ArticleAnalytical and bioanalytical chemistry2020
In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples.
Article in Analytical and bioanalytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- Large-scale generation of in silico based spectral libraries to annotate dark chemical space features in non-target analysis.Analytical and bioanalytical chemistry · 2025Article
- Reporting Chemical Data in the Environmental Sciences.ACS environmental Au · 2025Review
- Prioritizing Chemical Candidates from Non-targeted Analysis Using Metadata, Spectral Similarity, and Hazard Scoring within INTERPRET NTA.Analytical chemistry · 2025Article
- Automated QA/QC reporting for non-targeted analysis: a demonstration of "INTERPRET NTA" with de facto water reuse data.Analytical and bioanalytical chemistry · 2025Article
- Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies.Analytical chemistry · 2025Review
- Galaxy QCxMS for straightforward semi-empirical quantum mechanical EI-MS prediction.GigaByte (Hong Kong, China) · 2025Article
- Development and application of a non-targeted analysis method using GC-MS and LC-MS for identifying chemical contaminants in drinking water via point-of-use filters.Microchemical journal : devoted to the application of microtechniques in all branches of science · 2024Article
- Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.bioRxiv : the preprint server for biology · 2024Article
- Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics.Mass spectrometry (Tokyo, Japan) · 2024Article
- Adding open spectral data to MassBank and PubChem using open source tools to support non-targeted exposomics of mixtures.Environmental science. Processes & impacts · 2023Article
- Identifying xenobiotic metabolites withFrontiers in toxicology · 2023Article
- Wikipedia on the CompTox Chemicals Dashboard: Connecting Resources to Enrich Public Chemical Data.Journal of chemical information and modeling · 2022Article
- How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.Journal of chemical information and modeling · 2022Article
- Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia.Environment international · 2022Article
- FindPFΔS: Non-Target Screening for PFAS─Comprehensive Data Mining for MSAnalytical chemistry · 2022Article
- Recent advances in proteomics and metabolomics in plants.Molecular horticulture · 2022Review
- A Framework for Utilizing High-Resolution Mass Spectrometry and Nontargeted Analysis in Rapid Response and Emergency Situations.Environmental toxicology and chemistry · 2022Article
- Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data.Metabolites · 2022Article
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Authors and funding
11 authors.
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Abstract
High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.
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