SCANNER: a web platform for annotation, visualization and sharing of single cell RNA-seq data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptome profiling.
• The complexity of scRNA-seq data presents analytical challenges, necessitating interdisciplinary collaboration.
• Effective platforms for data sharing and exploration are crucial for advancing scRNA-seq research.

Purpose of the Study:

• To develop a user-friendly web resource, SCANNER, for collaborative analysis of scRNA-seq data.
• To provide a public platform for sharing and exploring single-cell transcriptomic data without requiring extensive coding skills.
• To enable efficient investigation of gene set activation at the single-cell level.

Main Methods:

• Development of the Single-Cell Transcriptomics Annotated Viewer (SCANNER) web resource.
• Implementation of a real-time database for secure data management.
• Hosting and updating a diverse database of scRNA-seq datasets, including various cancers, COVID-19, and healthy tissues.

Main Results:

• SCANNER facilitates easy data sharing and exploration for the scientific community.
• Analysis using SCANNER revealed a higher proportion of cancer-associated fibroblasts and activated fibroblast growth genes in female melanoma patients compared to males.
• ACE2 expression was identified primarily in lung pneumocytes, secretory, and ciliated cells, with differential expression observed between smokers and non-smokers.

Conclusions:

• SCANNER provides a valuable, accessible tool for collaborative scRNA-seq data analysis.
• The platform aids in uncovering biologically significant differences, such as sex-based variations in melanoma and smoking-related gene expression in the lungs.
• SCANNER's continuously updated database will support future discoveries in single-cell transcriptomics.