mSpecs: a software tool for the administration and editing of mass spectral libraries in the field of metabolomics

Thielen, Bernhard; Heinen, Stephanie; Schomburg, Dietmar

doi:10.1186/1471-2105-10-229

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…As NIST libraries were collected from various sources, spectra originated from a range of different conditions and thus, reported retention indices could only be used with great caution. Consequently, various laboratories started collecting metabolite spectra under standardized conditions into customized and lab-specific libraries and tools (Hummel et al, 2007;Hiller et al, 2009;Thielen et al, 2009), some of which were publicly available while others were made available under a license fee (Kind et al, 2009b). Such libraries could also incorporate spectra of unknown compounds, for example from deconvoluted spectra of plant GC-MS profiles.…”

Section: Gas Chromatography -Mass Spectrometry (Gc-ms)mentioning

confidence: 99%

Data Processing, Metabolomic Databases and Pathway Analysis

Fiehn

Kind

Barupal

2011

Annual Plant Reviews Volume 43

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Metabolomics relies on a variety of bioinformatics tools aiming to derive information from data. Data acquisition with chromatography-coupled mass spectrometry yields high volumes of raw data that need to be processed to achieve de-noised and meaningful biochemical data that subsequently can be presented for statistics and pathway analysis in a coherent manner. A variety of new tools and algorithms have recently been developed that help in this process. Nevertheless, a surprisingly low number of metabolic signals can be unambiguously identified. If cutting-edge methods are used, we can confidently interpret signals that can neither be identified by reference standards nor annotated by database matching as 'novel metabolites'. Such methods may comprise high-resolution, high-accurate mass data in conjunction with good data alignment programs that perform peak picking and deconvolution, calculation of elemental formulas, database queries and constraining hit lists by interpreting mass spectral fragmentations and retention-based metabolomic libraries. In an effort to improve standardizations for metabolomic reports, we propose that not only metabolites must be named but also, for all reported metabolites, identifiers or structure codes (InChI keys) from public (bio)chemical databases need to be detailed. Machine-readable structures will vastly accelerate our knowledge of the magnitude and importance of the metabolome in various plant species, organs and physiological conditions. We detail how well-annotated metabolome data can then be used to visualize and interrogate biochemical pathways by matching information to the broad plant databases that currently exist.

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Section: Gas Chromatography -Mass Spectrometry (Gc-ms)mentioning

confidence: 99%

Data Processing, Metabolomic Databases and Pathway Analysis

Fiehn

Kind

Barupal

2011

Annual Plant Reviews Volume 43

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“…Following acquisition of the raw data from the instruments, a suite of software methods (3,(8)(9)(10)(11)(12)(13)(14) automatically integrates each ion across retention time and then uses that ionic information, which may include additional MS/MS fragmentation information and retention time, to identify the compound. After a compound is identified in a sample, one of the characteristic and stronger ions is used to determine a relative concentration of that compound in each sample.…”

Section: Introduction To Global Metabolomicsmentioning

confidence: 99%

Application of Metabolomics to Diagnosis of Insulin Resistance

Milburn

Lawton²

2013

Annu. Rev. Med.

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Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

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“…While various techniques are available for analyzing this type of data (Bryan, Brennan et al 2008;Scalbert, Brennan et al 2009;Thielen, Heinen et al 2009;Xia, Psychogios et al 2009), the fundamental goal of the analysis is the same -to quickly and accurately identify detected molecules so that biological mechanisms and modes of action can be understood. Metabolomics analysis was long thought of as, and in many aspects still is, an instrumentation problem; the better and more accurate the instrumentation (LC/MS, GC/MS, NMR, CE, etc.)…”

Section: Introductionmentioning

confidence: 99%