Pathways’ and click the ‘GO’ button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of pathway genes as annotated in the KEGG database NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the KEGG pathways that are most represented in the query disease gene list. For each pathway, in the ‘Hits’ column, the tool returns the ratio of shared genes (calculated, for instance, as the number of ‘VEGF signaling pathway’ genes that are also present in ‘Leopard’s Syndrome’, over the total number of ‘VEGF signaling pathway’ genes). The ‘p-value’ column provides the p-value calculated with a randomization test.

Multi-Protein Search


Do you have a list of genes and want to see them in a pathological context?

  • This functionality allows the visualization of a network of causal interactions linking a user-defined list of genes or proteins, and the identification of related DisGeNET diseases and KEGG pathways whose proteins are enriched in the custom list of gene products.
  • Getting started

    To get started go to the DISNOR home page at http://disnor.uniroma2.it, go to the ‘Multi-Protein Search’ section and select the search box. It is possible to enter a custom list of protein or gene by typing the correspondent UniprotKB IDs or gene names separated by tab, space or comma (e.g. ‘P01112, P15056, P10398, P16333, Q13191, Q06124, P12931’). Once the list has been entered, press the ‘Submit’ button.

    image of multi-protein search
    1. Explore the result page
    2. The disease page in organized in three frames: ‘Relations Viewer’ offers a schematic and detail-rich representation of the causal interactions between the input genes; ‘Complexity Level’ which allows to retrieve and display interactions occurring between the query genes (‘seeds’) at different level of complexity; ‘Disease-Gene Enrichment Analysis’ allows to compare the overlap between the list of seed genes and the genes associated to all the remaining diseases or to the list of genes associated to pathways (according to KEGG).

      image of multi-protein search result
    3. Using the graphic visualizer
    4. The graphic visualizer in an interactive graph viewer, where nodes are proteins (or other entities considered in SIGNOR) and edges illustrate the causal relationships between them. The graphic visualizer yields a dynamic, customizable display of the retrieved interactions, whose attributes are summarized with the use of symbols and color codes: direct interactions are displayed as solid lines and indirect ones as dashed lines, while edge color and arrow shape represent the effect (up- or down-regulation): up-regulations are represented as blue arrows, while down-regulations as red ‘T-shaped’ arrows. Nodes also have a color and shape code. The nodes that are used to query the SIGNOR database to retrieve the interactions that form the graph are small dark-green circles, while first neighbours are light green . White circles and blue clover leaves represent protein families and complexes respectively. Yellow squares represent small molecules and chemicals, while phenotypes and stimuli are rectangles.

      Proteins, small molecules, stimuli, phenotypes and other entities are spatially organized in four main cellular compartments: the extracellular space, the plasma membrane, the cytoplasm and the nucleus, according to their manual annotation. Nodes can be manually moved by the user to obtain a customizable layout of the entities. Every relationship is linked to a score, based on literature co-occurrences.

      After clicking on a node a pop-up window displays the entity details: name, external accession number (e.g. UniprotAC, PubchemID), chemical inhibitors and link to the related SIGNOR page.

      By clicking on each edge it is possible to obtain details about the interaction: the mechanism (e.g. phosphorylation, binding…), the cell line or organism in which the interaction has been observed, the reference and the sentence supporting the interaction.

      By clicking on symbols on the upper bar of the visualizer it is possible:

      1. take a screenshot of the graph;
      2. download data underlying the graph representation;
      3. show or hide the interaction score;
      4. show or hide the graph legend;
      5. filter the type and the effect of the interactions or include/hide small molecules or chemical compounds;
      6. select an edge score threshold;
      7. select a type layout among ‘compact’ (where nodes are in close proximity), ’moderate’ and ‘relaxed’ (where nodes are more far apart) layouts;

      image of result viewer
    5. Visualizing the pathway at different level of complexity
    6. The box allows the user to display interactions at different level of complexity using three different query strategies to retrieve interactions from the SIGNOR database: ‘Connect’ (Level 1) searches for signaling interactions involving only any two entities in the query list; ‘First Neighbours’ (Level 2) is a multi-step strategy that initially performs a search in SIGNOR for all interactions involving any of the seed entities, next prunes nodes with degree-one; and ‘All’ (Level 3) allows access to signaling interactions involving any of the seed entities and all the remaining proteins in the SIGNOR network without any further filtering. By default the visualizer displays Level 2 interactions (‘First Neighbours’); if no interaction is retrieved at level 2 then level 3 is shown;

