Data normalization software tools | Mass spectrometry-based untargeted metabolomics
In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.
Provides a web-based analytical pipeline for high-throughput metabolomics studies. MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies. This tool is also available as desktop version.
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.
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.
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.
An adaptive multi-seeds based heuristic clustering method that avoids the large memory need for storing seeds and/or distance matrix. MSClust uses a greedy heuristic strategy to build one cluster at a time. Each cluster is expanded from a limited initial set with multi-seeds, where the initial multi-seeds are generated based on an adaptive strategy. Unassigned sequences are then compared to the seeds sequentially. A new sequence is added to the current cluster and removed from the input if the average distance between the sequence and seeds is smaller than the user-defined threshold; otherwise, the sequence is marked as unassigned.
Provides a program for the quantitative analysis of high throughput Gas Chromatography-Mass Spectrometry (GC-MS)-based metabolomics data. MetaQuant is intended to automatically determine the accurate intracellular amount of hundreds of metabolites. It provides access to various functions: (i) metabolite definition, (ii) calibration, (iii) quantification, (iv) import and export of data and (v) batch analysis.
Facilitates the visualization of differences between metabolite profiles acquired by hyphenated mass spectrometry techniques. Differences are highlighted by applying arithmetic operations to all corresponding signal intensities from whole raw (automatically preprocessed and normalized) datasets on a datapoint-by-datapoint basis. The results are visualized using density plots.