I research fairly understudied protein and am interested in finding his protein partners in order to model the PPI.
I've used databases (STRING, Coexpedia and others) in order to select partners and hit some prominent leads, but before I continue I was wondering whether to opt for the manual coexpression analysis.
Could I get clearer picture from the analysis of a big dataset?( for example from RNA-seq of coding RNA from tissue samples of 122 human individuals representing 32 different tissues)
How time consuming would be to run such pipeline and where to find a proper dataset for the analysis (I mean which criterias are essential to ensure that this dataset is good)?
Andrii Dzobak , Yes, doing your own coexpression analysis can give you a clearer picture, especially for understudied proteins where databases like STRING or Coexpedia might miss context-specific partners. Using a large RNA-seq dataset (like GTEx) with many tissues and individuals is a great idea — it helps reveal tissue-specific coexpression patterns that might be more biologically relevant.
Make sure the dataset has good sample size, clean metadata, and consistent sequencing methods. GTEx is a solid choice — it's public, well-annotated, and widely used. The analysis pipeline (filtering, normalization, correlation) isn’t too time-consuming — maybe a day or two if you're familiar with R or Python. Totally worth it if you're aiming to model PPIs more accurately.
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