Developmental, Stem Cell and Cancer Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands.
J Mammary Gland Biol Neoplasia. 2020 Dec;25(4):319-335. doi: 10.1007/s10911-020-09474-z. Epub 2021 Feb 24.
An increasing number of '-omics' datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific '-omics' datasets and thereby expand the in silico toolbox.
越来越多的“组学”数据集正由全球各地的实验室生成,这些数据集包含了大量尚未开发的数据。然而,并非每个科学家都有能力获得分析此类数据所需的资源和专业知识。幸运的是,越来越多的研究人员正致力于开发用户友好的在线应用程序,使研究人员能够使用和研究这些数据集。在这里,我们将举例说明这种方法的实用性。我们以 Wnt7b 表达调控为例,重点介绍乳腺生物学领域研究人员可使用的一些易于访问的工具和资源。我们展示了如何将它们用于基因调控机制的计算机分析,从而提出新的假说并为实验提供后续线索。我们还呼吁乳腺科学界共同努力,生成和共享更多组织特异性的“组学”数据集,从而扩展计算机工具包。