1 - 22 of 22 results

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.

IMPaLA / Integrated Molecular Pathway-Level Analysis

Allows combined investigation of gene/protein and metabolite datasets with a comprehensive basis of biochemical pathways. IMPaLA can operate over-representation or Wilcoxon enrichment analysis (WEA). It can recognize pathways that may be disregulated on the transcriptional level, the metabolic level or both. This tool permits the identification of additional pathways with changed activity that would not be highlighted when analysis is applied to any of the functional levels alone.

InCroMAP / Integrated analysis of Cross-platform MicroArray and Pathway data

A tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways.


Includes widely used statistical methods to process and identify keys entities of input experiments, offers different integrative analysis methodologies and provides interactive visualization to facilitate biological interpretations. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. It supports in-depth analysis of metabolomic data by including four analysis modules: data normalization, statistical analysis, network construction and functional analysis.


A free open-source pathway analysis and drawing software. PathVisio allows you to draw, edit and analyse biological pathways. You can visualize your own experimental data on the pathways and find relevant pathways that are over-represented in your data set. PathVisio provides a basic set of features for pathway drawing, analysis and visualization. Additional features are available as plugins. Plugins are available for pathway building, pathway analysis, import/export functionality, data visualization or data integration. Plugins may be developed by anyone by using the PathVisio Plugin API.

MetMask / metabolite masking tool

Integrates metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. MetMask constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. It uses multiple identifier types in parallel when consolidating databases, thereby avoiding the problem of lacking a widely used identifier scheme. The software can be used to create tailored metabolite mappings with minimum user effort. Efficient handling of identifiers enables data summarization and biological interpretation via contextual analysis such as pathway projections.


A next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.


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Serves for the analysis of human data. 3Omics simplifies the data analysis by combining the advantages and operations of several existing systems and packages into a single platform. It accepts multiple experimental conditions or time-dependent transcriptomics data, proteomics data or metabolomics data. Users can perform correlation analysis, coexpression profiling, phenotype mapping, pathway enrichment analysis and Gene Ontology (GO) enrichment analysis.


An open source framework that provides visualization methods for multi-omics datasets. The open vector based graphics format SVG is used to facilitate the visualization of the results of complex functional genomics experiments. The integration of genomic and transcriptomic datasets originating from MeltDB, QuPE or EMMA is achieved via SOAP based web services. Thus, interactive visualizations of metabolite concentrations together with transcript measurements mapped on the pathways and GenomeMaps present in ProMeTra can easily be generated.

INMEX / INtegrative Meta-analysis of EXpression data

Assists researchers in conducting two common types of analyses - meta-analysis of multiple gene expression datasets ( meta-analysis) or joint analysis of a gene expression dataset and a metabolomic dataset (integrative analysis), that have been collected under the same or comparable biological conditions. INMEX supports facile data upload, flexible data annotation, comprehensive meta-analysis approaches, as well as integrative analysis of metabolomic and transcriptomic data. With the increasing numbers of data sets that are being generated and becoming publicly available, INMEX will become a valuable tool to the research community.