MeltDB 2.0–advances of the metabolomics software system

Kessler, Nikolas; Neuweger, Heiko; Bonte, Anja; Langenkämper, Georg; Niehaus, Karsten; Nattkemper, Tim Wilhelm; Goesmann, Alexander

doi:10.1093/bioinformatics/btt414

Cited by 78 publications

(55 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clustering/classification options include, but are not limited to, random forest, K -means clustering, and hierarchical clustering. Additional data analysis/identification pro-grams include the web-based MeltDB [26], commercial SIMCA-P software [27], and commercial SAS software.…”

Section: Notesmentioning

confidence: 99%

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Ulmer

Patterson

Koelmel

et al. 2017

Methods in Molecular Biology

View full text Add to dashboard Cite

Lipids have been analyzed in applications including drug discovery, disease etiology elucidation, and natural products. The chemical and structural diversity of lipids requires a tailored lipidomics workflow for each sample type. Therefore, every protocol in the lipidomics workflow, especially those involving sample preparation, should be optimized to avoid the introduction of bias. The coupling of ultra-high-performance liquid chromatography (UHPLC) with high-resolution mass spectrometry (HRMS) allows for the separation and identification of lipids based on class and fatty acid acyl chain. This work provides a comprehensive untargeted lipidomics workflow that was optimized for various sample types (mammalian cells, plasma, and tissue) to balance extensive lipid coverage and specificity with high sample throughput. For identification purposes, both data-dependent and data-independent tandem mass spectrometric approaches were incorporated, providing more extensive lipid coverage. Popular open-s ource feature detection, data processing, and identification software are also outlined.

show abstract

Section: Notesmentioning

confidence: 99%

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Ulmer

Patterson

Koelmel

et al. 2017

Methods in Molecular Biology

View full text Add to dashboard Cite

show abstract

“…The web-based software platform MeltDB (Fig. 1, f) has been evaluated for the analysis of Xcc metabolomics data by means of GC-MS [180][181][182]. This software tool supports storage, sharing, analysis and integration of metabolomics experiments and mapping of the measured metabolites onto the metabolic pathway maps provided by an integrated KEGG [183] database.…”

Section: Metabolomicsmentioning

confidence: 99%

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

View full text Add to dashboard Cite

Bacteria of the genus Xanthomonas are a major group of plant pathogens. They are hazardous to important crops and closely related to human pathogens. Being collectively a major focus of molecular phytopathology, an increasing number of diverse and intricate mechanisms are emerging by which they communicate, interfere with host signalling and keep competition at bay. Interestingly, they are also biotechnologically relevant polysaccharide producers. Systems biotechnology techniques have revealed their central metabolism and a growing number of remarkable features. Traditional analyses of Xanthomonas metabolism missed the Embden-Meyerhof-Parnas pathway (glycolysis) as being a route by which energy and molecular building blocks are derived from glucose. As a consequence of the emerging full picture of their metabolism process, xanthomonads were discovered to have three alternative catabolic pathways and they use an unusual and reversible phosphofructokinase as a key enzyme. In this review, we summarize the synthetic and systems biology methods and the bioinformatics tools applied to reconstruct their metabolic network and reveal the dynamic fluxes within their complex carbohydrate metabolism. This is based on insights from omics disciplines; in particular, genomics, transcriptomics, proteomics and metabolomics. Analysis of high-throughput omics data facilitates the reconstruction of organism-specific large-and genome-scale metabolic networks. Reconstructed metabolic networks are fundamental to the formulation of metabolic models that facilitate the simulation of actual metabolic activities under specific environmental conditions. SYSTEMS BIOLOGYIn recent years, systems and synthetic biology have substantially increased our understanding of many processes in life sciences at molecular and cellular levels [1][2][3]. Systems biology intends to understand the cell from a system-wide perspective. For over 100 years, life sciences have aimed at elucidating the details of molecular processes in organisms. In systems biology, scientists have started to inter-relate this data on a large scale in order to analyse the interactions of all elements [4,5], and thus initiate the decoding of entire systems, aiming at their quantitative understanding. This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13].

show abstract

“…Chem. XXXX, XXX, XXX−XXX calculated by dividing the sum of the total number of perfectly clustered replicate samples (581) and the number of retained well-clustered replicate samples (16) by the total number of replicate samples (599). According to the normality-based method, the s-value was 0.027.…”

Section: Analytical Chemistrymentioning

confidence: 99%

SMART: Statistical Metabolomics Analysis—An R Tool

et al. 2016

View full text Add to dashboard Cite

Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm .

show abstract

MeltDB 2.0–advances of the metabolomics software system

Cited by 78 publications

References 20 publications

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

SMART: Statistical Metabolomics Analysis—An R Tool

Contact Info

Product

Resources

About