SPLASH, a hashed identifier for mass spectra

Wohlgemuth, Gert; Mehta, Sajjan S.; Mejia, Ramon Francisco Pacquiao; Neumann, Steffen; Pedrosa, Diego França; Pluskal, Tomáš; Schymanski, Emma L.; Willighagen, Egon; Wilson, Michael; Wishart, David S.; Arita, Masanaori; Dorrestein, Pieter C.; Bandeira, Nuno; Wang, Mingxun; Schulze, Tobias; Salek, Reza M.; Steinbeck, Christoph; Nainala, Venkata Chandrasekhar; Mistrik, Robert; Nishi­oka, Takaaki; Fiehn, Oliver

doi:10.1038/nbt.3689

Cited by 66 publications

(51 citation statements)

References 17 publications

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“…To obtain high-quality spectra, the data was cleaned and recalibrated to within 5 ppm using known subformula annotation [8], all other peaks without a valid subformula within 5 ppm of the recalibrated data were removed. All substances with double chromatographic peaks, different substances with identical spectra (detected via the SPectraL hASH (SPLASH) [9, 10]), MS/MS containing only one peak or with a maximum intensity below were excluded from the datasets. Substances that were measured multiple times (because they were present in more than one mix) in the same ionization mode were only included once, selected by higher intensity.…”

Section: Methodsmentioning

confidence: 99%

Critical Assessment of Small Molecule Identification 2016: automated methods

et al. 2017

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BackgroundThe fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest (www.casmi-contest.org) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification.ResultsThe Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in “Category 2: Best Automatic Structural Identification—In Silico Fragmentation Only”, won by Team Brouard with 41% challenge wins. The winner of “Category 3: Best Automatic Structural Identification—Full Information” was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways.ConclusionsThe improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for “known unknowns”. As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for “real life” annotations. The true “unknown unknowns” remain to be evaluated in future CASMI contests.Graphical abstract.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0207-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Critical Assessment of Small Molecule Identification 2016: automated methods

et al. 2017

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“…By introducing a binning system, it might be possible to allow low resolution instruments obtain similar performances like searching spectra from high‐resolution instrumentations . Recently, a new spectral identifier for mass spectra the SPLASH was developed . It allows for a one‐way encoding of a mass spectrum into a fixed‐length identifier and can be considered an analog of the InChIKey.…”

Section: Ms/ms Search Algorithms and Scoringmentioning

confidence: 99%

“…109 Recently, a new spectral identifier for mass spectra the SPLASH was developed. 110 It allows for a one-way encoding of a mass spectrum into a fixed-length KIND ET AL.…”

Section: Reports From Several Critical Assessment Of Small Moleculementioning

confidence: 99%

Identification of small molecules using accurate mass MS/MS search

Kind

Tsugawa

Čajka

et al. 2017

Mass Spectrometry Reviews

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Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

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“…Currently, users examine multiple databases when performing searches because the databases are not completely overlapping 25 .…”

Section: Metabolomics Activity Screeningmentioning

confidence: 99%

Metabolomics activity screening for identifying metabolites that modulate phenotype

et al. 2018

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Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype1–3. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell’s or an organism’s phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

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SPLASH, a hashed identifier for mass spectra

Cited by 66 publications

References 17 publications

Critical Assessment of Small Molecule Identification 2016: automated methods

Critical Assessment of Small Molecule Identification 2016: automated methods

Identification of small molecules using accurate mass MS/MS search

Metabolomics activity screening for identifying metabolites that modulate phenotype

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