scholarly journals GREIN: An Interactive Web Platform for Reanalyzing GEO RNA-seq Data

2018 ◽  
Author(s):  
Naim Al Mahi ◽  
Mehdi Fazel Najafabadi ◽  
Marcin Pilarczyk ◽  
Michal Kouril ◽  
Mario Medvedovic
Keyword(s):  
Gene Expression ◽  
User Interfaces ◽  
Web Application ◽  
Statistical Power ◽  
Functional Characterization ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Link Type ◽  
Front End ◽  
User Friendly

ABSTRACTThe vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein.

scholarly journals Biochat: a database for natural language processing of Gene Expression Omnibus data

2018 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
Vsevolod Dyomkin ◽  
Kasra A. Vand ◽  
Themistocles Assimes ◽  
Or Gozani
Keyword(s):  
Gene Expression ◽  
Language Processing ◽  
Web Application ◽  
Gene Expression Omnibus ◽  
Omics Data ◽  
Textual Information ◽  
Link Type ◽  
Different Types ◽  
User Friendly ◽  
Omics Data Integration

AbstractA biological dataset’s metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at: http://www.biochat.ai. Biochat source code is hosted at: https://github.com/Bohdan-Khomtchouk/Bio-chat.Database URLhttps://github.com/Bohdan-Khomtchouk/Bio-chat

scholarly journals MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

2015 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
James R. Hennessy ◽  
Claes Wahlestedt
Keyword(s):  
Web Application ◽  
Differential Expression Analysis ◽  
Dynamic Network ◽  
Principal Component ◽  
Rna Seq ◽  
Software Suite ◽  
Software Analysis ◽  
Link Type ◽  
R Shiny ◽  
User Friendly

AbstractWe propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

scholarly journals GEO2RNAseq: An easy-to-use R pipeline for complete pre-processing of RNA-seq data

2019 ◽  
Author(s):  
Bastian Seelbinder ◽  
Thomas Wolf ◽  
Steffen Priebe ◽  
Sylvie McNamara ◽  
Silvia Gerber ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Species ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Sequencing Data ◽  
Interacting Species ◽  
Link Type ◽  
Fastq Format ◽  
Standard Tool ◽  
Processing Steps

ABSTRACTIn transcriptomics, the study of the total set of RNAs transcribed by the cell, RNA sequencing (RNA-seq) has become the standard tool for analysing gene expression. The primary goal is the detection of genes whose expression changes significantly between two or more conditions, either for a single species or for two or more interacting species at the same time (dual RNA-seq, triple RNA-seq and so forth). The analysis of RNA-seq can be simplified as many steps of the data pre-processing can be standardised in a pipeline.In this publication we present the “GEO2RNAseq” pipeline for complete, quick and concurrent pre-processing of single, dual, and triple RNA-seq data. It covers all pre-processing steps starting from raw sequencing data to the analysis of differentially expressed genes, including various tables and figures to report intermediate and final results. Raw data may be provided in FASTQ format or can be downloaded automatically from the Gene Expression Omnibus repository. GEO2RNAseq strongly incorporates experimental as well as computational metadata. GEO2RNAseq is implemented in R, lightweight, easy to install via Conda and easy to use, but still very flexible through using modular programming and offering many extensions and alternative workflows.GEO2RNAseq is publicly available at https://anaconda.org/xentrics/r-geo2rnaseq and https://bitbucket.org/thomas_wolf/geo2rnaseq/overview, including source code, installation instruction, and comprehensive package documentation.

scholarly journals GENAVi: a shiny web application for gene expression normalization, analysis and visualization

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alberto Luiz P. Reyes ◽  
Tiago C. Silva ◽  
Simon G. Coetzee ◽  
Jasmine T. Plummer ◽  
Brian D. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Data Analyst ◽  
Reproducible Analysis ◽  
Programming Knowledge ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Feature Count

Abstract Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

scholarly journals OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1165 ◽  
Author(s):  
Rui Li ◽  
Kai Hu ◽  
Haibo Liu ◽  
Michael R. Green ◽  
Lihua Julie Zhu
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Differential Analysis ◽  
Rna Seq ◽  
Data Quality Control ◽  
Private And Public

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.

scholarly journals A curated collection of transcriptome datasets to investigate the molecular mechanisms of immunoglobulin E-mediated atopic diseases

