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GRAM / Genetic RegulAtory Modules
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Assists users for finding regulatory networks of gene modules. GRAM is an algorithm that combines information from genome-wide location and expression data sets. This program works by first performing search over all possible combinations of transcriptional regulators indicated by the DNA-binding data with a stringent criterion for determining binding. It then detects a subset of these genes with highly correlated expression, which serves as a ‘seed’ for a gene module.
BANJO / BAyesian Network inference with Java Objects
Deciphers gene regulatory networks (GRNs) from gene expression data. BANJO permits users to infer putative information flow networks in the brain from microelectrode array data. It can handle nonlinearity of real brain electrophysiology data. In this tool, the inferred neural flow is appropriately constrained to the anatomical connectivity network. It matches physiological features of the expected flow network, and it is consistent with the measured temporal dynamics of the system.
TSNI / Time Series Network Identification
Allows users to recover direct targets of the transcription factor TRP63, rather than the whole gene network. TSNI uses dynamic gene expression profiles to elucidate the function of a transcription factor and to infer its direct targets. It is able to produce an estimated precision of about 60 per cent. This tool is complementary to genome-wide ChIP-chip approach, which requires the availability of specific antibodies and that is unable to distinguish between functional and nonfunctional target sequences.
GenePath
Infers relations between genes from mutant-based experiments. GenePath is based on principles of epistasis analysis. It can assist biologists in the systematic exploration of mutant data, in identifying and testing new relations, and in documenting and communicating genetic data. It can also help in teaching concepts of genetic data analysis. Examples are provided, including gene network studies on Dictyostelium discoideum (transition from growth to development, spore formation and intercellular communication) and Caenorhabditis elegans (programmed cell death and dauer larva formation).
CMGRN / Constructing Multilevel Gene Regulatory Networks
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An integrative web server to unravel hierarchical interactive networks at different regulatory levels. The developed method used the Bayesian network modeling to infer causal interrelationships among transcription factors or epigenetic modifications by using ChIP-seq data. Moreover, CMGRN used Bayesian hierarchical model with Gibbs sampling to incorporate binding signals of these regulators and gene expression profile together for reconstructing gene regulatory networks.
MERLIN / Modular regulatory network learning with per gene information
Infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. MERLIN is an algorithm for learning regulatory networks that strikes a balance between per-gene and per-module methods. It was used to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation.
GINsim / Gene Interaction Network simulation
Supports the definition, the simulation and the analysis of regulatory graphs, based on the logical formalism. GINsim is a software that displays a window enabling the creation of a new model, the import of a model in a supported format, or the opening of a previously defined model. This tool leans on two main types of graphs: Logical Regulatory Graphs, which model regulatory networks, and State Transition Graphs, which represent their dynamical behavior.
SeqSpider
Enables learning from tag distributions, a unique feature of ChIP-Seq and bisulfite sequencing data, and combined with a profile clustering method for noise removal. SeqSpider is a Bayesian network inference algorithm correctly predicted the interactions between DNA methylation, histone modifications, gene expression, transcription factors and chromatin modification complexes as well as their underlying motif interactions using datasets of two human embryonic stem cell lines from three laboratories. It also enables ab initio identification of interactions from multiple sources of heterogeneous data.
Magia
Enables integrative analysis of in silico target prediction. MAGIA is a web application that comprises three main sections: (i) data upload, (ii) data analysis and methods setup and (iii) results visualization, browsing and linking to external knowledgebase and tools. The software tries to dissect regulatory complexity reconstructing mixed regulatory circuits involving either human microRNA (miRNA) or transcription factor (TF) as regulators. Both data and analyses results are stored in a user-specific environment keeping the data private.
HOCCLUS / Hierarchical Overlapping Co-CLUStering2
Detects MiRNA-gene regulatory networks (MGRNs). HOCCLUS is an algorithm based on the ability to solve a non-negative matrix factorization problem to retrieve biclusters. The application, which is able to determine the optimal number of biclusters, first extracts a set of initial non-hierarchically organized biclusters. Then it successively and iteratively performs both their overlap identification and merging, for lastly rating the targeted biclusters.
