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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.
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.
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.
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.
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.
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