Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data.

BACKGROUND: Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools. RESULTS: Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches. CONCLUSIONS: Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Whole-genome sequencing of asian lung cancers: second-hand smoke unlikely to be responsible for higher incidence of lung cancer among Asian never-smokers.

Asian nonsmoking populations have a higher incidence of lung cancer compared with their European counterparts. There is a long-standing hypothesis that the increase of lung cancer in Asian never-smokers is due to environmental factors such as second-hand smoke. We analyzed whole-genome sequencing of 30 Asian lung cancers. Unsupervised clustering of mutational signatures separated the patients into two categories of either all the never-smokers or all the smokers or ex-smokers. In addition, nearly one third of the ex-smokers and smokers classified with the never-smoker-like cluster. The somatic variant profiles of Asian lung cancers were similar to that of European origin with G.C>T.A being predominant in smokers. We found EGFR and TP53 to be the most frequently mutated genes with mutations in 50% and 27% of individuals, respectively. Among the 16 never-smokers, 69% had an EGFR mutation compared with 29% of 14 smokers/ex-smokers. Asian never-smokers had lung cancer signatures distinct from the smoker signature and their mutation profiles were similar to European never-smokers. The profiles of Asian and European smokers are also similar. Taken together, these results suggested that the same mutational mechanisms underlie the etiology for both ethnic groups. Thus, the high incidence of lung cancer in Asian never-smokers seems unlikely to be due to second-hand smoke or other carcinogens that cause oxidative DNA damage, implying that routine EGFR testing is warranted in the Asian population regardless of smoking status.