2019 ◽  
Author(s):  
Susie S. Y. Huang ◽  
Fatima Al Ali ◽  
Sabri Boughorbel ◽  
Mohammed Toufiq ◽  
Damien Chaussabel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Immunoglobulin E ◽  
Gene Expression Omnibus ◽  
Atopic Diseases ◽  
Sample Number ◽  
Link Type ◽  
Good Concordance

ABSTRACTPrevalence of allergies has reached ~50% of industrialized populations and with children under ten being the most susceptible. However, the combination of the complexity of atopic allergy susceptibility/development and environmental factors has made identification of gene biomarkers challenging. The amount of publicly accessible transcriptomic data presents an unprecedented opportunity for mechanistic discoveries and validation of complex disease signatures across studies. However, this necessitates structured methodologies and visual tools for the interpretation of results. Here, we present a curated collection of transcriptomic datasets relevant to immunoglobin E (IgE)-mediated atopic diseases (ranging from allergies to primary immunodeficiencies). 30 datasets from the Gene Expression Omnibus (GEO), encompassing 1761 transcriptome profiles, were made available on the Gene Expression Browser (GXB), an online and open-source web application that allows for the query, visualization, and annotation of metadata. The thematic compositions, disease categories, sample number, and platforms of the collection are described. Ranked gene lists and sample grouping are used to facilitate data visualization/interpretation and are available online via GXB (http://ige.gxbsidra.org/dm3/geneBrowser/list). Dataset validation using associated publications showed good concordance in GXB gene expression trend and fold-change.Database URL: http://ige.gxbsidra.org/dm3/geneBrowser/list

scholarly journals CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Gene Expression Omnibus ◽  
Cellular Heterogeneity ◽  
Data Sets ◽  
Rna Seq ◽  
Individual Gene ◽  
Cancer Data ◽  
The Web

Abstract Background Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. Results We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI’s Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application. Conclusion CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.

scholarly journals ProkSeq for complete analysis of RNA-seq data from prokaryotes

2020 ◽  
Author(s):  
A K M Firoj Mahmud ◽  
Soumyadeep Nandi ◽  
Maria Fällman
Keyword(s):  
Gene Expression ◽  
Pathogenic Bacteria ◽  
Complete Analysis ◽  
Rna Seq ◽  
Link Type ◽  
Eukaryotic Genes ◽  
Differential Gene ◽  
User Friendly ◽  
Multiple Samples

AbstractSummarySince its introduction, RNA-seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for assessing gene expression have been designed around the structures of eukaryotic genes. There are a few stand-alone tools designed for prokaryotes, and they require improvement. A well-defined pipeline for prokaryotes that includes all the necessary tools for quality control, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results, and it produces publication-quality figures.Availability and implementationProkSeq is implemented in Python and is published under the ISC open source license. The tool and a detailed user manual are hosted at Docker: https://hub.docker.com/repository/docker/snandids/prokseq-v2.1, Anaconda: https://anaconda.org/snandiDS/prokseq; Github: https://github.com/snandiDS/prokseq.

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals Using SWISH to Realize Interactive Web-based Tutorials for Logic-based Languages

2019 ◽  
Vol 19 (2) ◽  
pp. 229-261 ◽  
Author(s):  
JAN WIELEMAKER ◽  
FABRIZIO RIGUZZI ◽  
ROBERT A. KOWALSKI ◽  
TORBJÖRN LAGER ◽  
FARIBA SADRI ◽  
...  
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Web Application ◽  
Web Server ◽  
Educational Material ◽  
Graphical User Interfaces ◽  
Probabilistic Logic ◽  
Programming Environments ◽  
Web Based ◽  
Front End

AbstractProgramming environments have evolved from purely text based to using graphical user interfaces, and now we see a move toward web-based interfaces, such as Jupyter. Web-based interfaces allow for the creation of interactive documents that consist of text and programs, as well as their output. The output can be rendered using web technology as, for example, text, tables, charts, or graphs. This approach is particularly suitable for capturing data analysis workflows and creating interactive educational material. This article describes SWISH, a web front-end for Prolog that consists of a web server implemented in SWI-Prolog and a client web application written in JavaScript. SWISH provides a web server where multiple users can manipulate and run the same material, and it can be adapted to support Prolog extensions. In this article we describe the architecture of SWISH, and describe two case studies of extensions of Prolog, namely Probabilistic Logic Programming and Logic Production System, which have used SWISH to provide tutorial sites.

Sign in / Sign up

Export Citation Format

Share Document