An object can defined as a distinct entity but not its property. This is because properties are always viewed in terms of precision of description. For example if I had two genes and asked their property one could say they were enzymes, but then you could also say they were ligase and hydrolase. While calling them enzyme made them identical, hydrolase and ligase are distinct properties. Therefore, in analysis of functions (because it is a property) it is very important at what level of description we are making any comparison to really get a comparative evaluation. One cannot make a distinction between two enzymes by calling one an enzyme and another an hydrolase.

The Gene Ontology dictionary contains sets structured vacabularies describing function. Each of these function is related to each other in a acyclic graph structure. The parent node is "Gene Ontology" and its two children are "Molecular Function" and "Biological Process". The idea on precision of description of a function term can be had from the depth of the graph at which the term is located. During evaluation therefore we should be careful what terms we are comparing and at what depth. Gene Ontologies terms are not distributed evenly over all depth and some times one term can be at several depths depending on what children you are looking at. But getting an overall view of the ontology graph helps you to understand the function and compare ORFs much better than otherwise. For this purpose we have a threshold Depth Levels in the map viewer that goes from 1 to 10. You can also view the whole graph of only the children or parent. These three options to my opinion will allow you to explore function in all prespectives. When you select a depth of 1 from example, what the program does is to extract the parent term at depth 1 for the query. But if you had selected depth 8 and there was one ontology term that you queried was from depth 5, then it would take the input term.

There are several other parameters to play with. The prediction threshold parameters and the drawing parameters. The prediction threshold parameters can be varied to screen the predictions taken to draw the graph. These are basically the three parameters output by the program ProKnow. The description on these can be had from the links provided on each of the screening paramaters.

Drawing graphs that are legible is not a trivial problem. Therefore I have provided parameters that can be varied to get a proper output. What you want to get is entirely at your choice. Because graphs that contain lot of nodes can be very difficult to interpret, I have set point nodes as the default. You can change this to box or some other shape and change page, image and fontsize to get a clearer graph. In case the graph you got is very complicated, you can see portions of the graph by entering the ontologies themselves rather than having them from the ORFs.
The thresholds are shown as 0, -1 and -2 ... for the headers for some pictures. These do not reflect the actual depth, but a symbolic depth used by the program.

You can only enter a legible ORF name (an Rv code for example in TB genome) or a correct ontology code to be able to do a successful query. In the individual information category, the input fields take on ORF name or ontology and returns results as sortable table. In the networked Information category, you can enter as many ORF names and Ontologies. When you enter ORFs against the first radio button, the program extracts all ontologies associated with it and then draws the ontology graph. If you know the ontology associated with an ORF, you can also enter it against the third radio button and get a zoomed in picture of the graph. This way you can get around looking at any type of graph at any resolution. If you are really intent to see a very big graph, it may not be possible in the browser, as it may not load. You may however download the picture by locating the picture name in "View --> Source Code" option of your browser. In that case you will have to paste appropriate file name in the location bar of your browser.

An important question one askes about two ORFs is how many ontologies they share. This helps understand relations between processes and how they interact. As in all drawings, here too you can set ontology depths or work with the "selected nodes" as available directly from the query. Each node in the graph will show the ontologies associated with the ORFs and the number of intersections between them.