Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery

Coble, Jamie B.; Fraga, Carlos G.

doi:10.1016/j.chroma.2014.06.100

Cited by 86 publications

(65 citation statements)

References 17 publications

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“…However, there is no currently available standardized soware/website for MS data processing and analysis. Using different soware, the results will be very different, [153][154][155] which shows that data processing and analysis soware/ websites have a great inuence on data processing. Data processing is a vital part of metabolomics research and each soware/website has its own characteristics.…”

Section: Tools For Ms Results Analysismentioning

confidence: 99%

Advances in mass spectrometry-based metabolomics for investigation of metabolites

et al. 2018

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Metabolomics is the systematic study of all the metabolites present within a biological system, which consists of a mass of molecules, having a variety of physical and chemical properties and existing over an extensive dynamic range in biological samples. Diverse analytical techniques are needed to achieve higher coverage of metabolites. The application of mass spectrometry (MS) in metabolomics has increased exponentially since the discovery and development of electrospray ionization and matrixassisted laser desorption ionization techniques. Significant advances have also occurred in separationbased MS techniques (gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and ion mobility-mass spectrometry), as well as separation-free MS techniques (direct infusion-mass spectrometry, matrix-assisted laser desorption ionization-mass spectrometry, mass spectrometry imaging, and direct analysis in real time mass spectrometry) in the past decades. This review presents a brief overview of the recent advanced MS techniques and their latest applications in metabolomics. The software/websites for MS result analyses are also reviewed.

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Section: Tools For Ms Results Analysismentioning

confidence: 99%

Advances in mass spectrometry-based metabolomics for investigation of metabolites

et al. 2018

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“…In a recent study, four automated preprocessing tools for GC-MS and LC-MS data were evaluated in terms of their ability to discover the greatest number of metabolites consistently in a set of samples, as well as the robustness of the methods [71]. The preprocessing tools selected were MetAlign [72], MZmine [73], XCMS [74], and SpectConnect [75].…”

Section: Data Processing and Identificationmentioning

confidence: 99%

Optimizing the lipidomics workflow for clinical studies—practical considerations

Hyötyläinen

Orešič

2015

Anal Bioanal Chem

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Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.

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“…XCMS package is the first cited software useful for pre-processing metabolic data, which incorporates matched filtration, peak detection, retention time alignment, and peak matching (Coble et al, 2014). LC-MS feature detection can be done with an original implemented algorithm Filter and a new developed algorithm named CentWave (Tautenhahn et al, 2008).…”

Section: Mzminementioning

confidence: 99%

“…MetAlign combines direct conversion to and from manufacturer formats, as well as netCDF, baseline correction, de-noising, accurate mass calculation, alignment, export of univariate statistical selections to differential MS data file, export to spreadsheets for multivariate statistical analysis and conversion of a multivariate statistical selection to a MS data file, being used and reported by many metabolomics publications, since 1995 to 2013 as the third cited pre-processing tool searched in SciFinder (www.cas.org/products/scifinder) and Web of Science (www.thomsonreuters.com/ web-of-science) (Coble et al,2014). MetAlign 3.0 is a new open source version which can be downloaded from the official site (www.metalign.…”

Section: Data Processingmentioning

confidence: 99%

Bioinformatics Tools for Metabolomic Data Processing and Analysis Using Untargeted Liquid Chromatography Coupled With Mass Spectrometry

Lazar¹,

Romanciuc²,

Socaciu³

et al. 2015

BUASVMCN-ASB

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Metabolomics is an important "omics" technology, complementary to genomics and proteomics, as parts of systems biology, giving information (qualitative fingerprints and quantitative profiling) as a mirror of cell and extracellular metabolic activity. A cohort of small metabolites are involved in the control and regulation of cellular functions, as intermediates or final products, their presence or levels being useful for the early diagnosis of different pathologies. Bioinformatics tools are mandatory for a future "computational" metabolomics, needed to manage large number of experimentally acquired data obtained from biological samples (plants, animal or human tissues). This review presents updated information about different high-throughput analytical techniques and data acquisition software (1-2), the pre-processing of data, converted to specific matrices, further processed by specific normalization and alignment procedures (3), then analysed by statistical univariate and multivariate chemometric and /or statistical techniques (4), identifying biomarkers by comparison with databases (5), and finally elucidating the networks and pathways (6). New software is available for data conversion, pre-processing, alignment algorithms, bucketing, normalization, underlying the challenges and comparisons with international data bases. Finally, the accurate identification of individual molecules as biomarkers, either evaluated by untargeted metabolomics techniques (Principal Component Analysis -PCA), Cluster Analysis -CA) or supervised ones (Partial Least Square Discriminant Analysis (PLS-DA) is presented. The accurate identification of metabolites and their involvement in metabolic networks and pathways became possible by well-established databases (HMDB, LIPID MAPS, KEGG, etc.), to validate all experimental data. Bioinformatics is a sine-qua-non tool, to be used and valorised by untargeted or targeted metabolomics, as an integrated technology in systems biology.

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Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery

Cited by 86 publications

References 17 publications

Advances in mass spectrometry-based metabolomics for investigation of metabolites

Advances in mass spectrometry-based metabolomics for investigation of metabolites

Optimizing the lipidomics workflow for clinical studies—practical considerations

Bioinformatics Tools for Metabolomic Data Processing and Analysis Using Untargeted Liquid Chromatography Coupled With Mass Spectrometry

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