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

https://github.com/nasqar/nasqar

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


Pre-Processing

Gene Count Merger

Gene Count Merger

Launch


Merge FPKMs

mergeFPKMs

Launch

Bulk RNA

DESeq2 Shiny

DESeq2 Shiny

Launch


STARTApp

STARTapp

Launch

Other

Shaman (Metagenomics)

Shaman

Launch


DEApp (RNASeq)

DEApp

Launch

Single-Cell RNA

SeuratV3 Wizard

SeuratV3 Wizard

Launch


Seurat Wizard

Seurat Wizard

Launch

Enrichment

clusterProfiler(GSEA)

clusterProfiler(GSEA)

Launch


clusterProfiler(ORA)

clusterProfiler(ORA)

Launch


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

  • Main packages: VSEARCH, DESeq2

  • URLs: Github Page