Systems biology research is like solving a puzzle: the goal is to figure out how the various parts interact and work together. The interactome of an organism is then analogous to the puzzle’s key: it describes the network of all the protein–protein interactions (PPIs). As such, identifying all the PPIs for an organism is of great value. Despite the use of high-throughput techniques in discovering PPIs, however, the coverage of experimentally determined PPI data remains poor. Such low coverage is partly because the set of possible PPIs to be verified is so large that any exhaustive experimental verification will take a long time, even with high-throughput techniques.
Provides protein-protein interaction (PPI) data for many species, including human. mentha allows users to assemble and analyze collections of proteins and networks of interest. It focuses on experimentally demonstrated physical interactions, in order to avoid the confusion between physical and genetic interactions and between experimental and inferred interactions. The database’s information come from manually curated protein-protein interaction databases.
Allows to analyze proteomic data. The compPASS method gives confidence measurements to interactions from parallel non-reciprocal proteomic datasets in using unbiased metrics. This tool is applicable to proteomic investigations ranging from focused studies on a small number of selected proteins to the analysis of entire protein families or biological regulatory networks.
Aims to proteomics data analyses. Perseus extracts biologically meaningful information from processed raw files. It uses bioinformatic analyses from MaxQuant output and completes the proteomics analysis pipeline. It contains various statistical methods or illustrations (data transformation, normalization, imputation, and more). This tool gets five main interfaces: (1) data upload, (2) export, (3) processing, (4) analysis and (5) multimatrix handling.
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
An open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration, analysis, and visualization. Additional features are available as Apps (formerly called Plugins). Apps are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases.
A web application for the structural annotation of protein-protein interaction networks. Submit your interactions and the server will find all the available structural data for both the single interactors and the interactions themselves. Additionally you can also visualize and download structural information for interactions involving a set of proteins or interactomes for one of the precalculated organisms.
Predicts high-confidence protein-protein interactions (PPIs) proteome-wide, including proteins with few or no known partners. FpClass is an in-silico method to identify sets of protein features that may act cooperatively, making an interaction more likely if a protein possesses the entire set of features rather than individual ones. It could help guide high-throughput screening in a combined computational-experimental approach to interactome mapping.
Makes structure-based computational predictions of protein-protein interactions (PPIs). The predictions are made by a structure-based threading approach. Given two protein sequences (or one sequence against all sequences of a species), the structure-based interaction prediction technique threads the sequence to all the protein complexes in the PDB and then chooses the best potential match. Based on this match, it uses machine learning techniques to predict whether the two proteins interact. This approach currently does not rely on any other functional genomic information (e.g. co-expression or cellular localization) and is hence independent of these. It may serve as an additional input into an integrative computational framework for predicting novel PPIs based on information from multiple sources.
It was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query.
A software tool that allows users to easily create an integrated human protein network, or HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.
Allows data mining and integration of microarray expression in Arabidopsis. CORNET permits users to construct molecular networks to address different biological questions. It allows the integration of co-expression and protein-protein interaction (PPI) networks, and a comprehensive visualization of the networks. This tool enables the utilization of different microarray platforms.
Studies the place of hyperbolic space (H2) into the human protein interaction network (hPIN). NetHypGeom employs the hyperbolic distance between proteins for link prediction and the reconstruction of signal transduction pathways. It can recognize proteins with transcription factor (TF), receptor, transporter or RNA-binding activity; as well as constituents of the cytoskeleton, proteins involved in ubiquitination/proteolysis and cancer proteins.
For a given protein sequence or structure query, it reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in experimentally-determined structural biological assemblies. IBIS also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.
A probabilistic framework to predict the interaction probability of proteins. We also develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework.
An interactive bioinformatic web-tool that has been developed to allow exploration and analysis of main currently known information about protein-protein interactions integrated and unified in a common and comparative platform. The analytical and integrative effort done in APID provides an open access frame where all known experimentally validated protein-protein interactions (BIND, BioGRID, DIP, HPRD, IntAct and MINT) are unified in a unique web application that allows an agile exploration of the interactome network and includes certain calculated parameters that weight the reliability of a given interaction (i.e. the "edges" of the interactome network) between two proteins, and also qualify the functional environment around any given protein (i.e. the "nodes" of the interactome network).
Investigates data stream in protein-protein interaction (PPI) networks. ITM Probe allows users to choose between three different types of models: emitting, absorbing or channel. The application permits to select interaction graphs and to determine the nodes that have to be excluded. Users also can set sinks, sources (only for the channel model), and dissipation criteria. It also includes a functionality for retrieving the submitted queries by ID.
Generates rec-YnH interaction score matrix to corresponding genes by using rec-YnH sequencing files. rec-YnH is a new yeast two and three-hybrid-based screening pipeline capable of identifying interactions within protein libraries of between protein libraries and RNA fragment pools. This software proceeds by combining batch cloning and transformation with intracellular homologous recombination to produce bait-prey fusion libraries.
A computational method that simultaneously integrates protein-protein interaction and RNAseq expression profiles during brain development to discover “modules” enriched for de novo mutations in probands. MAGI includes two main steps: the first involves finding relatively short seed pathways with high scores and the second is merging them into much larger clusters.
Predicts potential scaffold/matrix attachment regions (S/MARs) of the AT-rich class in DNA sequences. SMARTest is a prediction tool that uses a proprietary library of S/MAR-associated weight matrices for testing genomic DNA sequences for the occurrence of potential regions of S/MARs. The software can be useful for the prediction of matrix attachment regions because it is applicable to megabases of genomic sequences.
A general framework to predict, assess, and boost confidence in individual interactions inferred from a high-throughput experiment. The Coev2Net algorithm can be roughly divided into three distinct stages: 1) identification of the putative binding interface; 2) evaluation of the compatibility of the interface with an interface co-evolution-based model; and 3) evaluation of the confidence score for the interaction. It is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods.