Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool.
Ontology highlight
ABSTRACT:
SUBMITTER: Armanious D
PROVIDER: S-EPMC7749048 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:UnlabelledRapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by "User Guide" in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user's own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.AvailabilityModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.
| S-EPMC4006751 | biostudies-literature
Project description:Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.
| S-EPMC6480976 | biostudies-other
Project description:BACKGROUND: It is useful to develop a tool that would effectively describe protein mutation matrices specifically geared towards the identification of mutations that produce either wanted or unwanted effects, such as an increase or decrease in affinity, or a predisposition towards misfolding. Here, we describe a tool where such mutations are efficiently identified, categorized and visualized. To categorize the mutations, amino acids in a mutation matrix are arranged according to one of three sets of physicochemical characteristics, namely hydrophilicity, size and polarizability, and charge and polarity. The magnitude and frequencies of mutations for an alignment are subsequently described using color information and scaling factors. RESULTS: To illustrate the capabilities of our approach, the technique is used to visualize and to compare mutation patterns in evolving sequences with diametrically opposite characteristics. Results show the emergence of distinct patterns not immediately discernible from the raw matrices. CONCLUSION: Our technique enables effective categorization and visualization of mutations by using specifically-arranged mutation matrices. This tool has a number of possible applications in protein engineering, notably in simplifying the identification of mutations and/or mutation trends that are associated with specific engineered protein characteristics and behavior.
| S-EPMC2390542 | biostudies-literature
Project description:Motivation:Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered. Results:In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g. GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples. Availability and implementation:GENEASE can be accessed freely at http://research.cchmc.org/mershalab/GENEASE/login.html. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6137982 | biostudies-literature
Project description:As long as the structural study of molecular mechanisms requires multiple molecular dynamics reflecting contrasted bioactive states, the subsequent analysis of molecular interaction networks remains a bottleneck to be fairly treated and requires a user-friendly 3D view of key interactions. Structural Interaction Network Analysis Protocols (SINAPs) is a proprietary python tool developed to (i) quickly solve key interactions able to distinguish two protein states, either from two sets of molecular dynamics simulations or from two crystallographic structures, and (ii) render a user-friendly 3D view of these key interactions through a plugin of UCSF Chimera, one of the most popular open-source viewing software for biomolecular systems. Through two case studies, glucose transporter-1 (GLUT-1) and A2A adenosine receptor (A2AR), SINAPs easily pinpointed key interactions observed experimentally and relevant for their bioactivities. This very effective tool was thus applied to identify the amino acids involved in the molecular enzymatic mechanisms ruling the activation of an immunomodulator drug candidate, P28 glutathione-S-transferase (P28GST). SINAPs is freely available at https://github.com/ParImmune/SINAPs.
| S-EPMC8966674 | biostudies-literature
Project description:MotivationGenetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer.ResultsThe REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and 'B allele' frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or 'mirrored subclonal allelic imbalance', is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization.Availability and implementationRECUR is available as an R application. Source code and documentation are available at scheet.org.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6596882 | biostudies-literature
Project description:Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.
| S-EPMC10982686 | biostudies-literature
Project description:Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.
| S-EPMC9025992 | biostudies-literature
Project description:BackgroundProtein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es.ResultsThe tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein.ConclusionThe tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks.
| S-EPMC2762066 | biostudies-literature
Project description:Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.
| S-EPMC10719354 | biostudies-literature