      By clicking on ‘Add physical interactions from Mentha’ the user can include in the graph also protein-protein interactions from the mentha database (http://mentha.uniroma2.it/) involving any seed entities and filtered by score. Only interactions whose reliability score is higher than 0.4 are shown;

      It is also possible to select ‘edit nodes’ to consult and edit the list of the seed entities, by removing or adding nodes.

      By clicking on ‘Download Relations’ it is possible to download all the interaction visualized in the viewer (note: this does not include mentha interactions).

    7. Identify top similar diseases
    8. To identify diseases having some degree of similarity with the query list, go to ‘Disease-Gene Enrichment Analysis’ frame and click the “disease” GO button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of the disease genes of any other disease in DISNOR. NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the 20 diseases that have a higher degree of similarity (a lower p-value of the observed overlap with query gene list) . For each disease the tool returns, in the ‘Hits’ column, the ratio of shared genes (calculated, for instance, as the number of ‘Costello Syndrome’ associated genes that are also associated to ‘Leopard’s Syndrome’, over the total number of ‘Costello Syndrome’ genes). The ‘p-value’ column shows the p-value calculated with a randomization test.

      image of disease similarity result
    9. Identify top enriched Pathways
    10. To identify pathways in KEGG that are enriched enriched in the seed gene list, go to the ‘Disease-Gene Enrichment Analysis’ frame and to ‘Pathways’ and click the ‘GO’ button. The algorithm compares the list of genes that are displayed in the disease graph with the lists of pathway genes as annotated in the KEGG database NOTE: to limit server overload, a maximum number of 50 proteins will be used as input for each gene list in this analysis.
      The tool returns a table showing the KEGG pathways that are most represented in the query gene list. For each pathway, in the ‘Hits’ column, the tool returns the ratio of shared genes (calculated, for instance, as the number of ‘VEGF signaling pathway’ genes that are also present in ‘Leopard’s Syndrome’, over the total number of ‘VEGF signaling pathway’ genes). The ‘p-value’ column provides the p-value calculated with a randomization test.

      image of pathway analysis result

Documentation

  • Database Structure


    Introduction

    Disnor's conceptual develoment is tightly linked to the knowledgebase offered by SIGNOR's high quality signaling information. For this reason, the data used within Disnor is conceived as an extension of SINGOR's database as opposed to a stand-alone one.
    The diagram below provides a "zoomed-in" view of where the data originated from, how it is linked, and how the idea behind Disnor brings it all together.

    Note: Only the SIGNOR tables that are involved in the Disnor project are showed in the diagram.

    Diagram Legend

    • the purple area contains data that was imported from DisGeNET:
      disease information, including their Disease Ontology category attribution, umls and meshIDs were imported into the database together with their GDA information
      GDA information was used to form relationships between gene data in SIGNOR and disease data.
      SNP information was also imported from DisGeNET and used to link SNP to gene data in SIGNOR and SNP to disease data.
    • the dark green area contains SIGNOR data:
      entity data that is being linked to diseases to enrich the available descriptive information
      signaling data curated in the form of binary causal interaction within SIGNOR, later extracted to render disease networks within the resource.
    • Abbreviations
      PK: Primary Key
      FK: Foreign Key
      UIX: Unique Index
      blank: Simple Row
    • Relationships
      Relationships have been described using arrows that lead Foreign Keys to the referenced table field. Keys linked by a relationship will have the same color.
    • Tables
      The description of each table can be found in the section below the diagram.

