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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.
FCF / Flux Coupling Finder
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.
SABIO-RK
A curated database that contains information about biochemical reactions, their kinetic rate equations with parameters and experimental conditions. All the data are manually curated and annotated by biological experts, supported by automated consistency checks. SABIO-RK can be accessed via web-based user interfaces or automatically via web services that allow direct data access by other tools. Both interfaces support the export of the data together with its annotations in SBML (Systems Biology Markup Language), e.g. for import in modelling tools.
OptCom
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.
Subpathway-GM
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.
iPAVS / integrated Pathway resources, Analysis and Visualization System
An integrated biological pathway database designed to support pathway discovery in the fields of proteomics, transcriptomics, metabolomics and systems biology. The key goal of IPAVS is to provide biologists access to expert-curated pathways from experimental data belonging to specific biological contexts related to cell types, tissues, organs and diseases. IPAVS currently integrates over 500 human pathways (consisting of 24, 574 interactions) that include metabolic-, signaling- and disease-related pathways, drug-action pathways and several large process maps collated from other pathway resources. IPAVS web interface allows biologists to browse and search pathway resources and provides tools for data import, management, visualization and analysis to support the interpretation of biological data in light of cellular processes.
GAM / Genes And Metabolites
Facilitates an analysis of the metabolomic and transcriptional profiling data in the context of cellular reaction network. GAM service provides a way for a quick interactive analysis of the data to identify the most regulated metabolic subnetworks. The service supports multiple input formats, including results from widely-used DESeq2 and limma pipelines, provides automatic selection of recommended parameter values and uses a range of maximum-weight connected subgraph (MWCS) solvers yielding good suboptimal solutions in a time frame of 30 s.
PASMet / Prediction Analysis and Simulation of Metabolic Reaction Networks
Predicts pathways and constructs mathematical models to systematically analyse metabolic systems. PASMet provides four main functionalities: (i) prediction, (ii) construction, (iii) simulation and (iv) analysis. It includes an interface to access various algorithms for predicting probable regulatory mechanisms along with generating mathematical models from time-series data, simulating metabolic behaviors and determining metabolic bottlenecks in the model.
NICELips / Network Integrated Computational Explorer for Lipidomics
Generates associations between Kyoto Encyclopedia of Genes and Genomes (KEGG) and LIPID MAPS structure Database (LMSD) databases. NICELips consists of several components integrated into a workflow and it is the first tool to provide an efficient and consistent procedure for linking lipid compound databases. It provides a full overview of all lipid species in the cell, and particularly in the context of metabolic pathways that comprise all the chemical interactions and transformations between lipid compounds and enzymes.
MADE / Metabolic Adjustment by Differential Expression
Uses the statistical significance of changes in gene or protein expression to create a functional metabolic model that most accurately recapitulates the expression dynamics. MADE is a method for mapping expression data from a set of environmental, genetic or temporal conditions onto a metabolic network model without the need for arbitrary expression thresholds. It integrates gene or protein expression data and a metabolic model without a priori determination of activity thresholds.
PAPi / Pathway Activity Profiling
Compares metabolic pathway activities from metabolite profiles. The PAPi package allows to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, it calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. It also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity.
JigCell / JigCell Project Homepage
Supports modelers of biochemical reaction pathways. JigCell provides a model builder, run manager, comparator, and automatic parameter estimator. It permits users to understand and explain the underlying mechanisms driving the physiology of the organisms under study. This toolbox can be used to define a system to be modelled using Systems Biology Markup Language (SBML), display data in an organized manner, specify a set of specifications for simulation runs and define models in terms of components for the purpose of being combined in a larger model.
Cell++
Provides the user with the flexibility to model a variety of biochemical pathways from signal transduction to metabolism. Cell++ is a stochastic simulation environment with the capacity to study a wide variety of biochemical processes within a spatial context. Combining a cellular automata engine with Brownian dynamics, this software is able to simulate the bulk properties of large quantities of small molecules while simultaneously allowing more accurate representations of large molecules that can display more complex behavior.
MetaNetter
A Cytoscape plugin dedicated to the inference and visualization of high-resolution mass spectrometry data sets. The inference is achieved using a defined list of putative biochemical transformations. The flexibility provided by this tool allows for isolation of transformation types in order to facilitate a focused analysis. A wide range of correlation analysis tools allows an even stronger inference of network connections than possible with mass difference analysis alone. In addition to the rich interactions provided by Cytoscape, the plugin offers a convenient way to visualize some topological properties of the networks.
MeltDB
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.
GNAT / Glycosylation Network Analysis Toolbox
A platform-independent, user-extensible MATLAB-based toolbox that provides an integrated computational environment to construct, manipulate and simulate glycans and their networks. It enables integration of XML-based glycan structure data into SBML (Systems Biology Markup Language) files that describe glycosylation reaction networks. Curation and manipulation of networks is facilitated using class definitions and glycomics database query tools. High quality visualization of networks and their steady-state and dynamic simulation are also supported.
QSSPN / quasi-steady state Petri nets
Reproduces experimentally determined qualitative dynamic behaviours and permits mechanistic analysis of genotype–phenotype relationships. QSSPN is a method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signaling and whole-cell metabolism. It includes regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study.
tEFMA
Uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible elementary flux modes (EFMs). Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared to standard approaches tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks.
NetCmpt
A tool for calculating the competitive potential between pairs of bacterial species. The score describes the effective metabolic overlap (EMO) between two species, derived from analyzing the topology of the corresponding metabolic models. NetCmpt is based on the EMO algorithm, developed and validated in previous studies. It takes as input lists of species-specific enzymatic reactions (EC numbers) and generates a matrix of the potential competition scores between all pairwise combinations.
MetNetMaker
Allows creation and manipulation of SBML format metabolic networks with reactions and compounds defined as in the KEGG LIGAND database. MetNetMaker is an application that uses the KEGG LIGAND compound/reaction naming convention and provides a graphical interface dedicated to flux-balance analysis (FBA)-ready metabolic network creation. The software has two main tabs: (i) Reaction Creator to create custom reactions and add them to the reaction database and, (ii) Reaction Picker to browse the reaction database and select reactions using filters.
Correlation Calculator
Estimates partial correlation networks. The Correlation Calculator program implements the Debiased Sparse Partial Correlation algorithm (DSPC). It is a standalone Java application providing various methods of calculating pairwise correlations among repeatedly measured entities. It is designed for use with quantitative metabolite measurements such as mass spectrometry (MS) data on a set of samples. The workflow allows inspection and/or saving of results at various stages, and the final correlation results can be dynamically imported into MetScape as a correlation network.
BoostGAPFILL
A machine learning method for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. Authors test the robustness of the gap-filling algorithms using artificial gaps (i.e., metabolites that cannot be produced or consumed at steady state) to simulate poorly characterized biochemistry. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools.
MetaboSignal
Allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). MetaboSignal is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. This approach is ideally suited to identify candidate genes in metabotype-Quantitative-Trait Locus (QTL) or to identify biological pathways affected in transgenic models. This approach is ideally suited to identify candidate genes in metabotype-QTL studies (e.g. trans-acting associations), or to identify biological pathways affected in transgenic models.
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