MetFrag relaunched: incorporating strategies beyond in silico fragmentation

By Christoph Ruttkies, Emma L. Schymanski, Sebastian Wolf, Juliane Hollender, Steffen Neumann. Journal of Cheminformatics, 2016

905 citations recorded by the source corpus.

BackgroundThe in silico fragmenter MetFrag, launched in 2010, was one of the first approaches combining compound database searching and fragmentation prediction for small molecule identification from tandem mass spectrometry data. Since then many new approaches have evolved, as has MetFrag itself. This article details the latest developments to MetFrag and its use in small molecule identification since the original publication.ResultsMetFrag has gone through algorithmic and scoring refinements. New features include the retrieval of reference, data source and patent information via ChemSpider and PubChem web services, as well as InChIKey filtering to reduce candidate redundancy due to stereoisomerism. Candidates can be filtered or scored differently based on criteria like occurence of certain elements and/or substructures prior to fragmentation, or presence in so-called “suspect lists”. Retention time information can now be calculated either within MetFrag with a sufficient amount of user-provided retention times, or incorporated separately as “user-defined scores” to be included in candidate ranking. The changes to MetFrag were evaluated on the original dataset as well as a dataset of 473 merged high resolution tandem mass spectra (HR-MS/MS) and compared with another open source in silico fragmenter, CFM-ID. Using HR-MS/MS information only, MetFrag2.2 and CFM-ID had 30 and 43 Top 1 ranks, respectively, using PubChem as a database. Including reference and retention information in MetFrag2.2 improved this to 420 and 336 Top 1 ranks with ChemSpider and PubChem (89 and 71 %), respectively, and even up to 343 Top 1 ranks (PubChem) when combining with CFM-ID. The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total. Further examples are given to demonstrate flexibility of the enhanced features.ConclusionsIn many cases additional information is available from the experimental context to add to small molecule identification, which is especially useful where the mass spectrum alone is not sufficient for candidate selection from a large number of candidates. The results achieved with MetFrag2.2 clearly show the benefit of considering this additional information. The new functions greatly enhance the chance of identification success and have been incorporated into a command line interface in a flexible way designed to be integrated into high throughput workflows. Feedback on the command line version of MetFrag2.2 available at http://c-ruttkies.github.io/MetFrag/ is welcome.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/2dd925fa1ff0637112a4ce2148d5674af1666ec3

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Citations in the corpus

The CompTox Chemistry Dashboard: a community data resource for environmental chemistry (2017, methodology): “A recent example is the use of data downloads from the dashboard (vide supra) used as a source of candidate structures and as a suspect list within MetFrag [143, 144].”
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, methodology): “MetFrag [92] is a combinatorial fragmenter that retrieves candidate structures from PubChem, ChemSpider, KEGG, and a few other more speciﬁc compound databases.”
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools (2019, background): “Indeed, since the molecular networking approach was proposed in 2012 [10], numerous complementary molecular mining workflows as well as annotation and classification tools have been introduced including SIRIUS [3], CSI:FingerID [4], MetFusion [11], MetFamily [12], and many others [1,2,7,8,13–22] and”