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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.
MS-FLO / Mass Spectral Feature List Optimizer
Improves the quality of feature lists after initial processing. MS-FLO is a stand-alone web-based application that can be used for recognition and removal of erroneous features in liquid chromatography−tandem mass spectroscopy (LC-MS) datasets. The software is designed to examine datasets after initial processing and alert users to erroneous features, thus strengthening the statistical power of the dataset. It can be added to any untargeted LC-MS-based metabolomics workflow.
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.
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.
Offers a quick and easy data quality check of liquid chromatography-high resolution mass spectrometry (LC-HRMS) derived data. QCScreen allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.
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.
QC-RFSC / QC-based Random Forest Signal Correction algorithm
Removes unwanted variations at feature-level in large-scale metabolomics and proteomics data. QC-RFSC is an algorithm that integrates the random forest (RF) based ensemble learning approach to learn the unwanted variations from quality control (QC) samples. It also predicts the correction factor in the neighboring real samples responses. Beside metabolomics data analysis, this method significantly improves the data quality for the proteomics.
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