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Aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. STRING covers more than 2000 organisms, which has necessitated scalable algorithms for transferring interaction information between organisms. Further improvements include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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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.
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.
PSICQUIC / Proteomics Standard Initiative Common QUery InterfaCe
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.
IBIS / Inferred Biomolecular Interactions Server
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.
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.
PreSPI / Prediction System for Protein Interaction
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.
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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.
APID / Agile Protein Interaction DataAnalyzer
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).
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.
HIPPIE / Human Integrated Protein-Protein Interaction rEference
A human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.
A web-based tool designed to perform protein-protein interaction (PPI) network analysis from gene expression data. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse.
PPISearch / Protein-Protein Interaction Search
Identifies homologous protein-protein interactions (PPIs) (called PPI family) and infers transferability of interacting domains and functions of a query protein pair. PPISearch first identifies two homologous families of the query, respectively, by using BLASTP to scan an annotated PPIs database (290 137 PPIs in 576 species), which is a collection of five public databases. We determined homologous PPIs from protein pairs of homologous families when these protein pairs were in the annotated database and have significant joint sequence similarity (E < or = 10(-40)) with the query. Using these homologous PPIs across multiple species, this sever infers the conserved domain-domain pairs (Pfam and InterPro domains) and function pairs (Gene Ontology annotations). The PPISearch server should be useful for searching homologous PPIs and PPI families across multiple species.
A public web server specifically designed to instantly construct genome-scale protein networks based on associalogs (functional associations transferred from a template network by orthology) for a query species with only protein sequences provided. Assessment of the networks by JiffyNet demonstrated generally high predictive ability for pathway annotations. Furthermore, JiffyNet provides network visualization and analysis pages for wide variety of molecular concepts to facilitate network-guided hypothesis generation.
Allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks.
Cancer PanorOmics
Integrates and displays high-throughput cancer sequencing analyses, together with data obtained from individual patients. Cancer PanorOmics is a web app that maps mutations on the high-resolution 3D structure of proteins and protein–protein interactions (PPIs) to provide a molecular context to the genomic alterations. This visualisation tool offers a contextualized view of the genomic alterations uploaded by the user within the available knowledge for the selected tumor type.
Contains the source Matlab codes for the transcription factor binding site (TFBS) predictors MultiTF and MultiTF-PPI with two different priors. MultiTF-PPI is a probabilistic protein-protein interaction (PPI) guided method for competitive transcription factor binding prediction. It provides all prediction results in terms of probabilities, which also allows us to answer quantitative questions of the TF binding. Most importantly, MultiTF-PPI also includes explicit interactions between TFs. MultiTF is a probabilistic model for competitive TF binding prediction is built on the standard probabilistic building blocks.
Affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF.
SCENERY / Single CEll NEtwork Reconstruction sYstem
Allows analysis of cytometry data. SCENERY is a web application permitting users to upload their data and configure the study of their data. It provides a wide range of data analysis methods including: (1) basic pre-processing methods allowing users to transform, compensate and manually gate samples; (2) univariate analysis methods such as regression and factor analysis; and (3) advanced machine learning methods for association and causal network reconstruction (NR) that identify interactions between the measured quantities.
An approach to predict PPIs from sequence alone which is based on evolutionary profiles and profile-kernel Support Vector Machines (SVMs). Profppikernel improved significantly over the state-of-the-art, in particular for proteins that are sequence-dissimilar to proteins with known interaction partners. Filtering by gene expression data increased accuracy further for the few, most reliably predicted interactions (low recall). The overall improvement was so substantial that we compiled a list of the most reliably predicted PPIs in human.
CoPIT / co-interacting protein identification technology
Allows comprehensive identification and analysis of membrane protein interactomes and their dynamics. CoPIT integrates experimental and computational methods for a coimmunoprecipitation (Co-IP)-based workflow from sample preparation for mass spectrometric analysis to visualization of protein-protein interaction networks. The approach particularly improves the results for membrane protein interactomes, which have proven to be difficult to identify and analyze.
ANAT / Advanced Network Analysis Tool
Assists users for inference of functional networks of proteins. ANAT provides access to interaction data from several sources, advanced algorithms for network reconstruction. It supports four types of network-based analyses: (i) inferring an anchored network that connects a given set of proteins to a designated set of focal points, (ii) inferring high-confidence “general” networks, (iii) finding the highest-confidence paths between pairs of proteins, and (iv) viewing the local neighbourhood of a given set of proteins.
Pathway Studio
An analytical solution for biological researchers and incorporates an expansive knowledge base of molecular facts that enable researchers to connect independent research findings to gain new insights, to analyze and interpret the results of biological experiments, and to build biological models to develop new hypotheses and to communicate and publish complex biological concepts. Incorporating compelling interactive graphics, Pathway Studio helps researchers work more rapidly and with more confidence than performing manual research through articles. It further helps avoid repeating previously conducted research, and can produce more compelling research conclusions from experimental process.
PyML / Python Machine Learning
An interactive object oriented framework for machine learning written in Python. PyML is a kernel method for predicting protein– protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. It provides tools for feature selection, model selection, syntax for combining classifiers and methods for assessing classifier performance.
InterPreTS / Interaction Prediction through Tertiary Structure
A web-based tool for predicting protein-protein interactions. Given a set of protein sequences (in fasta format), this tool will use BLAST to find homologues of known structure for all pairs (i.e. templates that can model each pair of sequences based on homology) and then evaluate the suitability of those templates for modelling the interaction. InterPreTS includes a useful interface for visualising molecular details of any predicted interaction.
A network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. A platform-independent and dependency- as well as installation-free implementation is provided that only requires little manual effort by the user.
IraPPA / Integrative Ranking of Protein Protein Assemblies
Enhances the identification of near-native structures when applied to four docking methods. IraPPA is based on methods developed for Internet search ranking and electoral voting to solve the problem of that atomic modeling of protein-protein interactions (ppi) requires the selection of near-native structures from a set of docked poses based on their calculable properties. It aims to select and combine physicochemical descriptors for ranking docked poses. The tool was applied independently to decoy structures from four state of the art docking programs, SwarmDock, pyDock, ZDOCK and SDOCK.
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