The COVIDome Explorer researcher portal.
Ontology highlight
ABSTRACT:
SUBMITTER: Sullivan KD
PROVIDER: S-EPMC8316015 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. In order to expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients including matched analysis of the whole blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate here the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.
| S-EPMC7987038 | biostudies-literature
Project description:COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. In order to expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients including matched analysis of the whole blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate here the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.
2021-03-30 | ST001736 | MetabolomicsWorkbench
Project description:COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. In order to expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients including matched analysis of the whole blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate here the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.
2021-03-29 | ST001735 | MetabolomicsWorkbench
Project description:MotivationGiven the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices.Availability and implementationVOE is accessible at http://bcil.github.io/VOE/ CONTACT: agbiotec@gmail.comSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5860494 | biostudies-literature
Project description:Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.
| S-EPMC6013759 | biostudies-other
Project description:16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.
| S-EPMC9484756 | biostudies-literature
Project description:BACKGROUND:Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge. RESULTS:Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows. CONCLUSIONS:PAWER is freely available at https://biit.cs.ut.ee/pawer .
| S-EPMC7499988 | biostudies-literature
Project description:From a network medicine perspective, a disease is the consequence of perturbations on the interactome. These perturbations tend to appear in a specific neighbourhood on the interactome, the disease module, and modules related to phenotypically similar diseases tend to be located in close-by regions. We present LanDis, a freely available web-based interactive tool ( https://paccanarolab.org/landis ) that allows domain experts, medical doctors and the larger scientific community to graphically navigate the interactome distances between the modules of over 44 million pairs of heritable diseases. The map-like interface provides detailed comparisons between pairs of diseases together with supporting evidence. Every disease in LanDis is linked to relevant entries in OMIM and UniProt, providing a starting point for in-depth analysis and an opportunity for novel insight into the aetiology of diseases as well as differential diagnosis.
| S-EPMC10999415 | biostudies-literature
Project description:Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.
| S-EPMC9252786 | biostudies-literature
Project description:The region surrounding a protein, known as the surface of interaction or molecular surface, can provide valuable insight into its function. Unfortunately, due to the complexity of both their geometry and their surface fields, study of these surfaces can be slow and difficult and important features may be hard to identify. Here, we describe our GRaphical Abstracted Protein Explorer, or GRAPE, a web server that allows users to explore abstracted representations of proteins. These abstracted surfaces effectively reduce the level of detail of the surface of a macromolecule, using a specialized algorithm that removes small bumps and pockets, while preserving large-scale structural features. Scalar fields, such as electrostatic potential and hydropathy, are smoothed to further reduce visual complexity. This entirely new way of looking at proteins complements more traditional views of the molecular surface. GRAPE includes a thin 3D viewer that allows users to quickly flip back and forth between both views. Abstracted views provide a fast way to assess both a molecule's shape and its different surface field distributions. GRAPE is freely available at http://grape.uwbacter.org.
| S-EPMC2896102 | biostudies-literature