Panome Bio, a CRO, launched MassID
, a cloud-based computational platform designed to improve how researchers process and interpret untargeted LC/MS metabolomics data. The platform is described in a bioRxiv preprint titled “MassID provides near complete annotation of metabolomics data with identification probabilities.”
Untargeted metabolomics is a powerful approach for biological discovery, capable of detecting hundreds to thousands of small molecules that reflect physiology, disease state, and response to treatment, according to company researchers, who added, however, that its maximal impact has been limited by the complexity of LC/MS datasets. They explained that it is highly challenging for researchers to identify the true metabolite signals hidden among the tens of thousands of signals coming from chemical noise and artifacts. As a result, this has hindered the ability of researchers to identify all metabolites present in the sample while also generating high-confidence biological interpretations.
MassID was developed to address this challenge by providing an end-to-end computational pipeline that converts raw LC/MS/MS data into cleaned, normalized, and annotated metabolite profiles—while also introducing probability-based confidence scoring for the metabolite identifications made, noted Gary J. Patti, PhD, CSO of Panome Bio.
“Metabolomics is capable of incredible biological insight, but the field has been held back by the complexity of LC/MS data and the inability to assign validated confidence to metabolite identifications,” said Patti, who is also a professor at Washington University in St. Louis. “MassID changes what’s possible by introducing probabilistic metabolite identification and global false discovery rate control, which brings untargeted metabolomics closer to the statistical rigor of genomics.”
MassID also reportedly expands the number of metabolites that can be identified and used for downstream analysis through a 280,000 metabolite database and advanced computational modeling, all executed with scalable cloud infrastructure. In a human plasma dataset, MassID structurally identified more than 4,500 metabolites, including over 1,200 compounds identified with >95% confidence.
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