Overview
This portal hosts a number of web-based Bioinformatics analysis and visualization apps.
Users can either run apps Online or download/run on a local machine
GitHub
Abstract
As 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 the average researcher.
To lower this computational barrier, we have created a dynamic web-based platform, NASQAR. It provides an intuitive interface that allows users to easily and efficiently explore their data in an interactive way using popular tools for a variety of applications, including Transcriptome Data Preprocessing, RNAseq Analysis (including Single-cell RNAseq), Metagenomics, and Gene Enrichment
Data Privacy
Although data uploaded for analysis on the online instance of NASQAR (at http://nasqar.abudhabi.nyu.edu/) is by default discarded after a users session ends, this does not guarantee total data privacy. In cases where data privacy is a concern (e.g. patient or pilot data), it is recommended that NASQAR is deployed on a local intranet for private users, or on a personal computer. A Docker image of NASQAR is publicly available through DockerHub and can be used to deploy the application seamlessly on any system with Docker installed, whether a local computer, a public internet server, or a private server (e.g. research institutions intranet).
Run NASQAR on local computer/server (Docker)
Prerequisite: Docker (version >= 17.03.0-ce)
To run NASQAR locally on port 80, use the following docker command:
docker run -p 80:80 aymanm/nasqarall:nasqar
To run NASQAR locally on a different port (e.g. port 8083), use:
docker run -p 8083:80 aymanm/nasqarall:nasqar
Access via web-browser on http://localhost/ or http://localhost:8083/ depending on port number used.
Apps
Enrichment
Citation
Lastly, if you use any of the apps on our portal as part of a publication, please remember to add the appropriate NASQAR citation, as follows:
NASQAR: A web-based platform for High-throughput sequencing data analysis and visualization
Ayman Yousif, Nizar Drou, Jillian Rowe, Mohammed Khalfan, Kristin C. Gunsalus
bioRxiv 709980; doi: https://doi.org/10.1101/709980
Also make sure to add appropriate citation for the open source apps we are hosting which are always clearly displayed on the individual app pages or GitHub
Data Pre-processing Tools
Gene Count Merger
Merge individual raw gene counts files into one csv file
Convert gene ids to gene names & Remove duplicate genes
Add pseudo counts
URLs: Github Page
Merge FPKMs
Merge individual FPKM files into one csv file.
Convert gene ids to gene names & Remove duplicate genes
Convert FPKMs to TPMS
Add pseudo counts
Create Samples MetaTable
Create meta table for samples/factors/conditions
Convenient to use with RNAseq/DEApp (below)
RNAseq Tools
Single Cell
Seurat Wizard
R Shiny interface for Seurat single-cell analysis library developed and maintained by NYUAD CGSB Bioinformatics Core
Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
URLs: Github Page
SeuratV3 Wizard
R Shiny interface for Seurat (version 3.0-alpha) single-cell analysis library developed and maintained by NYUAD CGSB Bioinformatics Core
Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
URLs: Github Page
Bulk RNA
DESeq2 Shiny
An interactive web application for differential expression analysis based on DESeq2
DESeq2: an R package for Differential gene expression analysis based on the negative binomial distribution.
URLs: Github Page
START App: RNAseq
The START App: R Shiny Transcriptome Analysis Resource Tool
A web-based RNAseq analysis and visualization resource using edgeR and limma-voom
URLs: Github Page
DEApp
DEApp: an interactive web application of differential expression analysis
This app uses edgeR, limma-voom, and DESeq2
URLs: Github Page
Gene Enrichment Tools
ClusterProfShinyGSEA (Gene Set Enrichment Analysis)
A web-based application to perform Gene Set Enrichment Analysis (GSEA) using clusterProfiler and shiny R libraries
This is based on clusterProfiler R package
URLs: Github Page
ClusterProfShinyORA (Over-Representation Analysis)
A web-based application to perform Over-Representation Analysis (ORA) using clusterProfiler and shiny R libraries
This is based on clusterProfiler R package
URLs: Github Page
Metagenomics Tools
Shaman
SHAMAN is a SHiny application for Metagenomic ANalysis including the normalization, the differential analysis and mutiple visualization
URLs: Github Page
Epidemiology Covid-19
Covid-19 Tracker
This app provides up-to-date visualizations of data tracking the global spread of Coronavirus Disease 2019 (COVID-19) and easy access to the World Health Organization's daily situation reports on it
Author: Jay Ulfelder
URLs: Github Page
Coronavirus Dashboard
Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic.
Author: Rami Krispin
URLs: Github Page