BioJupies: Automated Generation of Interactive Notebooks for RNA-seq Data Analysis in the Cloud

AbstractInteractive notebooks can make bioinformatics data analyses more transparent, accessible and reusable. However, creating notebooks requires computer programming expertise. Here we introduce BioJupies, a web server that enables automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses. Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, their gene expression tables, or fetch data from >5,500 published studies containing >250,000 preprocessed RNA-seq samples. Generated notebooks have executable code of the entire pipeline, rich narrative text, interactive data visualizations, and differential expression and enrichment analyses. The notebooks are permanently stored in the cloud and made available online through a persistent URL. The notebooks are downloadable, customizable, and can run within a Docker container. By providing an intuitive user interface for notebook generation for RNA-seq data analysis, starting from the raw reads, all the way to a complete interactive and reproducible report, BioJupies is a useful resource for experimental and computational biologists. BioJupies is freely available as a web-based application from:http://biojupies.cloudand as a Chrome extension from theChrome Web Store.

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BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

Cell Systems ◽

10.1016/j.cels.2018.10.007 ◽

2018 ◽

Vol 7 (5) ◽

pp. 556-561.e3 ◽

Cited By ~ 61

Author(s):

Denis Torre ◽

Alexander Lachmann ◽

Avi Ma’ayan

Keyword(s):

Data Analysis ◽

Rna Seq ◽

Automated Generation

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

Nucleic Acids Research ◽

10.1093/nar/gkaa412 ◽

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.

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Facilitating Collaborative Analysis in SWAN

EPJ Web of Conferences ◽

10.1051/epjconf/201921407022 ◽

2019 ◽

Vol 214 ◽

pp. 07022

Author(s):

Enrico Bocchi ◽

Diogo Castro ◽

Hugo Gonzalez ◽

Massimo Lamanna ◽

Pere Mato ◽

...

Keyword(s):

Data Analysis ◽

Special Kind ◽

Service Model ◽

Web Browser ◽

Web Based ◽

Single File ◽

Cloud Storage Service ◽

Collaborative Analysis ◽

Interactive Data ◽

The Ideal

SWAN (Service for Web-based ANalysis) is a CERN service that allows users to perform interactive data analysis in the cloud, in a “software as a service” model. It is built upon the widely-used Jupyter notebooks, allowing users to write - and run - their data analysis using only a web browser. By connecting to SWAN, users have immediate access to storage, software and computing resources that CERN provides and that they need to do their analyses. Besides providing an easier way of producing scientific code and results, SWAN is also a great tool to create shareable content. From results that need to be reproducible, to tutorials and demonstrations for outreach and teaching, Jupyter notebooks are the ideal way of distributing this content. In one single file, users can include their code, the results of the calculations and all the relevant textual information. By sharing them, it allows others to visualize, modify, personalize or even re-run all the code. In that sense, this paper describes the efforts made to facilitate sharing in SWAN. Given the importance of collaboration in our scientific community, we have brought the sharing functionality from CERNBox, CERN’s cloud storage service, directly inside SWAN. SWAN users have available a new and redesigned interface where theycan share “Projects”: a special kind of folder containing notebooks and other files, e.g., like input datasets and images. When a user shares a Project with some other users, the latter can immediately see andwork with the contents of that project from SWAN.

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MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data

Biomarker Insights ◽

10.4137/bmi.s25132 ◽

2015 ◽

Vol 10 ◽

pp. BMI.S25132 ◽

Cited By ~ 68

Author(s):

Jun-ichi Satoh ◽

Yoshihiro Kino ◽

Shumpei Niida

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Data Analysis ◽

Small Rna ◽

Small Rna Sequencing ◽

Rna Seq ◽

Analysis Pipeline ◽

Web Based ◽

Public Data ◽

Data Analysis Pipeline

Background Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious. Methods To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath. Results By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28–3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186–5p, miR-425–5p, miR-550a-5p, miR-1468, miR-4781–3p, miR-5001–3p, and miR-6513–3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17–3p, miR-29b-3p, miR-98–5p, miR-144–5p, miR-148a-3p, miR-502–3p, miR-660–5p, miR-1294, and miR-3200–3p. DIANA miRPath indicated that miRNA-regulated pathways potentially down– regulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication. Conclusions The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA–Seq data.

