CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra

Allen, Felicity; Pon, Allison; Wilson, Michael; Greiner, Russell; Wishart, David S.

doi:10.1093/nar/gku436

Cited by 393 publications

(421 citation statements)

References 23 publications

(40 reference statements)

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“…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%

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

Dührkop

Shen

Meusel

et al. 2015

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

797

829

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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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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.

797

829

View full text Add to dashboard Cite

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“…Limited metabolite coverage is obtained when using data-dependent modes since only intense ions are fragmented and many artifact ions will be detected and fragmented due to the inherent presence of isotopes, in-source fragments or high abundance ions from contamination or chemical noise [53]. Resources to annotate MS/MS spectra by comparison with experimental [53] or in-silico spectral libraries have been proposed, including MolFind [54], MetFrag [60], MetFusion [55], MyCompoundID [56, 57], CFM-ID [58] and MS-FINDER [59]. Alternatively, data-independent acquisition (DIA) modes such as MS E [62] or SWATH [63], in which all fragment ions for all precursors are simultaneously acquired, allows an increased MS/MS spectral coverage and reinforces the annotation confidence [64].…”

Section: Computational Annotation Strategiesmentioning

confidence: 99%

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

et al. 2017

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Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.

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“…Similarly, CFM-ID uses competitive fragment modeling to produce a probabilistic generative model for the MS/MS fragmentation process and provides efficient identification of metabolites in electrospray tandem mass spectrometry (ESI-MS/MS) generated spectra. Users can accurately identify compounds, assign peaks and predict spectra shown in LC-MS/MS data (Allen et al 2014). MassBank was developed as first public repository of mass spectral data for chemical identification and structural elucidation of chemical compounds detected by mass spectrometry (Horai et al 2010).…”

Section: New Techniques and Tools Helped To Understand Complex Chemicmentioning

confidence: 99%

A decade of understanding secondary metabolism in Pseudomonas spp. for sustainable agriculture and pharmaceutical applications

Shahid

Malik

Mehnaz

2018

Environmental Sustainability

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Pseudomonas spp. have been widely studied for their plant growth promoting and antimicrobial metabolites. The genus got attention due to the production of array of secondary metabolites involved in the suppression of phytopathogens and ability to stimulate plant growth by means of nitrogen-fixation, production of hydrolytic enzymes, regulatory hormones, and solubilization of inorganic minerals. In recent years, research was focused towards identification of biosynthesis pathways and genes involved in the production of secondary metabolites that led to the discovery of novel metabolites including many new phenazine derivatives, quorum-sensing signals, rhizoxin analogues, cyclic lipopeptides, and a new class of alkylsubstituted aromatic acids. Identification of these biosynthetic pathways provided insights for their successful application in agriculture and for environmental sustainability. In addition, many genomic and metabolomic databases such as; METLIN, KEGG, GNPS, CFM-ID, MassBank, and MetaboLights, allowed exploring intricate metabolic pathways and significant genes involved in the biosynthesis of compounds. Several softwares, genome-mining tools and new techniques, such as MALDI-IMS and MALDI-FTICR MS were developed to facilitate the characterization of new metabolites. Additionally, use of MALDI-imaging techniques facilitated real-time visualization of complex microbial communities and their relationship with pathogens. Secondary metabolites of Pseudomonas spp. were also demonstrated for their apoptotic, anti-mitotic, nematocidal, herbicidal, anthelmintic, insecticidal, and phytotoxic effects. Total biosynthesis of metabolic derivatives and genetic engineering enabled to develop strains with improved yield of targeted bio-products. Availability and access to published genomic sequences and comparative bioinformatics tools helped in identification of strain-specific traits and development of multifunctional inocula. This review highlights significant advances in identification of Pseudomonas secondary metabolites for their successful agricultural and pharmaceutical applications.

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CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra

Cited by 393 publications

References 23 publications

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

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

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

A decade of understanding secondary metabolism in Pseudomonas spp. for sustainable agriculture and pharmaceutical applications

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