Noise filtering software tools | Mass spectrometry-based untargeted metabolomics
Smoothing aims to remove noise in the measured spectra, which facilitates further peak detection. Smoothing is an optional stage in data processing and can also be left out if the data is not noisy or if the input data is already available as centroids.
An open-source software tool for mass-spectrometry data processing, with the main focus on LC-MS data. It is based on the original MZmine toolbox described in the 2006 Bioinformatics publication, but has been completely redesigned and rewritten since then. Our main goal is to provide a user-friendly, flexible and easily extendable software with a complete set of modules covering the entire LC-MS data analysis workflow.
Allows intensity drift correction in Liquid chromatography coupled to mass spectrometry (LC/MS) data. intCor is an R package that implements five methods: common principal component analysis (CPCA), component correction CC, median fold change, ComBat and the CPCA+ median fold change. This tool permits to normalize the LC/MC metabolomic data to control the quality of the acquisition data step.
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).
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.
Implements a methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML). proFIA is an R package that includes noise modelling, injection peak reconstruction, band detection and filtering then signal matching and missing value imputation. It generates the peak table. This approach also filters out the features whose intensity is not significantly higher than the solvent baseline.
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.
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.
Assists users to select the mass chromatograms with a minimal amount of high-frequency noise and a minimal background. CODA detects several correlated mass chromatograms for each component, depending on the amount of fragmentation, or cluster peaks. This algorithm can choose high-quality mass chromatograms by selecting only mass chromatograms above a certain mass chromatographic quality (MCQ) level. It is able to compute the similarity index (cJ) between the length-scaled mass chromatogram and the smoothed standardized mass chromatogram.
Allows users to study and modify metabolomics raw data. CorrectOverloadedPeaks is a program that aims to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Regarding the overloaded signals, they are detected automatically and modified using a Gaussian or Isotopic-Ratio approach. Moreover, this tool can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.
An automated tool with friendly user interfaces for quantifying metabolites in full-scan liquid chromatography-mass spectrometry (LC-MS) data. iMet-Q has a complete quantitation procedure for noise removal, peak detection and peak alignment. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification.
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.
Assists users for metabolomics data analysis. Specmine includes a workflow that can be adapted for specific case studies, addressing tasks as data loading, pre-processing, normalization, metabolite identification, univariate and multivariate statistical analysis, clustering, machine learning and feature selection. It also offers modules for the visualization of data including box plots, volcano plots and spectra.
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.
Allows processing of liquid chromatography coupled to tandem mass spectrometry (LC-MSn) metabolomics data. MassCascade is a library and node-suite containing a collection of data processing algorithms and a visualization framework. MassCascade-KNIME is the software plug-in for the Konstanz Information Miner (KNIME) environment. The library can be used as a standalone or in combination with the plug-in, which provides a modular, step-by-step solution for building complex workflows with the ability to inspect the output of each method in between nodes.
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.
Assists users to analyze gas chromatography/differential mobility spectrometry (GC/DMS) metabolomics data. AIMS can also be applied for other similarly structured data sets, such as comprehensive two-dimensional gas chromatography (GC/GC) and other ion mobility spectrometry (IMS) modalities. Furthermore, this software includes a flexible code that can be adapted for new modules developed by others.
Offers library searching of data generated from any Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas chromatography-mass spectrometry (GC-MS) platform. AnalyzerPro is a commercial and comprehensive post-processing utility for low and high-resolution LC-MS and GC-MS data with multi-vendor data support. It also provides optimized workflows for sample-to-sample comparison, target component analysis, quantification.
Allows users to work on molecule match, mass chromatograms or molecular formulae. Mnova MS is a program permitting several functions such as: (1) computation of MS peak purity; (2) visualize user’s data; (3) predict isotope clusters; (4) or process and report MS data on user’s computer.