  Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq)
technologies and experimental protocols have led to the generation of vast
amounts of genomic 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. Furthermore, we propose that bridging the
gap between computational and wet lab scientists through user-friendly
web-based platforms is 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.
