Potential of metabolomics as a functional genomics tool

Bino, R.J.; Hall, Robert D.; Fiehn, Oliver; Kopka, Joachim; Saito, Kazuki; Draper, John; Nikolau, Basil J.; Mendes, Pedro; Roessner, Ute; Beale, Michael H.; Trethewey, Richard N.; Lange, B. Markus; Wurtele, Eve Syrkin; Sumner, Lloyd W.

doi:10.1016/j.tplants.2004.07.004

Cited by 695 publications

(460 citation statements)

References 54 publications

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“…2-D HPLC (2-DLC) has received considerable attention as the resolving power of 1-D HPLC is insufficient for many purposes [44], especially for many bioanalytical applications [45,46]. However, the primary impediment to the widespread use of 2-DLC is its long analysis time [46,47], making the technique nearly impractical for routine use. The significance of greatly increasing the peak capacity of separations by extending from one to two separation dimensions was discussed in detail over two decades ago by Giddings [48] and Guiochon and coworkers [49].…”

Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 99%

Practice and theory of high temperature liquid chromatography

McNeff¹,

Yan

Stoll

et al. 2007

J of Separation Science

115

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Review Practice and theory of high temperature liquid chromatographyHigh temperature liquid chromatography (HTLC) exists in a temperature region beyond ambient (ca. 408C) and below super critical temperatures. The promises of HTLC, such as increased analysis speed, enhanced separation productivity, "green" LC with pure water mobile phases coupled to universal FID detection, and fast analysis of complex samples by combination with fast 2-D techniques, have become an option for routine practice. The focus of this paper is to review the key developments that have made the application of HTLC a practical technique and draw attention to new developments in 2-D techniques that incorporate HTLC that offer an opportunity to vastly increase the usefulness of HPLC for the analysis of complex samples.

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Section: Increasing Peak Capacity By Using Htlcmentioning

confidence: 99%

Practice and theory of high temperature liquid chromatography

McNeff¹,

Yan

Stoll

et al. 2007

J of Separation Science

115

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show abstract

“…The systematic error in mass measurements using TDC-based TOF-MS can only be avoided by careful and time-consuming manual analysis, limiting the exact mass calculations to those chromatographic scans that display an analyte mass intensity similar to the lock-mass intensity. For instance, in untargeted LC/TOF-MS based metabolomics studies, mass signals identified as being significantly different between (groups of) samples are usually identified by manually retrieving the accurate masses and corresponding elemental compositions from the raw data (Bino et al 2004;von Roepenack-Lahaye et al 2004;Vorst et al 2005;Wilson et al 2005). Although dedicated software is available to align and compare high mass resolution LC/TOF-MS datasets, e.g.…”

Section: Introductionmentioning

confidence: 99%

Accurate mass error correction in liquid chromatography time-of-flight mass spectrometry based metabolomics

Mihaleva¹,

Vorst²,

Maliepaard

et al. 2008

Metabolomics

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Compound identification and annotation in (untargeted) metabolomics experiments based on accurate mass require the highest possible accuracy of the mass determination. Experimental LC/TOF-MS platforms equipped with a time-to-digital converter (TDC) give the best mass estimate for those mass signals with an intensity similar to that of the lock-mass used for internal calibration. However, they systematically underestimate the mass obtained at higher signal intensity and overestimate it at low signal intensities compared to that of the lock-mass. To compensate for these effects, specific tools are required for correction and automation of accurate mass calculations from LC/MS signals. Here, we present a computational procedure for the derivation of an intensity-dependent mass correction function. The chromatographic mass signals for a set of known compounds present in a large number of samples were reconstructed over consecutive scans for each sample. It was found that the mass error is a linear function of the logarithm of the signal intensity adjusted to the associated lock-mass intensity. When applied to all mass data points, the correction function reduced the mass error for the majority of the tested compounds to B1 ppm over a wide range of signal intensities. The mass correction function has been implemented in a Python 2.4 script, which accepts raw data in NetCDF format as input, corrects the detected masses and returns the corrected NetCDF files for subsequent (automated) processing, such as mass signal alignment and database searching.

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“…Subsequently, metabolomics approaches have been forwarded as a means to link the functional biochemical phenotype to other functional genomics data (Weckwerth and Fiehn, 2002;Sumner et al, 2003;Bino et al, 2004;Hall et al, 2005). Like transcriptomics and proteomics, metabolomics involves two main components: instrumental analysis (analytical) and data analysis (bioinformatics).…”

mentioning

confidence: 99%

A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles

Tikunov

Lommen²,

Vos³

et al. 2005

Plant Physiology

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462

412

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To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of .20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between-and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.

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Potential of metabolomics as a functional genomics tool

Cited by 695 publications

References 54 publications

Practice and theory of high temperature liquid chromatography

Practice and theory of high temperature liquid chromatography

Accurate mass error correction in liquid chromatography time-of-flight mass spectrometry based metabolomics

A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles

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