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
NASQAR2 Updates:
R version has been updated to 4.2.3
GeneCountMerger:
- Added Wormbase ID to SYMBOL mapping.
deseq2shiny:
- Volcano plot
- Venn Diagram
- Input data now takes gene.id and gene.name together with Gene count data
ClusterProfShinyORA:
- Selective selection of interested pathways for plotting
These updates bring exciting new features and improvements to NASAQ2, enhancing its capabilities and usability.
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 80:3232 nyuadcorebio/nasqarall:nasqar
To run NASQAR locally on a different port (e.g. port 8083), use:
docker run -p 8083:3232 nyuadcorebio/nasqarall:nasqar
Access via web-browser on http://localhost/ or http://localhost:8083/ depending on port number used.
Apps
Bulk RNA
Single-Cell RNA
SeuratV3 Wizard
Epigenetics
ATACseqQC Shiny
Data manipulation and QC
Other
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
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Merge individual raw gene counts files into one csv file
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Convert gene ids to gene names & Remove duplicate genes
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Add pseudo counts
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URLs: Github Page
Merge FPKMs
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Merge individual FPKM files into one csv file.
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Convert gene ids to gene names & Remove duplicate genes
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Convert FPKMs to TPMS
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Add pseudo counts
Create Samples MetaTable
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Create meta table for samples/factors/conditions
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Convenient to use with RNAseq/DEApp (below)
RNAseq Tools
Single Cell
SeuratV3 Wizard
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R Shiny interface for Seurat (version 3.0-alpha) single-cell analysis library developed and maintained by NYUAD CGSB Bioinformatics Core
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Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
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URLs: Github Page
Bulk RNA
DESeq2 Shiny
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An interactive web application for differential expression analysis based on DESeq2
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DESeq2: an R package for Differential gene expression analysis based on the negative binomial distribution.
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URLs: Github Page
DEBrowser Shiny
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An interactive web application for differential expression analysis based on DEBrowser
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DEBrowser: an R package for Differential gene expression analysis
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URLs: Github Page
DEApp
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DEApp: an interactive web application of differential expression analysis
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This app uses edgeR, limma-voom, and DESeq2
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URLs: Github Page
START App: RNAseq
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The START App: R Shiny Transcriptome Analysis Resource Tool
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A web-based RNAseq analysis and visualization resource using edgeR and limma-voom
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URLs: Github Page
Gene Enrichment Tools
ClusterProfShinyGSEA (Gene Set Enrichment Analysis)
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A web-based application to perform Gene Set Enrichment Analysis (GSEA) using clusterProfiler and shiny R libraries
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This is based on clusterProfiler R package
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URLs: Github Page
ClusterProfShinyORA (Over-Representation Analysis)
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A web-based application to perform Over-Representation Analysis (ORA) using clusterProfiler and shiny R libraries
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This is based on clusterProfiler R package
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URLs: Github Page