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

Ayman Yousif, Nizar Drou, Jillian Rowe, Mohammed Khalfan, Kristin C Gunsalus
doi: https://doi.org/10.1101/709980
Ayman Yousif
1NYU Abu Dhabi Center for Genomics & Systems Biology, Division of Biological Sciences, Abu Dhabi, United Arab Emirates
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Nizar Drou
1NYU Abu Dhabi Center for Genomics & Systems Biology, Division of Biological Sciences, Abu Dhabi, United Arab Emirates
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Jillian Rowe
1NYU Abu Dhabi Center for Genomics & Systems Biology, Division of Biological Sciences, Abu Dhabi, United Arab Emirates
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Mohammed Khalfan
2Center for Genomics & Systems Biology, Department of Biology, New York University, 10003 New York, United States
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Kristin C Gunsalus
1NYU Abu Dhabi Center for Genomics & Systems Biology, Division of Biological Sciences, Abu Dhabi, United Arab Emirates
2Center for Genomics & Systems Biology, Department of Biology, New York University, 10003 New York, United States
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  • For correspondence: kcg1@nyu.edu
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Abstract

Background 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 many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).

Results NASQAR 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.

Conclusions NASQAR 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Minor edits to deseq2shiny section and Seurat Wizards section. Added a new table for a comprehensive summary of the applications included in NASQAR. Minor edit to supplementary materials

  • http://nasqar.abudhabi.nyu.edu/

  • Abbreviations

    NASQAR
    Nucleic Acid SeQuence Analysis Resource;
    RNA-seq
    RNA sequencing;
    NGS
    Next Generation Sequencing;
    CRISPR
    Clustered Regularly Interspaced ShortPalindromic Repeats;
    GUI
    Graphical User Interface;
    PCA
    Principal Components Analysis;
    t-SNE
    t-Distributed Stochastic Neighbor Embedding;
    UMAP
    Uniform Manifold Approximation and Projection;
    QC
    Quality Control;
    UCSC
    University of California, Santa Cruz;
    rlog
    regularized logarithm transformation;
    vst
    variance stabilizing transformation;
    ORA
    Over-Representation Analysis;
    GSEA
    Gene Set Enrichment Analysis;
    GO
    Gene Ontology;
    KEGG
    Kyoto Encyclopedia of Genes and Genomes;
    START
    Shiny Transcriptome Analysis Resource Tool;
    Shaman
    SHiny application for Metagenomic ANalysis;
    csv
    comma separated values
  • Copyright 
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    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
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    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

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