BNFinder / Bayesian Network Finder
Provides a more comprehensive method for inferring networks with predefined error rate. BNFinder is a flexible tool for network topology learning from experimental data. It also introduces the possibility of calculating the optimal networks under the Mutual Information Test (MIT) score adapted to handle continuous variables as well as discrete ones. It can be also used for classification tasks. Finally, BNFinder can use parallelization on muliplecore machines to greatly improve the running times of Bayesian Networks (BNs) learning.
caspo / Cell ASP Optimizer
Provides a logic-based implementation of the hypotesis-driven research loop in systems biology. The caspo toolbox combines various steps: (i) learn a family of logical networks derived from a given topology and explaining the experimental response to various perturbations; (ii) classify all logical networks in a given family by their input-output behaviors; (iii) predict the response of the system to every possible perturbation based on the ensemble of predictions; (iv) design new experimental perturbations to discriminate among a family of logical networks; and (v) control a family of logical networks by finding all interventions strategies forcing a set of targets into a desired steady state. The caspo toolbox is a powerful software that we expect to keep improving as more researchers start using it for their investigations.
LogicTRN / Logic Transcriptional Regulatory Networks
Characterizes logic relations between transcription factors (TFs) by combining cis-regulatory logics and transcriptional kinetics. LogicTRN can identify TF targets and to reconstruct transcriptional regulatory networks (TRNs). It also can analyze data sets representing the estrogen-induced breast cancer and human-induced pluripotent stem cell (hiPSC)-derived cardiomyocyte (CM) development. It explores the nature of transcriptional gene regulation with biological meanings.
GAGE / Generally Applicable Gene-set Enrichment
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Allows gene set or pathway analysis. GAGE is able to reveal novel and relevant regulatory mechanisms from microarray studies. It produces good results concerning consistency across parallel studies or experiments; sensitivity and specificity of the pathway inference and biological relevance of the pathways identified. The tool compare expression level changes of a gene sets to the whole set background by using a two-sample t-test.
Basset
Applies convolutional neural networks (CNNs) to learn the functional activity of DNA sequences from genomics data. Basset is a package to apply deep CNNs to learn DNA sequence activity. Basset effectively learned the complex code of DNA accessibility across many cell types and substantially surpassed the predictive accuracy of the present state of the art. We demonstrated how our model precisely implicates the nucleotides driving activity, highlighting genomic positions with either fragile activity that can be lost by mutation or latent potential activity that can be unlocked by mutation.
COGRIM / Clustering Of Genes into Regulons using Integrated Modeling
Allows prediction of target gene. COGRIM is based on the integration of serum response factor (SRF) expression and position weight matrix (PWM) scan data resulted in 64 predicted SRF gene targets. This tool recognizes that SRF is the central component of a hierarchical cascade model of muscle-specific gene transcriptional network. It is able to select genes with balanced fold changes between binding and expression data.
NetProphet
An algorithm for inferring transcriptional regulatory networks from gene expression data. NetProphet capitalizes on the complementarity of the coexpression and differential expression (DE) strategies by combining them. NetProphet can also exploit any expression data source, including environmental perturbations that affect many transcription factors (TFs) simultaneously and to predict the targets of TFs that have not been individually perturbated in the available expression profiles.
DIANA-mirExTra
An online web server enabling users perform A-to-Z functional analyses, starting from next-generation sequencing expression data to the identification of important regulators with crucial roles in the investigated libraries. Users can analyze their own experiments or utilize the extensive mirExTra NGS expression library, in order to assess the role of microRNAs and transcription factors in various states, diseases and conditions. DIANA-mirExTra v2.0 permits complete substitution of in silico predictions with experimentally supported interactions and TSS positions for human and mouse. Importantly, the new web server performs sophisticated methodologies and advanced visualizations from a user-friendly interface. The multifaceted modular structure of this web application permits numerous different use-case scenarios and enables researchers to utilize DIANA-mirExTra v2.0 as a one stop shop for differential expression, functional or investigative analyses.
DREM / Dynamic Regulatory Events Miner
Integrates static interaction data of dynamic regulatory networks with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM accepts continuous binding values and utilizes TF expression levels when searching for dynamic models. It can discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. The tool uses time series expression data and static interaction data which is often condition-independent.
RGBM / Regularized Gradient Boosting Machines
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Supplies an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data. RGBM is a gene regulatory network (GRN) inference algorithm. It concerns data from heterogeneous information sources about dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. It can identify the main regulators of the molecular subtypes of brain tumors.
MMIA / miRNA-mRNA Integrated Analysis
Integrates microRNA and mRNA expression data with predicted microRNA target information for analyzing microRNA-associated phenotypes and biological functions by gene set enrichment analysis (GSEA). To assign biological relevance to the integrated microRNA/mRNA profiles, MMIA uses exhaustive human genome coverage (5782 gene sets), including various disease-associated genes as well as conventional canonical pathways and Gene Ontology. MMIA provides users with miRNA-mRNA expression data combined analysis tools and broad gene sets.
mirConnX
A user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface.
