Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
University of Pennsylvania, Philadelphia, PA, USA.
Sci Data. 2022 Jun 16;9(1):343. doi: 10.1038/s41597-022-01431-1.
Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 128 studies and 1,322 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CF-Seq: http://scangeo.dartmouth.edu/CFSeq/ ).
研究囊性纤维化 (CF) 病原体的研究人员已经生成了许多可在基因表达综合数据库 (GEO) 中获得的 RNA-seq 数据集。尽管这些研究是公开的，但要比较相似的研究、可视化研究中的基因表达模式以及使用已发表的数据生成新的实验假设，仍需要大量的计算专业知识和人工努力。此外，通过与领域相关的属性（如菌株、处理或介质）来过滤可用的研究或评估研究人员如何评估特定基因对各种实验条件的反应非常困难。为了减少数据分析的这些障碍，我们开发了一个名为 CF-Seq 的 R Shiny 应用程序，它可与来自 13 种临床相关 CF 病原体的 128 项研究和 1322 个个体样本的摘要一起使用。该应用程序允许用户通过实验因素过滤研究，并通过点击按钮查看复杂的差异基因表达分析。在这里，我们展示了一系列用例，证明该应用程序是生成新假设的有用且高效的工具。（CF-Seq：http://scangeo.dartmouth.edu/CFSeq/）。
