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Identifies endogenous mammalian biochemical structures contained within chemical structure space. BioSM is a molecular classifier that uses the structures of known endogenous mammalian biochemical compounds to assist in the classification process. The graph-based method implemented in this application can also be expanded to predict metabolic pathways. It can be useful for searching large chemical databases in metabolomics applications where the number of potential false positives is very large.
It captures the entire lifecycle of a sample starting from project and experiment design to sample analysis, data capture and storage. MASTR-MS is a downloadable and installable LIMS solution that can be deployed either within a single laboratory or used to link workflows across a multisite network. It acts as an electronic notebook, facilitating project management within a single laboratory or a multi-node collaborative environment. MASTR-MS can be used to track and share metabolomics experiments within a single laboratory or across large collaborative networks. Its comprehensive functions and features enable researchers and facilities to effectively manage a wide range of different project and experimental data types and facilitate the mining of new and existing datasets.
COVAIN / COVAriance INverse engineering
A statistical data analysis software that runs under Matlab enviroment. Though COVAIN was initially designed for metabolomics data processing, it can analyze other types of omics data, such as proteomics and transcriptomics. COVAIN gets its name from one of its functionalities: COVAriance INverse engineering. The design principle is to put most common data analysis methods – including preprocessing, uni- and multi-variate statistics, time-series analysis and network properties – into one software with a full graphical user interface (GUI) support, thus making data analysis more convenient for both biologists and bioinformatician.
MetaFIND / Metabolomics Feature Interrogation and Discovery
Allows “post-feature selection” correlation analysis. MetaFIND is a metabolomics feature analysis tool that analyzes the set of features retrieved by the investigator's chosen feature selection technique and provides support for uncovering the various metabolite signatures that may be present within this set. The software also enables the examination of correlations outside the selected feature set. It can be useful for metabolite signature identification, feature discovery and may aid inference of metabolic relationships by identifying highly correlated metabolites.
Considers as a modelica library for human physiology. By graphical joining instances of Physiolibrary classes, user can create models of cardiovascular circulation, thermoregulation, metabolic processes, nutrient distribution, gas transport, electrolyte regulation, water distribution, hormonal regulation and pharmacological regulation. After easy-to-use setting of the parameters, the models are ready to be run. After simulation, user can examine variables as their values change over time.
Provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration.
Allows analysis of glycosyltransferases (GTrs) involved in natural product biosynthesis. SEARCHGTr assists users in identification of homologous sequences, depiction of domains and linkers and extraction of putative donor/acceptor binding residues. It permits comparison of amino acids lining the substrate binding pocket and provides clues for altering donor/acceptor selectivity of GTrs by site. The identification of substrates for various uncharacterized GTrs in newly sequenced genomes can be simplified by this tool.
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