Department of Immunotechnology, Lund University, Lund, Sweden.
Science for Life Laboratory, National Bioinformatics Infrastructure Sweden (NBIS), Lund University, Lund, Sweden.
BMC Bioinformatics. 2021 Mar 4;22(1):107. doi: 10.1186/s12859-021-04043-5.
Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose.
The OmicLoupe software was developed to facilitate visual data exploration and provides more than 15 interactive cross-dataset visualizations for omics data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three different studies, where it allowed for detection of both technical data limitations and biological trends across different omic layers. An example is an analysis of SARS-CoV-2 infection based on two previously published studies, where OmicLoupe facilitated the identification of gene products with consistent expression changes across datasets at both the transcript and protein levels.
OmicLoupe provides fast exploration of omics data with tailored visualizations for comparisons within and across data layers. The interactive visualizations are highly informative and are expected to be useful in various analyses of both newly generated and previously published data. OmicLoupe is available at quantitativeproteomics.org/omicloupe.
跨多个组学数据集探索基因产物行为可以发现数据中的技术限制,并揭示生物学趋势。然而,这种探索具有挑战性,因为需要针对特定目的的可视化工具。
OmicLoupe 软件旨在促进可视化数据探索,并为组学数据提供了超过 15 种交互式跨数据集可视化功能。它将可视化扩展到多个数据集,以进行质量控制、统计比较以及重叠和相关性分析,同时允许快速检查和下载选定的特征。OmicLoupe 的使用在三个不同的研究中得到了证明,它可以在不同的组学层面上检测到技术数据限制和生物学趋势。一个例子是基于两项已发表的研究对 SARS-CoV-2 感染的分析,OmicLoupe 促进了在转录和蛋白质水平上在数据集之间具有一致表达变化的基因产物的鉴定。
OmicLoupe 提供了针对特定目的的快速探索组学数据的可视化工具,可以在数据层内和跨数据层进行比较。交互式可视化功能非常有信息量,预计在分析新生成和已发表的数据时将非常有用。OmicLoupe 可在 quantitativeproteomics.org/omicloupe 上获得。