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). 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. 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.

中文翻译：

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NASQAR：基于Web的平台，用于高通量测序数据分析和可视化。

随着高通量测序应用的不断发展，基于序列的数据的数量和种类的迅速增长要求开发新的软件库和工具来进行数据分析和可视化。通常，有效使用这些工具需要许多研究人员以外的计算技能。为了缓解这种计算障碍，我们创建了一个基于Web的动态平台NASQAR（核酸序列分析资源）。NASQAR提供了一系列自定义和公开可用的开源Web应用程序，这些应用程序广泛使用了各种R包来提供交互式数据分析和可视化。该平台可从http://nasqar.abudhabi.nyu.edu/公开访问。开源代码位于GitHub上的https://github.com/nasqar/NASQAR上，该系统也可以作为Docker映像在https：//hub.docker.com/r/aymanm/nasqarall。NASQAR是纽约大学阿布扎比分校和纽约大学纽约基因组与系统生物学中心的核心生物信息学团队之间的合作。NASQAR为非编程专家提供了一个多功能而直观的工具箱，可以轻松高效地交互地浏览，分析和可视化其转录组学数据。当前可用于各种应用的流行工具，包括转录组数据预处理，RNA序列分析（包括单细胞RNA序列），元基因组学和基因富集。并以交互方式可视化其转录组学数据。当前可用于各种应用的流行工具，包括转录组数据预处理，RNA序列分析（包括单细胞RNA序列），元基因组学和基因富集。并以交互方式可视化其转录组学数据。当前可用于各种应用的流行工具，包括转录组数据预处理，RNA序列分析（包括单细胞RNA序列），元基因组学和基因富集。