PainNetworks.org - Overview of site functionality

December 12, 2012

1 Intro

This Vignette presents an overview of the website, outlining the different featuresavailable to the user to analysise their gene(s) or experiment(s) of interest.

The basic search functionality of the website is presented first, alongside detailsof how the user can view the resultant network produced by the site (Section 2).Several other ways in which the user can view and modify his search results usingthe different tabs are then presented in Section 3. This is followed by some examplesof how a pain researcher might potentially use the website (Section ??).

2 Search functionality

The default view of the website is shown in Figure 1. This is the first page of thesite that visitors will see. The network will be displayed in the pane on the left andthe search options are in the pane on the right. We will now go through some of themain features of this website view using Figure 1 as a guide. Each of the maroonnumbers refers to a different feature of the website.

1. Organism-centric views: Given the difficulty in investigating neuronal tissueusing humans (Antunes-Martins et al., 2012) much of the data we have on paingenetics is produced using animal models of pain. Conversely, the majority ofthe data in public databases on protein-protein interactions comes from human.As explained above we can deal with this by inferring interlogs and thus we cantransfer interaction annotation between species (more details in Section ??).

However these methods are not error free and not all genes/interactions willhave equivalents in other organisms. For example a rat gene changing in ananimal model of pain will not necessarily have a direct equivalent in human; aninteraction between two proteins in human will not necessarily be transferredto rat, since one of the interacting proteins might not have a direct equivalentin rat. Therefore we present human, rat and mouse-centric views of the data.

Each organism-centric view will show the genes and interactions found in thatspecies as well as the orthologues that could be mapped from other species.Interactions will also be inherited from other species but they will be shown

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Figure 1: The default view of the website. More details on the different features aregiven in the numbered paragraphs below, the numbers of the paragraph correspondthe the numbered parts of the website shown here.

a different colour. Figure 2 shows the human and mouse-centric views for thesame network. Note that the green edges show inherited interactions and blueedges show interactions that were were uncovered in the given species. We seethat most interactions were found in human and that the mouse-centric viewinherits many of these interactions.

2. The network panel: Clicking on a node of interest brings up additional in-formation about that gene. This information includes more general annotationsuch as gene ontology (GO) annotation and protein-domain information, de-tails of the tissues in which the gene is expressed and information on whetherthe gene represented by the node shows differential expression in any pain-related microarray datasets within the database (Figure 3, top row). Edgesare also clickable. Clicking on an edge brings up the Pubmed ID from whichthe interaction was taken. This allows the researcher to check the source ofthe interactions in their network. Examples of the type of information that isbrought back when clicking on a node or edge of interest is found in Figure3 (bottom row). Unbroken lines represent indirect interactions, broken linesrepresent indirect interactions (more details direct and indirect interactions aregiven in ??.

3. The search bar: Here the user enters the query gene or set of query genes withwhich the database will be searched. This will return the query gene or genes,displayed alongside their interactors. If these interactors have connections toeach other these will also be shown.

4. Preset query genes.: The user does not need to have to have a set ofquery genes in mind. It is possible to search the database using a preselected

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Mouse-centric view Human-centric view

Figure 2: Organism-centric views. Nodes represent genes, edges represent associa-tions between genes (either interactions between the protein products of known genes(solid lines) or co-complex membership(broken lines)). Blue edges represent associ-ations between genes that have been established in the given organism (mouse orhuman in this case). Green edges represent associations between genes that were in-herited from other species, through the interlogs method described in the manuscriptfor the site (ref).

list of genes. The user can select such a list by clicking on the dropdownbar, which contains various lists of genes. For example, the user might select“gxd anatomy” from the list. This allows the user to select all genes that areexpressed in a given tissue as their query genes, as inferred by the MGI GXDexpression data. After selected “gxd anatomy”, the user would then enter thetissue of interest in the search bar below, such as “Dorsal Root Ganglia”.

