Deriving Accurate Lipid Classification based on Molecular Formula

Mitchell, Joshua M.; Moseley, Hunter N. B.

doi:10.1101/572883

“…SMIRFE only provides chemical identification to the level of molecular formula, representing the start of chemical identification in a manner that reduces assignment bias. A logical next step is to use the SMIRFE-derived molecular formula to infer the biochemical type of metabolite . Yet full molecular elucidation will require the use of orthogonal information like tandem MS and NMR.…”

Section: Discussionmentioning

confidence: 99%

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra

Mitchell

¹

,

Flight

²

,

Moseley

³

2019

Self Cite

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Improvements in Fourier transform mass spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have developed a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using detected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas; instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 Da was constructed, and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and 15 N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs, and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

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“…SMIRFE Limitations and Future Directions SMIRFE only provides chemical identification to the level of molecular formula, representing the start of chemical identification in a manner that reduces assignment bias. A logical next step is to use the SMIRFE-derived molecular formula to infer the biochemical type of metabolite 37 . But full molecular elucidation will require the use of orthogonal information like tandem MS and NMR.…”

Section: Implications For Experimental Designmentioning

confidence: 99%

Small Molecule Isotope Resolved Formula Enumeration: a Methodology for Assigning Isotopologues and Metabolites in Fourier Transform Mass Spectra

Moseley¹,

Mitchell²,

Flight³

2019

Preprint

Self Cite

0

View full text Add to dashboard Cite

Improvements in Fourier Transform Mass Spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically-introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have devel-oped a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using de-tected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas, instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 daltons was constructed and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and 15N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

show abstract

“…SMIRFE Limitations and Future Directions SMIRFE only provides chemical identification to the level of molecular formula, representing the start of chemical identification in a manner that reduces assignment bias. A logical next step is to use the SMIRFE-derived molecular formula to infer the biochemical type of metabolite 36 . But full molecular elucidation will require the use of orthogonal information like tandem MS and NMR.…”

Section: Implications For Experimental Designmentioning

confidence: 99%

Small Molecule Isotope Resolved Formula Enumeration: a Methodology for Assigning Isotopologues and Metabolites in Fourier Transform Mass Spectra

Moseley¹,

Mitchell²,

Flight³

2019

Preprint

Self Cite

0

View full text Add to dashboard Cite

Improvements in Fourier Transform Mass Spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically-introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have devel-oped a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using de-tected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas, instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 daltons was constructed and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and 15N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

show abstract

Deriving Accurate Lipid Classification based on Molecular Formula

Cited by 3 publications

References 49 publications

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra

Small Molecule Isotope Resolved Formula Enumeration: a Methodology for Assigning Isotopologues and Metabolites in Fourier Transform Mass Spectra

Small Molecule Isotope Resolved Formula Enumeration: a Methodology for Assigning Isotopologues and Metabolites in Fourier Transform Mass Spectra

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