  • Table Description show


  • Row Description
    disease_association
    disease_idUnique ID assigned to a disease
    diseaseDisease name, as imported from DisGeNET
    idsList of linked UniprotKB IDs, as imported from DisGenNET
    umlsUnified Medical Language System ID, to provide xref to Metathesaurus
    mesh_idMedical Subject Headings ID, to provide xref to MeSH
    associated
    entity_idReferences entity_id within the entity table, linking a disease to a biological entity
    disease_idReferences disease_id within the disease_association table
    entity
    entity_idUnique ID assigned to a biological entity
    type_idDefines entity type within the SIGNOR database classification
    entity_nameReports main entity name (gene name for proteins)
    entity_db_idReports the ID from database of origin to provide accurate xref information (UniprotKB ID for proteins)
    snp
    snp_idUnique ID assigned to each SNP
    snp_db_idReports the ID from dbSNP at NCBI, to provide useful xref link
    gene_nameReports the name of the gene it is linked to
    snp_to_gene
    snp_idReferences snp_id in snp table, linking a SNP to its gene
    gene_idReferences entity_id in entity table
    snp_to_disease
    snp_idReferences snp_id in snp table linking a disease to a SNP
    disease_idReferences disease_id in disease table
    disease_cat
    cat_idUnique ID assigned to each disease category
    cat_nameCategory name as assigned by Disease Ontology
    cat_doidDisease Ontology ID
    cat_descriptionDescription of category, data from Disease Ontology
    cat_filter
    cat_idUnique category ID, references disease_cat table
    disease_idUnique disease ID, references disease_association table. Each disease can be linked to multiple categories.
    regulates
    relation_idUnique ID assigned to each relation curated within SIGNOR
    entityaIt defines the regulator in the context of a SIGNOR relation and references to Unique Index (UIX) entity_db_id in the entity table
    entitybIt defines the target in the context of a SIGNOR relation and references to Unique Index (UIX) entity_db_id in the entity table
    mechanismMechanism through which the regulation takes place
    effectEffect of the regulatory interaction
    modificationa, modificationbDescribes modification that may alter entitya/b
    modulator_complex, target_complexDescribes complex modulator/target are involved with, if any
    pmidpmid of literature the interaction has been extracted from
    tax_idTaxonomy ID of the organism the interaction has been observed in
    residueresidue that is involved in the interaction, if known
    sequencethe 15aa sequence surrounding the residue involved in the interaction
    modares, modbresresidues involved in modificationa/b
    modaseq, modbseq15aa sequences surrounding modares/modbres
    directdefines whether the interaction is known to be direct
    sentencea passage from the scientific article the information has been extracted from
    scorescore assigned to interactions between proteins based on co-occurrence in literature (see more)
    annotatorthe person who curated the interaction
    date_addeddate when interaction was first added to SIGNOR

Acknowledgments to original resources

  • All Gene-Disease Association (GDA) and SNP-Disease Association data was retrieved from DisGeNET v5.0 under the Open Database License
  • Disease classification categories were extracted from Disease Ontology, an open source ontology for the integration of biomedical data that is associated with human disease
  • Causal signaling interaction data is extracted from SIGNOR (the SIGnaling Network Open Resource)
  • PPI interaction data used in the network generation options is imported from mentha, a resource for browsing integrated protein-interaction networks
  • KEGG PATHWAY is a collection of manually drawn pathway maps representing details on the molecular interaction, reaction and relation networks. Disnor imports the pathway data found in this source in order to provide enrichment analysis tools
  • Disnor's disease information is mapped to NCI Metathesaurus, a wide-ranging biomedical terminology database, under the under the terms of the UMLS® Metathesaurus® license, and Medical Subject Headings, the NLM's curated medical vocabulary resource
  • Protein data displayed within Disnor is linked to UniprotKB, a resoure that provides a collection of functional information on proteins
  • Chemical data displayed within Disnor is linked to PubChem, a resource that provides information on the biological activities of small molecules