Welcome to the description of the GOSP-FIT tool for identifying proteins or genes annotated to a function similar to a given GO term. The tool provides a stepwise query selection menu, enabling the user to construct a query and adapting the selection choices in the process, leaving only relevant options open that correspond to his/her selections.

The user input is a list of GO Ids. In this case, input is GO IDs aligned and pasted in the Input text area or uploaded from a file. The user input is of the following form:

GO ID1

GO ID2

GO ID3

GO ID4

Depending on the term semantic similarity approach selected, the GOSP-FIT tool produces a comprehensive summary in a table format on the next page of the user interface. Output is a table with seven columns as shown below:

Note that in all the DaGO-Fun applications, the level of a term is the length of the longest path (or the maximum number of links) from the root of the ontology down to that term. The root itself is located at the level 0 considered to be the reference level. Note that by clicking on a given active GO ID, the associated details about the term in the GO-DAG are displayed using the AmiGO tool. The new page provides information about all proteins identified to be related to the term and allows the user to access the information about a specific protein by clicking on it.

We aim to let the GOSP-FIT tool retrieves proteins for as many user GO ID inputs as possible, however, because of limitations in computational resources, we have to balance the maximum number of GO terms for each user query. The maximum number of GO terms is 20, in which case the tool will display only 10 of them stepwise. Unfortunately if you have cases where your data exceed these limitations, it is necessary to divide the input data, run the GOSP-FIT tool separately, and merge the results at the end of the process. Alternatively you can contact the administrators who are willing to collaborate and run large data sets for analysis.

For more information, please refer to the associated publication: "Gaston K. Mazandu and Nicola J. Mulder. DaGO-Fun: Tool for Gene Ontology-based functional analysis using term information content measures, 2013", DaGO-Fun preliminary paper currently under review.