still in development

Yes, don't use that for the moment in classical networking

Yes, you can do that, thats the default intensity threshold. That is relative to the base peak in the MS2.

So now the 85 peak will need to be within 10% of the intensity of the 393 peak. You can put any expression on the 85 peak to modulate up or down for what you want

QUERY scaninfo(MS2DATA) WHERE MS2PREC=393.2283:TOLERANCEMZ=0.1: INTENSITYMATCH=Y:INTENSITYMATCHREFERENCE AND MS2PROD=85.029:TOLERANCEMZ=0.1: INTENSITYMATCH=Y:INTENSITYPERCENT=10

Hi @galanojeanmarie.bsky.social Yes, you're just missing one thing with the variable Y to determine the peak intensity of the second one.

LOL - fun problem to have. I think this might be possible - I think the main graphml, we'll just need to get the actual task and display title.

Thanks for the feedback - let me see if we can integrate. We've already added direct links for modifinder - so we can easily push it out to the resolver as well with the mirror plots.

If you're new to MassQL - definitely checkout the research briefing that describes how MassQL enables scientists to precisely express and reproducibly search for mass spectrometry patterns in large datasets:

www.nature.com/articles/s41...

and find the full article:

www.nature.com/articles/s41...

Big thanks to Xianghu Wang for all the work as lead author and all coauthors who made this possible and the funding from @corteva.bsky.social

While most of the clustering innovation in mass spectrometry has focused largely in proteomics - we hypothesize due to the ability to assess performance - I hope that tools like MS-RT can accelerate the computational innovation in metabolomics.

After validation, we used MS-RT to evaluate the performance of several commonly used MS/MS clustering tools used in proteomics, specifically MS-Cluster and Falcon. We found that Falcon made generally favorable tradeoffs between purity can clustering completeness (how much was actually clustered).

We validate this MS-RT approach by using proteomics MS/MS dataset and comparing the purity estimates from MS-RT with estimates using state-of-the-art proteomics database search approaches. We found that while not exactly identical the relative order across clustering tools is maintained.