OASIS: web-based platform for exploring cancer multi-omics data

2015 ◽  
Vol 13 (1) ◽  
pp. 9-10 ◽  
Author(s):  
Julio Fernandez-Banet ◽  
Anthony Esposito ◽  
Scott Coffin ◽  
Istvan Boerner Horvath ◽  
Heather Estrella ◽  
...  
Keyword(s):  
Omics Data ◽  
Web Based

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Panomicon: A web-based environment for interactive, visual analysis of multi-omics data

Heliyon ◽  
2020 ◽  
Vol 6 (8) ◽  
pp. e04618
Author(s):  
Rodolfo S. Allendes Osorio ◽  
Johan T. Nyström-Persson ◽  
Yosui Nojima ◽  
Yuji Kosugi ◽  
Kenji Mizuguchi ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Omics Data ◽  
Web Based ◽  
Interactive Visual Analysis

scholarly journals MetaboMAPS: Pathway sharing and multi-omics data visualization in metabolic context

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 288
Author(s):  
Julia Koblitz ◽  
Dietmar Schomburg ◽  
Meina Neumann-Schaal
Keyword(s):  
Experimental Data ◽  
Systems Biology ◽  
Metabolic Pathways ◽  
Common Knowledge ◽  
Numerical Data ◽  
Omics Data ◽  
Web Based ◽  
Ongoing Research ◽  
Complex Interactions ◽  
Biology Research

Metabolic pathways are an important part of systems biology research since they illustrate complex interactions between metabolites, enzymes, and regulators. Pathway maps are drawn to elucidate metabolism or to set data in a metabolic context. We present MetaboMAPS, a web-based platform to visualize numerical data on individual metabolic pathway maps. Metabolic maps can be stored, distributed and downloaded in SVG-format. MetaboMAPS was designed for users without computational background and supports pathway sharing without strict conventions. In addition to existing applications that established standards for well-studied pathways, MetaboMAPS offers a niche for individual, customized pathways beyond common knowledge, supporting ongoing research by creating publication-ready visualizations of experimental data.

scholarly journals multiSLIDE: a web server for exploring connected elements of biological pathways in multi-omics data

2019 ◽  
Author(s):  
Soumita Ghosh ◽  
Abhik Datta ◽  
Hyungwon Choi
Keyword(s):  
Keyword Search ◽  
Data Sets ◽  
Omics Data ◽  
Web Based ◽  
Molecular Features ◽  
External Data ◽  
Cluster Data ◽  
Wide Range ◽  
Time Basis ◽  
Gene Ontologies

AbstractEmerging multi-omics experiments pose new challenges for exploration of quantitative data sets. We present multiSLIDE, a web-based interactive tool for simultaneous heatmap visualization of interconnected molecular features in multi-omics data sets. multiSLIDE operates by keyword search for visualizing biologically connected molecular features, such as genes in pathways and Gene Ontologies, offering convenient functionalities to rearrange, filter, and cluster data sets on a web browser in a real time basis. Various built-in querying mechanisms make it adaptable to diverse omics types, and visualizations are fully customizable. We demonstrate the versatility of the tool through three example studies, each of which showcases its applicability to a wide range of multi-omics data sets, ability to visualize the links between molecules at different granularities of measurement units, and the interface to incorporate inter-molecular relationship from external data sources into the visualization. Online and standalone versions of multiSLIDE are available at https://github.com/soumitag/multiSLIDE.

scholarly journals PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

2018 ◽  
Author(s):  
Rafael Hernández-de-Diego ◽  
Sonia Tarazona ◽  
Carlos Martínez-Mira ◽  
Leandro Balzano-Nogueira ◽  
Pedro Furió-Tarí ◽  
...  
Keyword(s):  
Data Analysis ◽  
Pathway Analysis ◽  
Feature Matching ◽  
Omics Data ◽  
Data Types ◽  
Web Based ◽  
Web Resource ◽  
Analysis Workflow ◽  
Pathway Diagrams ◽  
Molecular Layers

ABSTRACTThe increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental to understand interconnections across molecular layers and to fully leverage the biology discovery power offered by the multi-omics approach.We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information.Unlike other visualization tools, PaintOmics 3 covers a complete pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, etc. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at http://bioinfo.cipf.es/paintomics/.

scholarly journals i2dash: Creation of Flexible, Interactive and Web-based Dashboards for Visualization of Omics-pipeline Results

2020 ◽  
Author(s):  
Arsenij Ustjanzew ◽  
Jens Preussner ◽  
Mette Bentsen ◽  
Carsten Kuenne ◽  
Mario Looso
Keyword(s):  
Single Cell ◽  
Data Visualization ◽  
Large Scale ◽  
R Package ◽  
Cloud Services ◽  
Sequencing Analysis ◽  
Omics Data ◽  
Web Based ◽  
Automated Data Processing ◽  
Generic Design

AbstractData visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation and display of such visualizations. However, the combination of automated data processing pipelines handling omics data and dynamically generated, interactive dashboards is poorly solved. Here, we present i2dash, an R package intended to encapsulate functionality for programmatic creation of customized dashboards. It supports interactive and responsive (linked) visualizations across a set of predefined graphical layouts. i2dash addresses the needs of data analysts for a tool that is compatible and attachable to any R-based analysis pipeline, thereby fostering the separation of data visualization on one hand and data analysis tasks on the other hand. In addition, the generic design of i2dash enables data analysts to generate modular extensions for specific needs. As a proof of principle, we provide an extension of i2dash optimized for single-cell RNA-sequencing analysis, supporting the creation of dashboards for the visualization needs of single-cell sequencing experiments. Equipped with these features, i2dash is suitable for extensive use in large scale sequencing/bioinformatics facilities. Along this line, we provide i2dash as a containerized solution, enabling a straightforward large-scale deployment and sharing of dashboards using cloud services.i2dash is freely available via the R package archive CRAN.

scholarly journals i2dash: Creation of Flexible, Interactive and Web-based Dashboards for Visualization of Omics Data

2021 ◽  
Author(s):  
Arsenij Ustjanzew ◽  
Jens Preussner ◽  
Mette Bentsen ◽  
Carsten Kuenne ◽  
Mario Looso
Keyword(s):  
Omics Data ◽  
Web Based

scholarly journals Network- and Enrichment-based Inference of Phenotypes and Targets from large-scale Disease Maps

2021 ◽  
Author(s):  
Matti Hoch ◽  
Suchi Smita Gupta ◽  
Konstantin Cesnulevicius ◽  
David Lescheid ◽  
Myron Schultz ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Knowledge Bases ◽  
Easy Access ◽  
Omics Data ◽  
Rna Seq ◽  
Web Based ◽  
Inflammation Resolution ◽  
Disease Specific ◽  
Omics Data Integration

Disease maps have emerged as computational knowledge bases for exploring and modeling disease-specific molecular processes. By capturing molecular interactions, disease-associated processes, and phenotypes in standardized representations, disease maps provide a platform for applying bioinformatics and systems biology approaches. Applications range from simple map exploration to algorithm-driven target discovery and network perturbation. The web-based MINERVA environment for disease maps provides a platform to develop tools not only for mapping experimental data but also to identify, analyze and simulate disease-specific regulatory networks. We have developed a MINERVA plugin suite based on network topology and enrichment analyses that facilitate multi-omics data integration and enable in silico perturbation experiments on disease maps. We demonstrate workflows by analyzing two RNA-seq datasets on the Atlas of Inflammation Resolution (AIR). Our approach improves usability and increases the functionality of disease maps by providing easy access to available data and integration of self-generated data. It supports efficient and intuitive analysis of omics data, with a focus on disease maps.

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