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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.
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.
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.
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.
Represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R).
SimExTargId / Simultaneous metabolomic MS1-profiling Experiment and statistically relevant MS2 Target Identification
Permits users to realize autonomous and real-time analysis of metabolomic data. SimExTargId is an open source R package that provides an autonomous workflow that can also calculate data preprocessing in real-time, thereby alerting the user to signal degradation or loss. This method also facilitates real-time monitoring of liquid chromatography-mass spectrometry (LC-MS) data acquisition.
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.
MET-IDEA / Metabolomics Ion-based Data Extraction Algorithm
A current and significant limitation to metabolomics is the large-scale, high-throughput conversion of raw chromatographically coupled mass spectrometry datasets into organized data matrices necessary for further statistical processing and data visualization. MET-IDEA is a data extraction tool which surmounts this void. It is compatible with a diversity of chromatographically coupled mass spectrometry systems, generates an output similar to traditional quantification methods, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized datasets by several orders of magnitude.
An extension of the widely used mass spectrometry-based metabolomic software package XCMS. X(13)CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X(13)CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions.
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.
yamss / Yet Another Mass Spectrometry Software
Analyzes and visualizes high-throughput metabolomics data aquired using chromatography-mass spectrometry. yamss preprocess data in a way that enables reliable and powerful differential analysis. Currently, yamss implements a preprocessing method “bakedpi”, which stands for bivariate approximate kernel density estimation for peak identification. “bakedpi” is a preprocessing algorithm for untargeted metabolomics data. The output of “bakedpi” is essentially a table with dimension peaks (adducts) by samples, containing quantified intensity measurements representing the abundance of metabolites.
CAMS / Chromatogram Alignment via Mass Spectra
Detects and aligns chromatograms. CAMS is the implementation of an alignment algorithm that adjusts automatically the retention time shifts among chromatograms. Peaks of each chromatogram are detected with a continuous wavelet transform (CWT) with Haar wavelet and the aligning procedure is accelerated by the Fast Fourier Transform (FFT) cross correlation. The software can conserve the shape of peaks and can avoid the disadvantages of chromatogram preprocessing by using the full chromatographic information that is generated from hyphenated chromatographic instruments.
An open source tool which is a flexible and accurate method for pre-processing very large numbers of GC-MS samples within hours. A novel strategy was developed to iteratively correct and update retention time indices for searching and identifying metabolites. TargetSearch includes a graphical user interface to allow easy use by those unfamiliar with R. It allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software.
A package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. xMSanalyzer comprises of utilities that can be classified into five main modules: 1) merging apLCMS or XCMS sample processing results from multiple sets of parameter settings, 2) evaluation of sample quality, feature consistency, and batch-effect, 3) feature matching, and 4) characterization of m/z using KEGG REST; 5) Batch-effect correction using ComBat.
Comprises a library of functions for processing of instrument gas chromatography–mass spectrometry (GC-MS) data. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. It implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments.
A MathDAMP extension for the visualization of three-way comparisons between metabolite profiles. TriDAMP is based on the HSB (hue, saturation, brightness) color model. The three compared values are assigned specific hue values from the circular hue range (e.g. red, green, and blue). The hue value representing the three-way comparison is calculated according to the distribution of three compared values. If two of the values are identical and one is different, the resulting hue is set to the characteristic hue of the differing value. If all three compared values are different, the resulting hue is selected from a color gradient running between the hues of the two most distant values (as measured by the absolute value of their difference) according to the relative position of the third value between the two. The saturation of the color representing the three-way comparison reflects the amplitude (or extent) of the numerical difference between the two most distant values according to a scale of interest. The brightness is set to a maximum value by default but can be used to encode additional information about the three-way comparison.
Aims to compare and evaluate various publicly available open source label-free data processing workflows. msCompare is a modular framework that allows the arbitrary combination of different feature detection/quantification and alignment/matching algorithms in conjunction with a scoring method to evaluate their overall performance. msCompare was used to assess the performance of workflows built from modules of publicly available data processing packages such as SuperHirn, OpenMS, and MZmine and in-house developed modules. It was found that the quality of results varied greatly among workflows, and interestingly, heterogeneous combinations of algorithms often performed better than the homogenous workflows. This scoring method showed that the union of feature matrices of different workflows outperformed the original homogenous workflows in some cases. msCompare is available as a standalone command line program or can be used through a Graphical User Interface (GUI) into the Galaxy framework.
Allows users to perform Two Dimensional Gas Chromatography-Mass Spectrometry (2D-GCMS) derived metabolite peak alignment and identification. R2DGC uses individual sample files including basic peak information to generate an alignment table which shows the peaks common to several samples and match the aligned one to a reference library. The pipeline also furnish a reference library gathering information about 298 peaks issued from over 125 metabolite standards and commonly observed background peaks.
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.
MSFACTs / Metabolomics Spectral Formatting, Alignment and Conversion Tools
Imports, aligns, and reformats spectral and chromatographic data. MSFACTs is an application that provides an automated, rapid, and flexible means of reducing large complex chromatographic/spectrometric data sets generated in metabolomic studies into well-organized, two-dimensional matrices. This method accepts and converts integrated peak lists, composed of chromatographic retention times and peak areas.
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