Gerstner Nico, Kehl Tim, Lenhof Kerstin, Eckhart Lea, Schneider Lara, Stöckel Daniel, Backes Christina, Meese Eckart, Keller Andreas, Lenhof Hans-Peter
Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany.
Healthcare Digital & Data, Merck Healthcare KGaA, Darmstadt, Germany.
Front Mol Biosci. 2021 Sep 16;8:716544. doi: 10.3389/fmolb.2021.716544. eCollection 2021.
Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.
如今，实验性高通量技术，如下一代测序或微阵列，通常用于创建细胞的详细分子图谱。一般来说，这些平台会生成高维且有噪声的数据集。为了对其进行分析，需要强大的生物信息学工具来深入了解所研究的生物过程。在此，我们概述了GeneTrail工具套件，它为（表观）基因组、转录组、miRNA组和蛋白质组图谱的分析和可视化提供了丰富的功能。我们的框架能够分析标准的批量、时间序列和单细胞测量数据，并包括各种先进方法，以识别潜在失调的生物过程，并检测这些失调过程中的驱动因素。我们通过对一个单细胞COVID-19数据集的分析突出了我们网络服务的功能，该分析展示了其揭示复杂分子机制的潜力。可通过http://genetrail.bioinf.uni-sb.de免费且无需登录访问GeneTrail。
