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A total solution to deal with not only data dependent MS/MS but also data independent MS/MS experiments for metabolomics and lipidomics. Its feature is 1) implementing de-convolution method for data independent MS/MS 2) using unified criteria for peak identification 3) supporting all data processing step from raw data import to statistical analysis 4) user-friendly graphic user interface. MS-DIAL deals with data independent acquisition MS/MS data (ex. SWATH) by means of two step algorithms (peak spotting and MS2Dec) for spectral deconvolution. Also, it supports compound identification, peak alignment, and principal component analysis on the graphical user interface. The spectrum information is outputted by MassBank, NIST, and Mascot formats. And the organized data matrix (sample vs metabolite) is exported as tab delimited text file.
PiMP / Polyomics integrated Metabolomics Pipeline
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Allows users to analyze and visualize liquid chromatography – mass spectrometry (LC-MS) data. PiMP is a comprehensive and integrated web enabled pipeline that consists of five tasks: (1) project administration, (2) data upload, (3) quality control, (4) analysis parameters and (5) data interpretation. Users can define the experimental design, specify metadata and share the project with collaborators with a chosen level of permission. It aims at automatization and standardization of metabolomics analysis.
MAIT / Metabolite Automatic Identification Toolkit
An R package of a set of tools and functions to perform an automatic end-to-end analysis of LC/MS metabolomic data, putting special emphasis on peak annotation and metabolite identification. The goal of the MAIT package is to provide an array of tools that makes programmable metabolomic end-to-end statistical analysis possible. MAIT includes functions to improve peak annotation through the process called biotransformations and to assess the predictive power of statistically significant metabolites that quantify class separability.
An R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities.
A next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.
A software tool for the efficient and automatic analysis of GC/MS-based metabolomics data. Starting with raw MS data, MetaboliteDetector detects and subsequently identifies potential metabolites. Moreover, a comparative analysis of a large number of chromatograms can be performed in either a targeted or nontargeted approach. It automatically determines appropriate quantification ions and performs an integration of single ion peaks. The analysis results can directly be visualized with a principal component analysis. Since the manual input is limited to absolutely necessary parameters, the program is also usable for the analysis of high-throughput data. However, the intuitive graphical user interface of MetaboliteDetector additionally allows for a detailed examination of a single GC/MS chromatogram including single ion chromatograms, recorded mass spectra, and identified metabolite spectra in combination with the corresponding reference spectra obtained from a reference library. MetaboliteDetector is able to import GC/MS data in NetCDF and FastFlight format.
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.
Assist users in processing, visualization and re-analysis of publicly-submitted raw and processed Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics datasets. MetabolomeExpress performs three main functions: (i) store complete GC/MS metabolomics datasets in a way that makes them highly accessible, (ii) provide researchers with cost-free online access to a powerful raw data processing pipeline and (iii) store metabolite response statistics in a central database.
An online tool for rapid processing and analysis of LCMS-based metabolomics data. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features.
Enables application and validation of Independent Component Analysis (ICA) on non-targeted metabolomics data. MetICA is a heuristic method, based on the FastICA algorithm and hierarchical clustering, as well as on the Icasso algorithm used in medical imaging studies. The software can be used for routine validation and interpretation of ICA in non-targeted metabolomics. It was tested on simulated data and several mass spectrometry (MS)-based non-targeted metabolomics data, including low resolution MS datasets.
MPP / Mass Profiler Professional
A powerful chemometrics platform designed to exploit the high information content of MS data and can be used in any MS-based differential analysis to determine relationships among two or more sample groups and variables. MPP provides advanced statistical analysis and visualization tools for GC/MS, LC/MS, CE/MS and ICP-MS data analysis. MPP also integrates smoothly with Agilent MassHunter Workstation, Spectrum Mill and ChemStation software and is the only platform that provides integrated identification/annotation of compounds and integrated pathway analysis for metabolomic and proteomic studies. The system also enables automated sample class prediction that revolutionizes mass spectrometer-based qualitative analysis of unknown samples in many applications.
Elements for Metabolomics
Assists users in analyzing metabolomics experiments using liquid chromatography coupled with mass spectrometry (LC-MS and LC-MS/MS). Elements for Metabolomics is a program that extracts features from the data, and performs a spectral library search to identify the extracted features. This tool allows users to organize, summarize and visualize the identified metabolites across a large number of biological samples. Moreover, it can support complex experiments by combining attributes (metadata) with Mass Spectrometry data.
MetDAT / Metabolite Data Analysis Tool
Allows analysis of mass spectrometry data. MetDAT permits users to combine experiment-centric workflows and data to optimize metabolite analysis. This tool is a web application providing interactive and customizable modules and user-driven analysis of data at hierarchical levels. It standardizes data analysis for researchers and realizes searches to simplify metabolite identification, and pathway mapping. It also contains a rich palette of visualization tools.
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