Question: What Is Your Experience With The String (Interactions) Database?
gravatar for Giovanni M Dall'Olio
10.7 years ago by
Barcelona, Spain
Giovanni M Dall'Olio420 wrote:

STRING is a database of predicted protein-protein interactions at EMBL. It cluster the results from many sources of protein-protein interactions databases, like Mint, etc.., and it also use the informations from KEGG-pathways and reactome, to provide the best annotations for the interactions of a protein.

I am a bit confused from the results that I see there, because when I look at the genes in the pathway I am studying, I see many errors and annotations that I don't understand.

What is your experience with STRING? If you want to do me a favor, go there and try to see the interactions annotated for a gene that you know already. Do you see anything weird?

ADD COMMENTlink modified 10.6 years ago by Andrew Su0 • written 10.7 years ago by Giovanni M Dall'Olio420
gravatar for David Nusinow
10.7 years ago by
Boston, MA
David Nusinow50 wrote:

I've been using STRING extensively, but not for protein-protein interactions work. STRING, as you note, is a bit of a mutt in terms of the different data sources it mines. Some that you're missing include a broad literature-based search, as well as gene expression data sets. So if you're interested primarily in physical interactions or any other single type of data source, STRING is a poor choice for your work. On the other hand, STRING does provide confidence scores for each association, as well as annotation for their data source types (with the license). So you can use those to filter out the interactions derived from data types you don't want to see.

ADD COMMENTlink written 10.7 years ago by David Nusinow50
gravatar for István Albert
10.7 years ago by
István Albert ♦♦ 310
University Park
István Albert ♦♦ 310 wrote:

I have not used STRING in particular but I have worked with protein interactions before (DIP dataset). I recall that even experimentally produced protein-protein interactions may have very large false positive ratios (as for false negatives, who knows?) Some papers claim that up to 50% of the interactions were spurious; and repeated experiments showed very small overlaps. Predictions may be even less reliable.

At the same time the DIP dataset performed substantially better if we only considered the interactions for which there were multiple sources of evidence, so that may be a strategy to consider in your case as well.

ADD COMMENTlink written 10.7 years ago by István Albert ♦♦ 310
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