Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7. avenue des Hauts-Fourneaux, Esch-sur-Alzette, L-4362, Luxembourg City, Luxembourg.
Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
Sci Rep. 2018 Sep 6;8(1):13355. doi: 10.1038/s41598-018-31688-9.
Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.
细胞分化是一个复杂的过程，在此过程中，一个不太专门化的细胞逐渐发展成一个更专门化的细胞。尽管研究力度不断加大，但确定细胞命运决定因素（在分化过程中决定细胞命运的转录因子 (TFs)）仍然是一个挑战，特别是当考虑来自共同祖细胞的密切相关的细胞类型时。在这里，我们开发了 SeesawPred，这是一个网络应用程序，它基于细胞分化的基因调控网络 (GRN) 模型，可以从转录组学数据中计算预测细胞命运决定因素。与以前的方法不同，它允许用户上传基因表达数据，并且不依赖于预编译的参考数据集，从而可以将其应用于新的分化系统。SeesawPred 在小鼠和人类的各种细胞分化示例中正确预测了已知的细胞命运决定因素，并且与最先进的方法相比表现更好。该应用程序可免费在学术、非营利性网站 http://seesaw.lcsb.uni.lu 上使用。
