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The raw peak intensity data was pre-processed in metaX. Pre-processing of raw peak data metabolite If a peaks table file is an input, metaX transforms the table data from a peak detection software, such as Progenesis QI (exported comma separated value (csv) format file), into an R object compatible with the subsequent workflow. If taking mzXML files as input, metaX will use the R package XCMS to detect peaks, then use the CAMERA package to perform peak annotation. In general, metaX can take mzXML files as input or a peak table file as input. Basically, the pipeline aims for users to easily perform end-to-end metabolomics data analysis with a flexible combination of different methods to efficiently integrate new modules and to build customized pipelines in multiple ways. Referring to the capabilities of the tools mainly used (as shown in Table 1), an automatic and comprehensive open source pipeline is urgent in bioinformatics analysis of metabolomics. There is no such comprehensive pipeline that is used across the metabolomics community. Open-source software, such as XCMS, CAMERA, MAIT, MetaboAnalyst and Workflow4Metabolomics, usually cover limited processing steps. The MS manufacturers generally provide propriety software, like SIEVE (Thermo Scientific), MassHunter (Agilent Technologies) and Progenesis QI (Waters), which are often limited in scope and function.
Metax mac software#
A number of software packages are available for MS-based metabolomics data analysis as listed in Table 1, including propriety commercial, open-source, and online workflows. Data analysis involves stepwise procedures including peak picking, quality control, data cleaning, preprocessing, univariate and multivariate statistical analysis and data visualization. Generally, these techniques generate a set data of mass spectra with chromatography that includes retention time, peak intensity and chemical masses. The most prevalent technology used in analysis of metabolomics is non-targeted mass spectrometry (MS) coupled with either liquid chromatography (LC-MS) or gas chromatography (GC-MS).
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The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.īiochemicals (metabolites) with low molecular masses are the ultimate products of biological metabolism, while a metabolome represents the total composite in a given biological system and reflects the interactions among an organism’s genome, gene expression status and the relevant micro-environment. The package and the example reports are available at. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. In addition, metaX offers a web-based interface ( ) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample.