Winners of CASMI2013: Automated Tools and Challenge Data

Nishi­oka, Takaaki; Kasama, Takeshi; Kinumi, Tomoya; Makabe, Hidefumi; Mizutani, Fumio; Miura, Daisuke; Miyashita, Masahiro; Nakamura, Takemichi; Tanaka, Ken; Yamamoto, A.

doi:10.5702/massspectrometry.s0039

Cited by 28 publications

(29 citation statements)

References 32 publications

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“…More details on the measurements and the additional information are available elsewhere. 5,6) A degree of pre-processing was necessary to apply a consistent work ow on the les, while di erent parameters were necessary for the di erent challenges to take full advantage of highly accurate data when it was available. As neither MOLGEN-MS/MS nor MetFrag/MetFusion account for isotope patterns in the MS/MS, unequivocally identi able isotope peaks were removed from the MS/MS les (Challenges 1 & 2, 4-6, 9, 11, 14-16) to avoid incorrect formula/fragment assignment to isotope peaks, especially problematic for MS/ MS where e.g.…”

Section: Methodsmentioning

confidence: 99%

“…us, the automatic database joining avoided the coverage problem experienced by participants who relied on PubChem alone, and approached the manual strategy to include many data sources applied by the winners. 5,6) In addition to the contest submissions, the results that would have been achieved with either MetFrag or MetFusion alone on the individual databases are shown in the remaining columns of Table 2, as well as additional rows for Challenges 15 & 16 with an exact mass search and no element restrictions, to demonstrate what could have been achieved for these challenges. As expected, MetFusion outperformed MetFrag, with better or similar ranks for all challenges except Challenges 4 and 7.…”

Section: Categorymentioning

confidence: 99%

“…e results with MOLGEN-MS/ MS were generally good (rank 1 or 2) for compounds with small m/z precursors, but no longer competitive for larger m/z values where other CASMI participants with di erent approaches outperformed MOLGEN-MS/MS. 5,6) e winner of Category 1 ranked the correct formula in rst place for all challenges.…”

Section: Categorymentioning

confidence: 99%

“…1) e CASMI 2013 contest led by Prof. Takaaki Nishioka included generally high resolution tandem mass spectra and fragmentation information for 16 challenge compounds, together with some biochemical background information. 5,6) Figure 1 contains the structure and name of each challenge. e spectra were acquired on a variety of instruments and matrices, with varying accuracy and additional information (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Following the evaluation of the participant submissions and release of the challenge solutions, the organisers also declared some "highly commended" structures that they considered as being likely to produce spectral data very similar to the actual challenge data. 5,6) In light of the 2012 CASMI results and the information provided for the 2013 contest, preliminary decisions were made on the submission strategy. MOLGEN-MS/MS performed well for Category 1 (molecular formula prediction) in CASMI 2012 with a simple enumeration approach.…”

Section: Introductionmentioning

confidence: 99%

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Solving CASMI 2013 with MetFrag, MetFusion and MOLGEN-MS/MS

et al. 2014

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e second Critical Assessment of Small Molecule Identi cation (CASMI) contest took place in 2013. A joint team from the Swiss Federal Institute of Aquatic Science and Technology (Eawag) and Leibniz Institute of Plant Biochemistry (IPB) participated in CASMI 2013 with an automatic work ow-style entry. MOLGEN-MS/MS was used for Category 1, molecular formula calculation, restricted by the information given for each challenge. MetFrag and MetFusion were used for Category 2, structure identi cation, retrieving candidates from the compound databases KEGG, PubChem and ChemSpider and joining these lists pre-submission. e results from Category 1 were used to guide whether formula or exact mass searches were performed for Category 2. e Category 2 results were impressive considering the database size and automated regime used, although these could not compete with the manual approach of the contest winner.e Category 1 results were a ected by large m/z and ppm values in the challenge data, where strategies beyond pure enumeration from other participants were more successful. However, the combination used for the CASMI 2013 entries was extremely useful for developing decision-making criteria for automatic, high throughput general unknown (non-target) identi cation and for future contests.

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Section: Methodsmentioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving CASMI 2013 with MetFrag, MetFusion and MOLGEN-MS/MS

et al. 2014

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Metabolomics‐based approach for ranking the candidate structures of unidentified peaks in capillary electrophoresis time‐of‐flight mass spectrometry

Yamamoto¹,

Sasaki²

2017

Electrophoresis

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The cover picture shows a concept of our computational procedure. The first step involves searching "precursor/product metabolites" (the arrow in the left side). In the second step, candidate structures for the unidentified peak are searched against the database using a molecular formula (the arrow in the right side). These structures are then ranked by structural similarity against precursor/product metabolites and candidate structures. Finally, we can identify the structure of the unidentified peak (the robot arm in the center). C 977 Modular fluidic resistors to enable widely tunable flow rate and fluidic switching period in a microfluidic oscillator

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Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

Hufsky

Böcker

2016

Mass Spectrometry Reviews

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Mass spectrometry (MS) is a key technology for the analysis of small molecules. For the identification and structural elucidation of novel molecules, new approaches beyond straightforward spectral comparison are required. In this review, we will cover computational methods that help with the identification of small molecules by analyzing fragmentation MS data. We focus on the four main approaches to mine a database of metabolite structures, that is rule-based fragmentation spectrum prediction, combinatorial fragmentation, competitive fragmentation modeling, and molecular fingerprint prediction.

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Winners of CASMI2013: Automated Tools and Challenge Data

Cited by 28 publications

References 32 publications

Solving CASMI 2013 with MetFrag, MetFusion and MOLGEN-MS/MS

Solving CASMI 2013 with MetFrag, MetFusion and MOLGEN-MS/MS

Metabolomics‐based approach for ranking the candidate structures of unidentified peaks in capillary electrophoresis time‐of‐flight mass spectrometry

Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

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