SECIMTools: a suite of metabolomics data analysis tools

Kirpich, Alexander; Ibarra, Miguel; Moskalenko, Oleksandr; Fear, Justin M.; Gerken, Joseph E.; Mi, Xinlei; Ashrafi, Ali; Morse, Alison M.; McIntyre, Lauren

doi:10.1186/s12859-018-2134-1

Cited by 37 publications

(37 citation statements)

References 53 publications

(47 reference statements)

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“…SECIMtools (SouthEast Center for Integrated Metabolomics) is a suite of metabolomics tools that can be run either as a standalone or on the Galaxy platform for bioinformatics 25 . It specializes in LC-MS data processing and offers multiple statistical analyses (heatmaps, PCA, PLS-DA, clustering, ANOVA), machine learning algorithms (random forest and LASSO), and, accordingly, many visualizations to show the results 26 . MetaboShiny also offers these analyses and further expands upon them, with over seventy machine learning methods available through the caret package.…”

Section: Comparison Of Metaboshiny With Existing Toolsmentioning

confidence: 99%

MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

Wolthuis

Magnúsdóttir

Pras‐Raves

et al. 2019

Preprint

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AbstractDirect infusion untargeted metabolomics, as mass-over-charge values and intensity of ions, allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted metabolomics data.

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Section: Comparison Of Metaboshiny With Existing Toolsmentioning

confidence: 99%

MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

Wolthuis

Magnúsdóttir

Pras‐Raves

et al. 2019

Preprint

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“…Databases also are built based on type of samples analyzed to facilitate the process of identifying particular bin/s within NMR spectra [69,70]. Similarly, many software and tools also have been developed for the analysis of GC-MS and LC-MS data [71,72].…”

Section: Advancements In Data Analysis Pipelinesmentioning

confidence: 99%

“…Their review covered tools that are used for data pre-processing, annotating, post-processing and statistical analysis, and readers are advised to consult that review to obtain a wide overview of the open source software [68]. Most of the available software tools are either R based [71,[74][75][76] or Python based [72,77,78].…”

Section: Advancements In Data Analysis Pipelinesmentioning

confidence: 99%

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Pinu

2018

Fermentation

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Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

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“…SECIMTools (South East Center for Integrated Metabolomics) is a set of Python‐based metabolomics tools and applications that are available both as standalone tools and for use in Galaxy . Tools include those for QC metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heat map, principal component analysis, and modular modularity clustering), basic statistical analysis methods (partial least squares—discriminant analysis, analysis of variance, t ‐test, and Kruskal–Wallis nonparametric test), advanced classification methods (random forest and support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator least absolute shrinkage and selection operator (LASSO) and Elastic Net).…”

Section: Tools For Analytical Platformsmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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SECIMTools: a suite of metabolomics data analysis tools

Cited by 37 publications

References 53 publications

MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Tools and resources for metabolomics research community: A 2017–2018 update

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