Searches protein database using a translated nucleotide query. BLASTX is a BLAST search application that compares the six-frame conceptual translation products of a nucleotide query sequence (both strands) against a protein sequence database. This application can also work in Blast2Sequences mode and can send BLAST searches over the network to public NCBI server if desired.
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.
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.
A web-based tool for the visualization and analysis of cellular pathways. Its primary map summarizes the metabolism in biological systems as annotated to date. Nodes in the map correspond to various chemical compounds and edges represent series of enzymatic reactions.
Integrates omics data for analyzing them together. Simple BL-SOM is an integrated analytical tool, where batch-learning SOM (BL-SOM) makes the learning process and resulting map independent of the order of input data and the initial condition. The software enables the identification of gene-to-gene and metabolite-to-gene networks and new gene functions.
A web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis.
Manages multi-omics datasets visualization within KEGG pathway diagrams. PaintOmics is dedicated to pathway analysis and proposes functionalities for converting names and/or identifiers, performing pathway enrichment and network analysis. This program is able to handle data from multiple type of omics measurement. Results can be displayed by pathways summary, classification, networks, enrichment or by detailed pathway view.
Aims users to detect metabolites by annotation of pathways from cross-omics data. MarVis-Suite serves especially for the extraction, clustering, and visualization of metabolic markers from data originating of non-targeted experiments. It provides interactive desktop user interfaces for interactive inspection of data clusters, and supplies specialized functions for the analysis of data from non-targeted mass spectrometry (MS) experiments.
Provides a bioinformatics framework for the visualization and interpretation of metabolomic and expression profiling data in the context of human metabolism. MetScape allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolite data. MetScape uses an internal relational database that integrates data from KEGG and EHMN.
Develops many interactive web-based databases and software to help the life-scientists understand the complexity of systems biology. Systems biology efforts focus on understanding cellular networks, protein interactions involved in cell signaling, mechanisms of cell survival and apoptosis leading to development or identification of drug candidates against a variety of diseases.
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 standard based on the XML markup language. The purpose of CellML is to store and exchange computer-based mathematical models. CellML allows scientists to share models even if they are using different model-building software. It also enables them to reuse components from one model in another, thus accelerating model building.
Aims to integrate and analyze metabolomics experiment data. MeltDB is a program that can be applied for the description and analysis of metabolomic experiments. This program hosts over 30 experiments predominantly from gas chromatography-mass spectrometry (GC/MS) measurements. Moreover, this tool includes an API allowing users to evaluate novel methods and algorithms for the preprocessing of metabolomic datasets.
Elucidates the topological and flux connectivity features of genome-scale metabolic networks and enables the global identification of blocked reactions, equivalent knockouts, and sets of affected reactions. The FCF computational procedure allows one to determine whether any two metabolic fluxes, v1 and v2, are (i) fully coupled, if a non-zero flux for v1 implies a non-zero, but also a fixed flux for v2 and vice versa; (ii) partially coupled, if a non-zero flux for v1 implies a non-zero, though variable, flux for v2 and vice versa; or (iii) directionally coupled, if a non-zero flux for v1 implies a non-zero flux for v2 but not necessarily the reverse. Due to its wide range of features and applicability to genome-scale networks, the FCF procedure provides a useful framework for both modelers and experimentalists seeking to extract biologically meaningful information from metabolic reconstructions.
Identifies an approximately minimum set of driver nodes to control a specified target set of nodes. This approach consists of a greedy algorithm (GA) based on the structural control theory: the system parameters are either fixed at zero or are independent free parameters. This algorithm can find the driver nodes for target control when the network structure is completely known.
Provides current experimental knowledge of the proximal hepatic insulin signaling network. This model provides a sequential method of postprandial hepatic control of glucose and lipid by insulin, according to which delayed aPKC switch-off contributes to selective hepatic insulin resistance. This model is quantitatively informed by two in vivo data sets from rodent studies, where hepatic insulin signaling was measured after refeeding or after various patterns (pulsatile/constant/T2D) of pre-hepatic insulin infusion.
Annotates metabolites in high precision mass spectrometry data. MassTRIX marks the identified chemical compounds on KEGG pathway maps using the KEGG/API. Selected genes or enzymes can be highlighted, e.g. to represent information on gene transcription or differences in the gene complement of different bacterial strains.
Provides a comprehensive flux balance analysis framework for microbial communities. OptCom relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. This modeling framework is general enough to capture any type of interactions (positive, negative or combination of both) for any number of species (or guilds) involved. In addition, OptCom is able to explain in vivo observations in terms of the levels of optimality of growth for each participant of the community.
A powerful analytical method for the identification of biologically meaningful metabolic subpathways. Subpathway-GM integrates ‘interesting genes’ and ‘interesting metabolites’ related to the study condition (e.g. disease) into the corresponding enzyme and metabolite nodes (referred to as signature nodes) within the metabolic pathway. We then analyzed lenient distance similarities of signature nodes within the pathway structure to locate key metabolic cascade subpathway regions. Finally, a hypergeometric test was used to evaluate the enrichment significance of these subpathway regions.