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BrowserGenome.org: web-based RNA-seq data analysis and visualization

Nature Methods ◽

10.1038/nmeth.3615 ◽

2015 ◽

Vol 12 (11) ◽

pp. 1001-1001 ◽

Cited By ~ 6

Author(s):

Jonathan L Schmid-Burgk ◽

Veit Hornung

Keyword(s):

Data Analysis ◽

Rna Seq ◽

Web Based

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Differential Expression Gene Explorer (DrEdGE): A tool for generating interactive online data visualizations for exploration of quantitative transcript abundance datasets

10.1101/618439 ◽

2019 ◽

Author(s):

Sophia C. Tintori ◽

Patrick Golden ◽

Bob Goldstein

Keyword(s):

Data Sharing ◽

Transcript Abundance ◽

Rna Seq ◽

Online Data ◽

Web Based ◽

Neuronal Tissue ◽

C Elegans ◽

Differential Expression Gene ◽

Public Data ◽

Data Visualizations

AbstractAs the scientific community becomes increasingly interested in data sharing, there is a growing need for tools that facilitate the querying of public data. Mining of RNA-seq datasets, for example, has value to many biomedical researchers, yet is often effectively inaccessible to non-genomicist experts, even when the raw data are available. Here we present DrEdGE (dredge.bio.unc.edu), a free Web-based tool that facilitates data sharing between genomicists and their colleagues. The DrEdGE software guides genomicists through easily creating interactive online data visualizations, which colleagues can then explore and query according to their own conditions to discover genes, samples, or patterns of interest. We demonstrate DrEdGE’s features with three example websites we generated from publicly available datasets—human neuronal tissue, mouse embryonic tissue, and a C. elegans embryonic series. DrEdGE increases the utility of large genomics datasets by removing the technical obstacles that prevent interested parties from exploring the data independently.

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FaDA: A Shiny web application to accelerate common lab data analyses

10.1101/2020.04.23.055673 ◽

2020 ◽

Author(s):

Yodit Feseha ◽

Quentin Moiteaux ◽

Estelle Geffard ◽

Gérard Ramstein ◽

Sophie Brouard ◽

...

Keyword(s):

Principal Component Analysis ◽

Data Analysis ◽

Rna Sequencing ◽

Quantitative Pcr ◽

Principal Component ◽

Component Analysis ◽

Receiver Operating Curve ◽

Web Based ◽

Data Analyses ◽

Receiver Operating

AbstractBackgroundWeb-based data analysis and visualization tools are mostly designed for specific purposes, such as data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, limited efforts have been made to develop tools designed for data of common laboratory data for non-computational scientists. The importance of such web-based tool is stressed by the current increased samples capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA.ResultsWe provide a web-based application FaDA, developed with the R Shiny package providing users to perform statistical group comparisons, including parametric and non-parametric tests, with multiple testing corrections suitable for most of the standard wet-lab analyses. FaDA provides data visualization such as heatmap, principal component analysis (PCA) and receiver operating curve (ROC). Calculations are performed through the R language.ConclusionsFaDA application provides a free and intuitive interface allowing biologists without bioinformatic skills to easily and quickly perform common lab data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/FadaAbbreviationsAUC: Area Under the Curve; FaDA: Fast Data Analysis; GEO: Gene Expression Omnibus; ELISA: enzyme-linked immunosorbent assay; PCA: Principal Component Analysis; qPCR: quantitative PCR; ROC: Receiver Operating Curve.

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NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

10.1101/709980 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ayman Yousif ◽

Nizar Drou ◽

Jillian Rowe ◽

Mohammed Khalfan ◽

Kristin C Gunsalus

Keyword(s):

New York ◽

Data Analysis ◽

Open Source ◽

High Throughput ◽

High Throughput Sequencing ◽

Web Applications ◽

Rna Seq ◽

Sequencing Data ◽

Web Based ◽

Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

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An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

Water ◽

10.3390/w12102928 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2928

Author(s):

Jeffrey D. Walker ◽

Benjamin H. Letcher ◽

Kirk D. Rodgers ◽

Clint C. Muhlfeld ◽

Vincent S. D’Angelo

Keyword(s):

Decision Making ◽

Case Studies ◽

Data Visualization ◽

Large Scale ◽

Research Management ◽

Web Based ◽

Environmental Models ◽

Potential Benefits ◽

Interactive Data ◽

Data Visualizations

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

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SC1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis

10.1101/2021.03.19.435534 ◽

2021 ◽

Author(s):

Marmar Moussa ◽

Ion Mandoiu

Keyword(s):

Data Analysis ◽

Single Cell ◽

Differential Expression Analysis ◽

Analysis Tool ◽

Rna Seq ◽

Web Based ◽

Inverse Document Frequency ◽

Sequencing Technologies ◽

Document Frequency ◽

Novel Method

Single cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. Here, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on Term-Frequency Inverse-Document-Frequency (TF-IDF) scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single cell omics data modalities like TCR-Seq and supports several single cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

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