I’m curious how this compares to traditional tools (like scripting in Python/R) for analyzing such datasets, both in ease of use and performance. Also, could similar query languages be developed for other fields (genomics, imaging, etc.) to empower domain experts? It’s cool to see a new DSL in academia

This is an anti-pattern. Do not make DSLs for subdomains of science. All scientific data can be stored and queried using general-purpose data analysis tools.

MassQL Introduction: https://mwang87.github.io/MassQueryLanguage_Documentation/

Was not familiar with the details of mass spectography.

Found this[1] page which includes an explanation of how it works, and also shows some analysis using the Spectra R package.

The SpectraQL[2] package adds MassQL support to Spectra, so one can compare a bit.

[1]: https://uclouvain-cbio.github.io/WSBIM2122/sec-ms.html

[2]: https://doi.org/doi:10.18129/B9.bioc.SpectraQL

I worked as a postdoc in the same lab as Ming (the creator) when he was developing the first versions of this.

I am on the cheminformatics side and have done a lot of querying of data from mass spec files and having this as a DSL is highly convenient.

Typically we would use R/Python and the wetlab focused chemists would use GUI tools to inspect and compare spectra. I have also experimented with loading data into an SQL database for the obvious reasons and because the amount of data collected in the lab was such that indexing could be highly useful.

The major convenience in MassQL is the ppm thresholding that is build into every query, it makes any SQL you might write super long and ugly. Both the built-in support for isotope ratios and to be able to query both the full scan and the individual MS2 simultaneously is super useful. The wetlab people also found it much more intuitive than building a script in python or making convoluted SQL queries that could do the same.