MIDAS: A Database-Searching Algorithm for Metabolite Identification in Metabolomics

Wang, Yingfeng; Kora, Guruprasad; Bowen, Benjamin P.; Pan, Chongle

doi:10.1021/ac5014783

Cited by 100 publications

(86 citation statements)

References 30 publications

(51 reference statements)

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“…We evaluate our method against the original FingerID method (18), CFM-ID (10), MAGMa (16), MIDAS (15), and MetFrag (14). FingerID was retrained on the combined training data, to enable a sensible evaluation against its successor presented here.…”

Section: Methodsmentioning

confidence: 99%

“…Particular progress has been made for restricted metabolite classes such as lipids (5), but as with peptides, results cannot be generalized to other metabolite classes. For the general case, several strategies have been proposed during recent years, including simulation of mass spectra from molecular structure (10,11), combinatorial fragmentation (12)(13)(14)(15)(16)(17), and prediction of molecular fingerprints (18,19).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop

Shen

Meusel

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

737

788

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Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.mass spectrometry | small compound identification | metabolomics | bioinformatics | machine learning M etabolites, small molecules that are involved in cellular reactions, can provide detailed information about cellular state. Untargeted metabolomic studies may use NMR or MS technologies, but liquid chromatography followed by MS (LC/MS) can detect the highest number of metabolites from minimal amounts of sample (1, 2). Untargeted metabolomics comprehensively compares the mass spectral intensities of metabolite signals (peaks) between two or more samples (3, 4). Advances in MS instrumentation allow us to simultaneously detect thousands of metabolites in a biological sample. Identification of these compounds relies on tandem MS (MS/MS) data, produced by fragmenting the compound and recording the masses of the fragments. Structural elucidation remains a challenging problem, in particular for compounds that cannot be found in any spectral library (1): In total, all available spectral MS/MS libraries of pure chemical standards cover fewer than 20,000 compounds (5). Growth of spectral libraries is limited by the unavailability of pure reference standards for many compounds.In contrast, molecular structure databases such as PubChem (6) and ChemSpider (7) contain millions of compounds, with PubChem alone having surpassed 50 million entries. Searching in molecular structure databases using MS/MS data is therefore considered a powerful tool for assisting an expert in the elucidation of a compound. This problem is considerably harder than the fundamental analysis step in the shotgun proteomics workflow, namely, searching peptide MS/MS data in a peptide sequence database (8): Unlike proteins and peptides, metabolites show a large structural variability and, consequently, also large variations in MS/MS fragmentation. Computational approaches for interpreting and predicting MS/MS data of small molecules date back to the 1960s (9): Due to the unavailability of molecular structure databases at that time, structure libraries were combinatorially generated and then "searched" using the experimental MS/MS data. "Modern" methods for this question have been developed since mid-2000. Particular progress has been made for restricted metabolite cl...

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop

Shen

Meusel

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

737

788

View full text Add to dashboard Cite

show abstract

“…Key articles that discuss several approaches for structure elucidation of both polar and non-polar molecules are suggested here (Scheubert et al, 2013, Hufsky et al, 2014). A popular approach is the combined use of an in silico fragmentation algorithm coupled with a mass spectral database search such as MetFrag combined with Mass-Bank (Wolf et al, 2010), Metabolite Identification via Database Searching (MIDAS) (Wang et al, 2014b) and High-throughput AutoMation of Mass Frontier (HAMMER) (Zhou et al, 2014). Example biomonitoring studies that utilized HRMS and in silico tools for structure confirmation are the analysis of environmental chemicals in breast milk (Baduel et al, 2015) and non-targeted and unknown perfluoroalkyl compounds in serum (Rotander et al, 2015).…”

Section: High Resolution Data Extraction Features For Scaling the mentioning

confidence: 99%

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Andra

Austin

Patel

et al. 2017

Environment International

119

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Global profiling of xenobiotics in human matrices in an untargeted mode is gaining attention for studying the environmental chemical space of the human exposome. Defined as the study of a comprehensive inclusion of environmental influences and associated biological responses, human exposome science is currently evolving out of the metabolomics science. In analogy to the latter, the development and applications of high resolution mass spectrometry (HRMS) has shown potential and promise to greatly expand our ability to capture the broad spectrum of environmental chemicals in exposome studies. HRMS can perform both untargeted and targeted analysis because of its capability of full- and/or tandem-mass spectrum acquisition at high mass accuracy with good sensitivity. The collected data from target, suspect and non-target screening can be used not only for the identification of environmental chemical contaminants in human matrices prospectively but also retrospectively. This review covers recent trends and advances in this field. We focus on advances and applications of HRMS in human biomonitoring studies, and data acquisition and mining. The acquired insights provide stepping stones to improve understanding of the human exposome by applying HRMS, and the challenges and prospects for future research.

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“…Systematic bond dissociation is also implemented in MIDAS ( M etabolite I dentification via Da tabase S earching) software [78]. …”

Section: Computational Tools For Msn and Fragmentation Treesmentioning

confidence: 99%

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Vaniya

Fiehn

2015

TrAC Trends in Analytical Chemistry

118

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Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

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MIDAS: A Database-Searching Algorithm for Metabolite Identification in Metabolomics

Cited by 100 publications

References 30 publications

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

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