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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.


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.


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.

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.

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.


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.

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.


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.


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).


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.


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.

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.

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.


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.


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.


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.


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 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.

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.