X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry

Mylonas, Roman; Mauron, Yves; Masselot, Alexandre; Binz, Pierre‐Alain; Budin, Nicolas; Fathi, Marc; Viette, Véronique; Hochstrasser, Denis F.; Lisacek, Frédérique

doi:10.1021/ac900954d

Cited by 73 publications

(63 citation statements)

References 20 publications

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“…For instance, a newly presented ''matching probability'' (mp) uses a high weight for the presence and absence of peaks (raised to the fourth power!) and a low weight for the intensity differences [64]: Another algorithm, X-Rank [65], ranks the peaks of the mass spectra by intensity and matches the rank pattern of the reference spectrum with the rank pattern of the unknown. The probability of a correct identification is computed for this match using parameters that have been trained before.…”

Section: Search Algorithms For Ms/ms Spectramentioning

confidence: 99%

Mass Spectrometry

Werther¹

2014

Handbook of Spectroscopy

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Section: Search Algorithms For Ms/ms Spectramentioning

confidence: 99%

Mass Spectrometry

Werther¹

2014

Handbook of Spectroscopy

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“…Such platforms rely on sophisticated software algorithms and can be used to predict the elemental formula of unknown compounds based on a single accurate mass measurement. However, some limitations of this technique must be taken into account (38,51,52 ), and fragment ions must be recorded to allow unequivocal analyte identification (53 ). Some LC-MS hyphenations offer such exact mass determination (50 ) [e.g., those that are equipped with time-of flight (54 ), Fourier transform ion cyclotron resonance (55,56 ), or orbi-trap (57,58 ) analyzers], but these are still rarely used in clinical laboratories.…”

Section: Pitfalls Of Lc-ms/msmentioning

confidence: 99%

Pitfalls Associated with the Use of Liquid Chromatography–Tandem Mass Spectrometry in the Clinical Laboratory

2010

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BACKGROUND Novel mass spectrometric techniques such as atmospheric pressure ionization and tandem mass spectrometry have substantially extended the spectrum of clinical chemistry methods during the past decade. In particular, liquid chromatography tandem–mass spectrometry (LC-MS/MS) has become a standard tool in research laboratories as well as in many clinical laboratories. Although LC-MS/MS has features that suggest it has a very high analytical accuracy, potential sources of inaccuracy have recently been identified. CONTENT The sources of inaccuracy in LC-MS/MS methods used in the routine quantification of small molecules are described and discussed. Inaccuracy of LC-MS/MS methods can be related to the process of ionization through the insource transformation of conjugate metabolites or target analytes and may also be attributable to ionization matrix effects that have a differential impact on target analytes and internal-standard compounds. Inaccuracy can also be associated with the process of ion selection, which mainly occurs when compounds from the sample matrix share mass transitions with a target analyte. In individual assays, most potential sources of inaccuracy can be controlled by sufficient LC separation–based sample workup before MS analysis. SUMMARY LC-MS/MS methods should undergo rigorous and systematic validation before introduction into patient care.

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“…The third component is a customized algorithm for database searching because the nature of MS/MS data is distinct from that of GC -MS data. A probability-based algorithm is essential for the annotation of large-scale metabolome data [66,72]. The last piece of infrastructure required is a metabolite ontology system for processing incomplete annotated data [73].…”

Section: Perspectivesmentioning

confidence: 99%

Integrative Analysis of Secondary Metabolism and Transcript Regulation in Arabidopsis Thaliana

Mizutani

Saito

2013

The Handbook of Plant Metabolomics

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Metabolic states in plants are dynamically controlled via transcriptional regulation in response to environmental and developmental conditions. However, the regulatory mechanisms between gene expression (input) and the resultant metabolite accumulation (output) remain unclear because complex post-transcriptional events such as feedback regulation and inter-tissue translocation often play important roles in these mechanisms [1]. Hence detailed investigations of the dynamic behavior of metabolic systems and an understanding of their general rules are a major challenge for plant systems biology [2,3]. One promising strategy is a global survey of input (gene expression) and output (metabolite accumulation) signals to estimate the mechanisms working within these systems [4]. In this regard, recent advances in analytical and informatics technologies enable us to perform integrated analyses of transcriptome and metabolome data while considering metabolic pathway information [5].The pioneering applications of this strategy involved the investigation of the reprogramming of gene expression and metabolism triggered by nutritional stresses such as sulfur starvation [6][7][8][9]. This research demonstrated that two main types of information can be derived from integrated analysis [10]. The first outcome of such studies was the discovery of a gene-to-metabolite network regulating plant metabolism during environmental stresses. Integrated analysis of time-course data showed that groups of metabolites and genes related to primary and secondary metabolites are coordinately modulated by sulfur deficiency-induced stress [8,[11][12][13][14]. A similar analysis was performed for various plants such as cold-acclimating Arabidopsis, pathogen-infected Medicago, fruit ripening tomato, and metabolically engineered rice [15][16][17][18][19][20]. Another outcome was the successful prediction of novel gene function by using the rules underlying gene expressions and metabolite accumulation. Integrated analysis allowed the prediction of genes involved in glucosinolate biosynthesis (e.g., genes encoding sulfotransferases [21], 2 MYB transcription factors [9], and chain elongation enzymes [22,23]) in a comprehensive manner. The strategy was also used in nonmodel plants [24,25].

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X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry

Cited by 73 publications

References 20 publications

Mass Spectrometry

Mass Spectrometry

Pitfalls Associated with the Use of Liquid Chromatography–Tandem Mass Spectrometry in the Clinical Laboratory

Integrative Analysis of Secondary Metabolism and Transcript Regulation in Arabidopsis Thaliana

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