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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.
Allows users to query the BinBase gas chromatography–mass spectrometry (GC-MS) metabolome database for exploring compounds. BinVestigate queries biological metadata for each metabolite. The software provides open access information about abundance, frequency, species, and organ origin. It can be used to query unknowns from metabolomics studies and to prioritize and select targeted unknowns for structural identification on the basis of their cross-study specificity and relevance.
ADAP-GC / Automated Data Analysis Pipeline
Extracts metabolite information from raw. ADAP-GC is an automated computational pipeline for untargeted, gas chromatography mass spectrometry (GC/MS)-based metabolomics studies. This workflow is designed to preprocess raw, untargeted, GC/MS metabolomics data. It carries out a sequence of computational tasks that includes construction of extracted ion chromatograms (EICs), detection of peaks from EICs, spectral deconvolution, and alignment of analytes across samples.
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.
Allows users to explore fragment data. MS2LDA is based on the Latent Dirichlet Allocation (LDA) method. It guides interpretation of comparative untargeted metabolomics experiments and prioritizes structural characterization of Mass2Motifs across large sample sets. This tool can find relevant substructures and/or substructures of both endogenous and exogenous origin in urine cohort samples where intragroup variance was suspected to arise from differential drug and/or food administration.
An LC/MS-based data analysis approach which incorporates novel nonlinear retention time alignment, feature detection, and feature matching. The XCMS software reads and processes LC/MS data stored in netcdf , mzXML, mzData and mzML files. It provides methods for feature detection, non-linear retention time alignment, visualization, relative quantization and statistics. XCMS is capable of simultaneously preprocessing, analyzing, and visualizing the raw data from hundreds of samples. XCMS is freely available under an open-source license.
A computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets.
AMDIS / Automated Mass spectral Deconvolution and Identification System
Provides an application for daily routine and emergency toxicology. AMDIS is a software developed to identify of even low-abundant peaks in the total ion chromatograms (TIC) and the reduction of the evaluation time by half. This method first deconvolutes pure component spectra and related information such as peak shape and retention time from complex chromatograms and subsequently matches the obtained spectra with those of a reference library.
A tool operating in the Taverna environment for putative identification of metabolites from accurate mass data acquired in mass spectrometry-based metabolic profiling studies. Three workflows perform the following steps. (Step 1) Generation of a list of pairwise peak correlations required for input to workflow 2 (workflow 1) (Step 2) Annotation of features to group different ion types of the same metabolite based on mass differences, similar retention times and correlation coefficient between peak responses (workflow 2). (Step 3) Matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula in a reference file within a specified mass tolerance (workflow 2). (Step 4) Matching of the molecular formulae to a reference file of metabolites (workflow 3).
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.
Aligns and calculates pairwise similarity scores among mass spectrometry (MS)/MS spectral data. MetCirc is an open-source package to make biological sense of mass spectral similarities from metabolomics data by providing a dedicated data analysis infrastructure and visualization interface to explore small molecules that mediate functionally important phenotypes. It can be used to pinpoint and formulate first structural hypothesis on previously non-characterized metabolites associated with a given phenotype.
Integrates algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. CAMERA integrates multiple methods for grouping related features, and uses a dynamic rule table for the annotation of ion species. It is designed to post-process XCMS feature lists, and to collect all features related to a compound into a compound spectrum. For this, a set of algorithms has been implemented in CAMERA, such as the fast retention time-based grouping, but also a graph-based algorithm to integrate the peak shape analysis, isotopic information and intensity correlation across samples. The automatic sample selection avoids poor results if compounds have a low intensity (or are absent) in some samples. The ion species annotation uses a dynamic rule set, and a new strategy to combine spectral information from samples measured in positive and negative ion mode.
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.
Clusters non-targeted mass spectrometric metabolomics data. With RAMClustR, feature detection is performed on both MS and idMS/MS (indiscriminant MS/MS) data and feature-feature relationships are determined simultaneously from the MS and idMS/MS data. This tool facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation as compared to single feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds.
Facilitates formula predictions and isomer structure selections from tandem mass spectrometry (MS/MS). MS-FINDER allows spectra elucidation of chemical structures from accurate mass precursor ions and MS/MS spectra. The software (1) searches the candidate either/both in the insilico metabolome expansion database MINE23 and in the PubChem compound database, and (2) scores and ranks candidate structures by the result of in silico MS/MS annotation using the hydrogen rearrangement (HR) rules.
Analyzes untargeted Liquide Chromatography/Mass Spectrometry (LC/MS) data from stable isotope-labeling experiments. geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. The detection of new mass spectral peaks that appear in samples that were fed an isotopically labeled precursor is a more robust strategy for tracking the fate of stable isotopes than iterative approaches over all MS signal data that are characteristic of existing tools.
MET-COFEA / METabolite COmpound Feature Extraction and Annotation
A liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform. MET-COFEA detects and clusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms.
A metabolite-based alignment approach entitled MET-XAlign to align metabolites across LC/MS metabolomics profiles. MET-XAlign takes the deduced molecular mass and estimated compound retention time information that can be extracted by a previously published tool, MET-COFEA, and aligns metabolites based on this information. MET-XAlign is able to cross-align metabolite compounds, either known or unknown, in LC/MS profiles not only across different samples, but also across different biological experiments, and different electrospray ionization modes.
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An extensively curated database of high-resolution tandem mass spectra that are arranged into spectral trees. mzCloud represents a key conceptual shift towards a modern mass spectral database. This tool is a scientifically solid and computationally robust platform for the identification of small molecules using tandem mass spectrometry. It offers a web-based interface providing a number of search, visualization and data processing tools. It also comprises curated databases of high and low resolution MSn spectra acquired under a number of experimental conditions which address the spectra reproducibility problem.
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