Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
Sci Data. 2019 Oct 31;6(1):256. doi: 10.1038/s41597-019-0202-7.
Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.
多组学方法使用多种高通量技术来描绘活细胞的不同分子层面。理想情况下，这些信息的整合应该会产生全面的细胞生理学和调节系统模型。然而，大多数多组学项目仍然只包含有限数量的分子检测，而且很少有多组学研究评估细胞生长、发育和适应等动态过程。因此，我们缺乏可以用于为动态细胞系统开发真正多层模型的正式分析方法和全面的多组学数据集。在这里，我们展示了 STATegra 多组学数据集，该数据集将多达 10 种不同组学技术的测量结果结合到同一个生物系统中，即经过充分研究的小鼠前 B 细胞分化。STATegra 包括染色质结构、基因表达、蛋白质组学和代谢组学的高通量测量，并且还补充了单细胞数据。据我们所知，STATegra 数据集是描述动态生物系统的最多样化的多组学数据集。