Predictive Networks
Enables the integration, navigation, visualization and analysis of gene interaction networks. Predictive Networks allows biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The pipeline involves two key steps: (i) the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources; (ii) to use these ‘known’ interactions together with gene expression data to infer robust gene networks.
predictionet
Contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, etc.) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen.
TFmiR
A freely available web server for deep and integrative analysis of combinatorial regulatory interactions between transcription factors, microRNAs and target genes that are involved in disease pathogenesis. Since the inner workings of cells rely on the correct functioning of an enormously complex system of activating and repressing interactions that can be perturbed in many ways, TFmiR helps to better elucidate cellular mechanisms at the molecular level from a network perspective.
GENIES / Gene Network Inference Engine based on Supervised Analysis
Predicts unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any 'profiles' of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene-gene similarity matrices (or 'kernels') in the tab-delimited file format.
PTHGRN / Post-Translational Hierarchical Gene Regulatory Network
An open web server to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN accepts three input: PPI, ChIP-seq binding peaks of TFs and epigenetic modifications, and gene expression data. The server provides the PPI data of human, mouse, rat, drosophila and Caenorhabditis elegans obtained from public databases BioGRID, STRING, Dip, HPRD et al. ChIP-based binding data of TF or epigenetic modifications were derived from ENCODE and modENCODE projects. Alternatively, users can submit their own datasets. Up- and down-regulated genes expression data should be separated during submission procedure.
COGERE / modeling of COndition-specific GEne REgulation
Allows users to deduce condition-specific gene regulation from gene expression data, in human and mouse. COGERE, available as both a web application and a standalone software, gathers several sources such as data mined from biomedical texts, relevant databases and computational predictions to generate model of prior information. The platform aims to assist users in generating hypothesis by suggesting references for inferred interactions.
MIPRIP / Mixed Integer linear Programming based Regulatory Interaction Predictor
Predicts regulators of a gene of interest from gene expression profiles of the samples under study and known regulator binding information (from e.g. ChIP-seq/ChIP-chip databases). MIPRIP is developed to study the specific regulation of the gene of interest in one group of samples compared to a control group. MIPRIP can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. two different phenotypes, disease/healthy controls or treatment/controls.
LDGM / Latent Differential Graphical Model
A method to infer differential network among different tissues. LDGM method allows to estimate the differential network between two tissue types directly, without inferring the network for individual tissues and without assuming normal distribution of the gene expression values. This approach also has a clear advantage of utilizing much smaller sample size to achieve reliable differential network estimation. The LDGM method may provide a unique way of integrating network inference from large gene expression data sets such as GTEx and regulatory genomics data sets from ENCODE and Roadmap Epigenomics projects to better ascertain the GRN dynamics globally across different tissue types and cell types.
RENATO / REgulatory Network Analysis TOol
Serves for the analysis gene expression profiling experiments that find regulators (transcription factors or miRNA) that account for gene activity. RENATO suits for the detecting common regulatory elements in a list of genes. This software allows the interpretation and visualization of transcriptional and post-transcriptional regulatory information. It maps genes to the regulatory network and estimates each regulator for significant over-representation in a list of genes.
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HumanNet / Human gene functional interaction Network
A probabilistic functional gene network of 18,714 validated protein-encoding genes of Homo sapiens, constructed by a modified Bayesian integration of 21 types of 'omics' data from multiple organisms, with each data type weighted according to how well it links genes that are known to function together in H. sapiens. Each interaction in HumanNet has an associated log-likelihood score (LLS) that measures the probability of an interaction representing a true functional linkage between two genes.
DRYGIN / Data Repository of Yeast Genetic Interactions
Aims at providing a central platform for yeast genetic network analysis and visualization. DRYGIN holds more than 5.4 million measurements of genetic interacting pairs involving 4500 genes. It associates the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology (GO) functional annotation. The tool will be central Saccharomyces cerevisiae resource assisting experimentalists in the generation of testable hypotheses.
Integrity
Hosts a knowledgebase designed for drug discovery. The Integrity database contains a large collection of drugs which are annotated with information on their respective drug targets, the diseases they are associated with, and the clinical phases of the drugs. Drug targets are assigned a status in Integrity, which can be ‘Validated’, ‘Candidate’, ‘Exploratory’, or none. Validated drug targets are associated with drugs under active development in clinical phases or with launched drugs for the disease of interest. Candidate drug targets are associated with drugs that are no longer under active development for the respective disease. Exploratory drug targets are associated with drugs that are currently under biological investigation for the disease. In Integrity, drugs are not directly linked to genes. Instead, drugs are linked to internal target IDs and these targets are then linked to Entrez Gene identifiers.
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