背景:高通量转录组研究的一个主要挑战是将数据以可解释的格式呈现给研究人员。在许多情况下,这些研究的输出是基因列表,然后检查丰富的生物学概念。帮助研究人员解释大型基因数据集的一种方法是使用eGIFT文本挖掘系统将从生物医学文献中获得的基因和信息术语(iTerm)相关联。然而,检查iTerm和基因对的大型列表是一项艰巨的任务。
结果:我们开发了WebGIVI,基于Web的交互式可视化工具(http://raven。anr.udel.edu/webgivi/)探索基因:iTerm对。WebGIVI是通过Cytoscape和数据驱动文档JavaScript库构建的,可用于将基因与iTerms联系起来,然后可视化基因和iTerm对。WebGIVI可以接受用于检索基因符号的基因列表和相应的iTerm列表。可以提交此列表以使用两种不同的方法可视化基因iTerm对:概念图或Cytoscape网络图。此外,WebGIVI还支持上传和可视化任何两列标签分隔的数据。
结论:WebGIVI提供了基因和iTerms的交互式集成网络图,允许过滤,排序,和分组,这可以帮助生物学家根据输入的基因列表发展假设。此外,WebGIVI可以可视化数百个节点并生成高分辨率图像,这对大多数研究出版物都很重要。源代码可以在https://github.com/sunliang3361/WebGIVI免费下载。WebGIVI教程可在http://raven获得。anr.udel.edu/webgivi/tutorial.php。
BACKGROUND: A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task.
RESULTS: We have developed WebGIVI, an interactive web-based visualization tool ( http://raven.anr.udel.edu/webgivi/ ) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data.
CONCLUSIONS: WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI . The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php .