Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Campus Box G-E5, Providence, RI 02912, USA.
BMC Bioinformatics. 2014 Feb 5;15:41. doi: 10.1186/1471-2105-15-41.
BACKGROUND: The advent of next-generation DNA sequencing platforms has revolutionized molecular microbial ecology by making the detailed analysis of complex communities over time and space a tractable research pursuit for small research groups. However, the ability to generate 10⁵-10⁸ reads with relative ease brings with it many downstream complications. Beyond the computational resources and skills needed to process and analyze data, it is difficult to compare datasets in an intuitive and interactive manner that leads to hypothesis generation and testing. RESULTS: We developed the free web service VAMPS (Visualization and Analysis of Microbial Population Structures, http://vamps.mbl.edu) to address these challenges and to facilitate research by individuals or collaborating groups working on projects with large-scale sequencing data. Users can upload marker gene sequences and associated metadata; reads are quality filtered and assigned to both taxonomic structures and to taxonomy-independent clusters. A simple point-and-click interface allows users to select for analysis any combination of their own or their collaborators' private data and data from public projects, filter these by their choice of taxonomic and/or abundance criteria, and then explore these data using a wide range of analytic methods and visualizations. Each result is extensively hyperlinked to other analysis and visualization options, promoting data exploration and leading to a greater understanding of data relationships. CONCLUSIONS: VAMPS allows researchers using marker gene sequence data to analyze the diversity of microbial communities and the relationships between communities, to explore these analyses in an intuitive visual context, and to download data, results, and images for publication. VAMPS obviates the need for individual research groups to make the considerable investment in computational infrastructure and bioinformatic support otherwise necessary to process, analyze, and interpret massive amounts of next-generation sequence data. Any web-capable device can be used to upload, process, explore, and extract data and results from VAMPS. VAMPS encourages researchers to share sequence and metadata, and fosters collaboration between researchers of disparate biomes who recognize common patterns in shared data.
背景:下一代 DNA 测序平台的出现彻底改变了分子微生物生态学,使对时间和空间上的复杂群落进行详细分析成为小型研究小组可行的研究目标。然而,相对轻松地生成 10⁵-10⁸ 个读取序列的能力带来了许多下游复杂性。除了处理和分析数据所需的计算资源和技能外,以直观和交互式的方式比较数据集也很困难,这无法生成假设并进行检验。
结果:我们开发了免费的网络服务 VAMPS(微生物种群结构的可视化和分析,http://vamps.mbl.edu)来解决这些挑战,并为从事大规模测序数据项目的个人或合作研究小组提供便利。用户可以上传标记基因序列和相关元数据;读取序列经过质量过滤,并分配给分类结构和分类无关的聚类。通过简单的点击和选择界面,用户可以选择分析他们自己或合作者的私人数据以及公共项目的数据的任意组合,通过他们选择的分类和/或丰度标准对这些数据进行过滤,然后使用广泛的分析方法和可视化来探索这些数据。每个结果都与其他分析和可视化选项进行了广泛的超链接,促进了数据探索,有助于深入了解数据关系。
结论:VAMPS 允许使用标记基因序列数据的研究人员分析微生物群落的多样性和群落之间的关系,在直观的可视化环境中探索这些分析,并下载数据、结果和图像以供发表。VAMPS 免除了单个研究小组在处理、分析和解释大量下一代序列数据方面所需的大量计算基础设施和生物信息学支持方面的投资。任何具有网络功能的设备都可以用于上传、处理、探索和提取 VAMPS 中的数据和结果。VAMPS 鼓励研究人员共享序列和元数据,并促进不同生物群落的研究人员之间的合作,这些研究人员在共享数据中发现了共同的模式。
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