PDF(Pubmed)
Abstract：
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
摘要：
高通量单细胞RNA-seq（scRNA-seq）技术和实验方案的快速发展导致了大量转录组数据的产生，这些数据填充了几个在线数据库和存储库。这里,我们系统地检查了大规模的scRNA-seq数据库，根据它们的范围和目的对它们进行分类，如一般，组织特异性数据库，疾病特异性数据库，以癌症为中心的数据库，和以细胞类型为中心的数据库。接下来,我们讨论了与管理大规模scRNA-seq数据库相关的技术和方法挑战，以及当前的计算解决方案。我们认为理解scRNA-seq数据库，包括他们的局限性和假设，对于有效利用这些数据进行可靠的发现和识别新的生物学见解至关重要。这样的平台可以通过用户友好的基于网络的界面帮助弥合计算和湿实验室科学家之间的差距，这些界面需要使单细胞数据的访问民主化。这些平台将促进跨学科研究，使来自不同学科的研究人员能够有效地合作。这篇综述强调了利用计算方法来解开单细胞数据复杂性的重要性，并为该领域的未来研究提供了一个有希望的方向。
