*NEW* - We have added a new Single-Cell Database Discovery (SCDD) service, accessible from the side-bar. The service facilitates the exploration of Human and Mouse Single-cell dataset metadata, indexed from NCBI Sequence Read Archive (SRA).

A resource for the single-cell research community-

Single-cell omics recently emerged as powerful tools to investigate heterogeneity of large populations of cells. Among others, improvements in sequencing, microscopy and microfluidic technologies let to a rapid increase of complex datasets with single-cell resolution. However, due to a lack of available platforms to easily share and integrate complex single-cell datasets, the accessibility of such datasets can be a barrier to efficient usage.
To address the issue of efficient single-cell data management, we introduced a single-cell data integration platform. The motivation for the development of this database was to enable easy access, integration and collaboration on single-cell datasets generated by the research community worldwide. This database features integration of single-cell metadata, cell images and sequence information. Currently the platform is divided into two parts; the first part is the single-cell dataset generated at the Division of Genomic Technologies at RIKEN CLST and the second part is the single-cell dataset published in peer-reviewed journals.

Image currently unavailable Graphical abstract for the SkewC paper.

SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data

Researchers at LSBDT have created the SkewC QC tool. The methodology is based on the assessment of gene coverage for each cell, and its skewness as a quality measure. SkewC is capable of processing any type of scRNA-seq dataset, regardless of the protocol. This tool is designed to avoid misclustering or false clusters by identifying, isolating, and removing cells with skewed gene body coverage profiles.