Networks

One network is available for exploration:

1. The MiRNAs and lncRNAs in Melanoma network gives a comprehensive overview of melanoma-relevant interactions between miRNAs, lncRNAs, and genes. It was scientifically curated in a manual fashion and is well-suited for exploration.

Adding your own data sets

You can add your own data to the network for data mining purposes. Due to the way this is implemented, no data upload to our server is necessary. Your data is only ever stored in your own browser session.

To successfully add data, you must first prepare a CSV file. This can be done in a spreadsheet application like LibreOffice or Microsoft Excel. The spreadsheet should look similar to this one:

• The table must have a header in the first row, and the header of the first column must be "Symbol". The headers of the remaining columns can be freely chosen , with each one representing one condition or difference.
  Column headers must be unique across all files you upload in one session.
• The first column lists the gene symbols for which you have data. As the map was set up to permit the analysis of human data, you need to employ the official HGNC symbols. (These are often identical to their mouse homologs' official symbols, but there are notable exceptions.) Gene symbols can be found in many other databases - see the below NCBI Gene and UniProt screenshots for eotaxin (CCL11) for reference:
  
  The page will automatically match up your symbols with all the corresponding factors (if there are any).
• All columns after the first list the values you want to project, with one condition or difference per column.
  There is no restriction on the kind of values you use, apart from them having to be numbers. The interpretation, of course, is yours to make.

Once you have the spreadsheet prepared, export a file in CSV format. Then click on the very first list entry Your data (click to add) and select the exported file. Your column names will be appended as data sets to the first list entry and can be selected for visualization from there.

Highlighting factors via ontology associations

We have associated each factor in the network with the GO term and Reactome ontologies. Ontology terms that were related to at least three genes in the current map can be selected from the drop-down menu, and the associated factors will be highlighted with a golden halo.

Legend colors and how to adjust them

While no data set is selected, the factors in the current map are colored according to their molecular type. Some will also have individual shapes. In this state, the legend shows a color code for reaction and factor types.

On selecting a data set, the legend will adapt to represent the spectrum of log₂FC values that were observed in the selected categories. Blue represents down-regulation and red up-regulation under the listed conditions.

The log₂FC color legend comes with two sliders that can be used to adjust the colors in the network. The sliders initially occupy their respective maxima, which is -1 and 1 after normalization, respectively. Moving either slider closer to zero will increase the saturation in all like-colored pie slices, while moving them towards the limits will decrease it. Note that all factors with absolute log₂FC higher than the adjusted slider position will drop out of the linear color projection range and appear fully saturated.

Extracting customized subnetworks

You can focus on your molecules of interest by eliminating all other factors from view. To do this, first mark all relevant factors via either of two methods:

• Click on a factor in the network view and press the Mark this factor button.
• Select the factor from the second drop-down menu below the one for regulatory subnetwork.

No matter which method you use, every marked factor will be added to the above-mentioned drop-down and shown with a thick black rim in the network view. Factors can be unmarked by deleting them from the drop-down or by clicking the Unmark this factor after seleting it in the network view.

Once you are finished with your selection, click the Extract button at the bottom right side. Its label will change to Restore and all unmarked factors will be temporarily removed from the network view. If the ☑ Include connected factors option above the button is checked, factors with direct connections to the marked ones will remain visible.

While a customized subnetwork is shown, you cannot mark or unmark factors. To restore the original network and re-enable factor marking operations, click the Restore button.