Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

Benton, H. Paul; Ivanišević, Julijana; Mahieu, Nathaniel G.; Kurczy, Michael E.; Johnson, Caroline H.; Franco, Lauren; Rinehart, Duane; Valentine, Elizabeth R.; Gowda, Harsha; Ubhi, Baljit K.; Tautenhahn, Ralf; Gieschen, Andrew; Fields, Matthew W.; Patti, Gary J.; Siuzdak, Gary

doi:10.1021/ac5025649

Cited by 169 publications

(147 citation statements)

References 23 publications

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“…Multiple bioinformatic strategies which are interoperable with the XCMS program have now emerged that enable identification of adducts, isotopes, artifacts, and in some cases even structures. [24] A selection of these resources is detailed in the sections that follow.…”

Section: Introducing Xcmsmentioning

confidence: 99%

A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

113

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Global profiling of metabolites in biological samples by liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental design. Most metabolomic studies a decade ago had a relatively simple experimental design in which the intensities of compounds were compared between only two sample groups. More recently, however, increasingly sophisticated applications have been pursued. Examples include comparing compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications have necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the “XCMS family” of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, X13CMS, and XCMS Online. Together, these informatic technologies can accommodate most cutting-edge metabolomic applications and offer some advantages when compared to the original XCMS program.

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Section: Introducing Xcmsmentioning

confidence: 99%

A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

113

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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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“…A contemporary metabolomics experiment incorporating MS analysis will typically detect on the order of several thousand metabolites representing a diverse array of chemical classes (139,140,141). Because of limited time and information, the majority of these detected metabolites will only be characterized by a few nonspecific descriptors originating from the multidimensional experiment, such as retention time, signal intensity, and molecular mass (142).…”

Section: Novel Strategies For Visualizing Big Datamentioning

confidence: 99%

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

May

McLean

2016

Annual Rev. Anal. Chem.

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Hybrid analytical instrumentation constructed around mass spectrometry (MS) are becoming preferred techniques for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements which are used to infer systems-level information. In this review, we describe multidimensional MS configurations as technologies which are big data drivers and discuss some new and emerging strategies for mining information from large-scale datasets. A discussion is included on the information content which can be obtained from individual dimensions, as well as the unique information which can be derived by comparing different levels of data. Finally, we discuss some emerging data visualization strategies which seek to make highly dimensional datasets both accessible and comprehensible.

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Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

Cited by 169 publications

References 23 publications

A roadmap for the XCMS family of software solutions in metabolomics

A roadmap for the XCMS family of software solutions in metabolomics

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

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