UC Riverside: New computer language helps spot hidden pollutants. “Developed at UC Riverside, Mass Query Language, or MassQL, functions like a search engine for mass spectrometry data, enabling researchers to find patterns that would otherwise require advanced programming skills. Technical details about the language, and an example of how it helped identify flame retardant chemicals in public […]
https://rbfirehose.com/2025/05/16/uc-riverside-new-computer-language-helps-spot-hidden-pollutants/
From invisible toxins in water to untapped medical compounds — this new language helps scientists find what others can’t. #MassQL #PollutionDetection #ChemTech
https://geekoo.news/digital-microscope-for-pollution-massql-transforms-chemical-research/
Exciting innovation in scientific research! 🚀 Researchers at the University of California, Riverside (UCR) have developed Mass Query Language (MassQL), a groundbreaking programming language designed to revolutionize the analysis of mass spectrometry data. This tool democratizes data interpretation, enabling biologists and chemists to extract meaningful insights without advanced coding skills.
💡 Key Highlights:
👉 Universal "Search Engine": MassQL acts as an intuitive yet powerful query language for mass spectra.
👩🔬 Broad Accessibility: Empowers life scientists to fully exploit complex molecular data.
💧 Environmental Impact: Successfully used to uncover previously undescribed organophosphate pollutants in global water samples.
🔬 Versatile Applications: Potential in biomarker discovery, microbial communication, drug development, and more.
This development highlights the transformative power of interdisciplinary collaboration, bridging the gap between computer science and life sciences.
#MassQL #ScientificResearch #DataAnalysis #EnvironmentalScience #Biochemistry
https://scienmag.com/innovative-computer-language-uncovers-hidden-environmental-pollutants/
SpectraQL https://bit.ly/3UqoPXN adds support for the #MassQL query language to 
/ #Spectra
The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects.
ENPKG integrates or is built on many computational metabolomics tools, such as #LOTUS, #SIRIUS, #GNPS, #matchms, #spec2vec, #GNPSDashboard, or #MassQL! A big thank you to the people behind them 🙏
➡ More info in the preprint: https://doi.org/10.26434/chemrxiv-2023-sljbt
Modern natural products (NPs) research relies on untargeted liquid chromatography coupled with mass spectrometry metabolomics. Together with cutting-edge processing and computational annotation strategies, such approaches can yield extensive spectral and structural information. However, current processing workflows require feature-alignment steps based on retention time which hinders the comparison of samples originating from different batches or analyzed using different instrumental setups. In addition, there is currently no analytical framework available to efficiently match processed metabolomics data and associated metadata with external resources. To address these limitations, we present a new sample-centric and knowledge-driven framework allowing multi-modal data alignment - e.g. through chemical structures, biological activities, or spectral features - and demonstrate its value in exploring large and chemodiverse natural extract datasets. Here, the experimental data is processed at the sample level, matched with external identifiers where possible, semantically enriched, and integrated into a unified knowledge graph. The use of semantic web technology enables comparison of processed and standardized data, information, and knowledge at the repository scale. We demonstrate the utility of the developed framework, the Experimental Natural Products Knowledge Graph (ENPKG), to leverage the results obtained from screening 1,600 plant extracts against trypanosomatids and streamline the identification of new antiparasitic compounds. Thanks to its versatility, the proposed approach allows for a radically novel exploitation of metabolomics data. Semantic web technologies are a fundamental asset and we anticipate that their adoption will complement the current computational metabolomics pipelines and enable the community to advance in the description of global chemodiversity and drug discovery projects.
ResearchBuzz: Firehose
Geekoo
Brian Greenberg 
Johannes Rainer
Pierre-Marie Allard