We have our own software that can do this. However, I have not tried it with such a large number of entries, so I would not recommend trying this directly with our web server. Our web server will only draw networks with

For protein-protein network, you can also use the software Cytoscape (cytoscape.org) with the Bisogenet plugin (you will need to add it).

You can use Ingenuity Pathway Analysis to draw interaction networks if you want to see if you're picking up any pathways....http://www.ingenuity.com/ There is a free trial you can sign up for...

Other information you can get from your data is label free quantification results...if you put your data files through a program called Scaffold...it will automatically give you relative protein abundance data within your sample set (i.e. it will tell you which proteins are in higher concentrations than others). If you have samples from different patients it can compare relative abundances of proteins across different patients, then you can see if there are proteins that go up and down in different diseases etc... http://www.proteomesoftware.com/

2000 proteins - I think the chances to deduce deeper insights from such a big set are limited. Can you narrow it down to a smaller - more significant list? You could try to search for enriched gene ontology terms in your list. This could point you to biological processes or functions which are overrepresented in your set of proteins. I do not have tried this tool yet, however it should take proteins lists: http://cbl-gorilla.cs.technion.ac.il/

Dear Kambiz

How did you collect these 2000 proteins? You can reach me at: [email protected]

Regards,

Mohsen

Thank you for all you comments. Regarding how the proteins have been collected different techniques were use such as MudPIT, GeLC-MS/MS, 2D-DIGE, 2-DE.

Is it not possible to compare different protein-protein interaction software and see if they will come out with a significant protein out? After that try out this protein in different patient such as azoospermia patient or other infertile patient?

We use ProteoIQ with quantitative label free analysis using area under the curve. We use the GO annotations and have the ability to create protein sets of just specific molecular functions and look at the changes in quantitation over the different conditions. So if you wanted to see the changes in proteins involved in process of cell proliferation or DNA replication you can exract those proteins from the list and look at the quantitative changes over time of the samples of just that group. You also have the option of using spectral counting but the AUC is more accurate.