5. Filtering options: Default option is to display all known interactions for thequery genes, and the interactions between the interactors of the known paingenes. However this can lead to a very large number of genes and interactionsbeing displayed. In addition, some of the interactors might not be relevantwithin the context of the network that the user is building. For example, theuser might be interested in the interactions between his query genes and othergenes that show expression in the dorsal horn of the spinal cord only. One wayto reduce the number of genes in the network and to make the network morespecific to a system of interest is by filtering the results. This can be done byspecifying a tissue type of interest and keeping only the genes that are knownto be expressed in a given tissue. Other ways to filter the network would beto only include direct protein-protein interactions as links between genes, andto only keep interactions discovered in the organism for which the network isbeing built (e.g. the network is mouse-centric, only keep PPIs discovered inmouse).

6. Clustering: Various algorithms can be used to search for clusters in the net-

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Figure 3: Obtaining node and edge annotation.

work (described in Section above). These clusters can be ranked. Clicking onthe cluster tab will enable the user to view the different clusters. More detailson the cluster tab and the ways in which the clusters can be ranked are givenbelow under “Clusters Tab” and shown in Figure 6, below.

7. Experimental data: It is possible to use the microarray data to highlightnodes in the network (if a given gene is changing in a microarray experimentthe node representing that gene will change colour. Examples of highlightedDE genes onto the network are shown in Figure 4.

3 Results tabs

We have demonstrated the basic search functionality of the website. By default thesearch results is displayed as the network, in the network tab to the left of the searchbox. However the search results can be viewed in other ways, other things can beshown in the network tab. In order to obtain a different view of the results the user

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Figure 4: Highlighting differentially expressed genes. Nodes are coloured accordingto the direction and magnitude of fold change. Red = a positive change (increasedexpression in the “case” samples compared to the “control” samples, blue = negative.Brighter colours = more evidence for differential expression. Only genes with anadjusted p-value for differential expression < 0.1 are shown.

must click on one of the several tabs positioned above the search box, these are shownin the top-right of Figure 1 above.

3.1 Network Genes Tab

The network genes tab, shown allows the user to view all genes in the network pane,alongside various annotation sources. By default the Ensembl ID, official gene symboland description are given, as well as details of different microarray datasets in whichthe given gene is DE. More annotation can be added by clicking “get more functionalannotation”. The user is able to download this list of genes along with the selectedannotations, as a csv file.

3.2 Clusters Tab

The user can use network clustering algorithms to search for different clusters ofgenes within the network as described in the methods section above. The clusterstab allows displays the clusters ordered by either the number of pain-related genesin each cluster, the number of DE genes taken from microarray pain experiments orthe size of the cluster.

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Figure 5: Viewing all genes in the network alongside their annotation.

3.3 Network Selection Tab

This tab allows the user to highlight genes in the network corresponding to variousannotation sources, as shown in Figure 7 above. The user can use this feature to spotclusters of genes displaying similar properties, for example a cluster of genes codingfor transmembrane proteins. It could also be used to look for druggable proteinsinteracting with the large number of pain genes.

3.4 Pain Model Genes Tab

This tab shows the pain model genes. These genes can come from the Pain GenesDatabase (PGD) or OMIM, or can be entered by the user (as described above inSection ??). Note these are different to the query genes. The user searches using querygenes and this returns a network made from the query genes and their interactions.Any pain model genes within this network will be highlighted in grey. This is useful

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Figure 6: Viewing different clusters in the network.

if the user has a list of possible pain genes, which are used as query genes, and wantsto see how many of the query genes lie close in the network to known pain genes(pain model genes). Pain model genes can also be used when clustering the network,to return clusters with a high enrichment in pain genes.

3.5 Update Pain Model tab

The pain model genes can be changed as shown in Figure 9 in accordance with theinterests of the user. Currently OMIM genes or PGD genes can be used. There is alsothe option for the user to input their own list of genes, depending on their researchinterests.

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Figure 7: Highlighting network genes. The user can highlight genes in the networkwith certain features, such as possible transmembrane proteins, predicted using theTMHMM algorithm (top), and druggable proteins (bottom).

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Figure 8: Pain model genes. Additional information on the genes can be obtained byclicking “get more functional annotation”, in the same way as for the Network GenesTab shown above in Figure 5.

Figure 9: Updating the pain model genes. The pain model genes can be changed.OMIM genes and genes from the Pain Genes Database are included. In addition theuser can input their own genes.

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