Bioinformatic management and analysis of single cell RNA sequencing data

Abstract

Since its development in 2009, single cell RNA sequencing (scRNA-seq) has been a game-changer in understanding cellular biology, offering unprecedented resolution. Over the past decade, advancements in scRNA-seq technology have greatly enhanced throughput, specificity, and cost-effectiveness, leading to profound insights into organ development, tissue heterogeneity, cellular communication, and disease mechanisms.

However, despite its benefits, scRNA-seq introduces new complexities, particularly in bioinformatic analysis. While the landscape of bioinformatic tools has considerably expanded with the rise of next generation sequencing (NGS), not all tools are suited for the processing of single cell data. Unique challenges, such as barcode detection, separating cell RNA from ambient RNA, and normalizing zero-inflated count matrices, necessitate specialized solutions while simultaneously increasing the entry barrier to data analysis. This challenge is further complicated by the growing amount of data and meta data in the field of single cell transcriptomics. In addition to the growing demand for storage capacity, it is becoming increasingly complex for researchers to keep track of available data as well as to organize or share their own experimental data for analysis, making it more difficult to extract the optimal outcome from an experiment. Therefore, standardized storage and data management solutions are crucial to ensure efficient research in the future.

To overcome these challenges, WASP (Web-Accessible Single Cell RNAseq Analysis Platform) was developed. WASP provides comprehensive preprocessing and downstream analysis, including clustering of cellular populations and identification of marker genes. By providing automated workflows and a user-friendly interface, WASP greatly reduces the entry barrier for researchers, enabling them to perform data analysis independently. Moreover, WASP supports protocols from various manufacturers, offering researchers flexibility in their choice of single cell platforms. To target the amount of growing scRNA-seq data, WASP has further been integrated into the openBIS research data management platform, facilitating the organization and storage of experimental single cell data as well as WASP analysis results. Leveraging cloud storage and virtual machines provided by the German Network for Bioinformatics Infrastructure (de.NBI), WASP enables direct analysis of single cell data within the openBIS environment, enhancing accessibility and promoting FAIR data practices.