Probing the complex fusion of genetic and environmental interactions, metabolic profiling (or metabolomics/metabonomics), the study of small molecules involved in metabolic reactions, is a rapidly expanding 'omics' field. A major technique for capturing metabolite data is 1H-NMR spectroscopy and this yields highly complex profiles that require sophisticated statistical analysis methods.
Provides a web-based analytical pipeline for high-throughput metabolomics studies. MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies. This tool is also available as desktop version.
A strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. CIMA selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The proposed approach focuses on the general metabolomic hypothesis-testing problem, whereas incorporating structure information into other common metabolomic inference practices, such as multivariate predictive model construction and network inference, is warranted for further research.
Includes widely used statistical methods to process and identify keys entities of input experiments, offers different integrative analysis methodologies and provides interactive visualization to facilitate biological interpretations. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. It supports in-depth analysis of metabolomic data by including four analysis modules: data normalization, statistical analysis, network construction and functional analysis.
Assists users nuclear magnetic resonance (NMR)-based metabonomics or metabolomics spectral processing and data analysis. Automics is a highly integrated platform that was developed to aid researchers for processing high dimensional NMR spectroscopic data. In addition, features such as data organization, data preprocessing and a wide range of data investigation techniques for multivariate data analysis, classification and regression have been implemented.
Assists users for metabolomics data analysis. Specmine includes a workflow that can be adapted for specific case studies, addressing tasks as data loading, pre-processing, normalization, metabolite identification, univariate and multivariate statistical analysis, clustering, machine learning and feature selection. It also offers modules for the visualization of data including box plots, volcano plots and spectra.
Identifies metabolites from untargeted liquid chromatography-mass spectrometry (LC-MS) raw files using spectral libraries. Scaffold Elements is a molecule identification platform for small molecule mass spectrometry experiments. The software enables visualization of complex experiments and tracking of quantitative changes. It supports several vendor instruments such as Agilent or Bruker and allows users to search multiple spectral libraries, such as METLIN or NIST. A free viewer is also available for sharing data.
Assists with analyze and management of multiple types of spectral and chemical data. KnowItAll Informatics System is an application that integrates toolsets for: spectral search, spectral identification, mixture analysis, multi-technique spectral data management, nuclear magnetic resonance (NMR) prediction, processing and subtraction, quality control analysis, reporting and publishing tools among others.
Integrates nuclear magnetic resonance (NMR) processing and visualization with mathematics and statistical analysis of data. dataChord Spectrum Miner is a visual application that was developed for metabolomics and spectrum mining. It includes component analysis module along with a variety of other data mining algorithms.
Combines standard nuclear magnetic resonance (NMR) spectral processing functionalities with techniques for multi-spectral dataset analysis. HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. It couples rigorous data pre-processing, artifact removal and identification of metabolic patterns via principal component analysis (PCA).
Assists users for the chemometric analysis of nuclear magnetic resonance (NMR), infrared (IR) or Raman spectroscopy data. ChemoSpec is an R package that includes modules to plot and inspect spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. This method was designed with metabolomics data sets in mind.
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