This study evaluated the computing, data storage, connectivity, and security resources of the Cloud as a model for forensic laboratory systems that produce Next-generation Sequencing (NGS) data.
Next-generation Sequencing (NGS) is a rapidly evolving technology with demonstrated benefits for forensic genetic applications, and the strategies to analyze and manage the massive NGS datasets are currently in development. The current study developed a complete front-to-end Cloud system to upload, process, and interpret raw NGS data using a web browser dashboard. The system was extensible, demonstrating analysis capabilities of autosomal and Y-STRs from a variety of NGS instrumentation (Illumina MiniSeq and MiSeq, and Oxford Nanopore MinION). NGS data for STRs were concordant with standard reference materials previously characterized with capillary electrophoresis and Sanger sequencing. The computing power of the Cloud was implemented with on-demand auto-scaling to allow multiple file analysis in tandem. The system was designed to store resulting data in a relational database, amenable to downstream sample interpretations and databasing applications following the most recent guidelines in nomenclature for sequenced alleles. Lastly, a multi-layered Cloud security architecture was tested and showed that industry standards for securing data and computing resources were readily applied to the NGS system without disadvantageous effects for bioinformatic analysis, connectivity or data storage/retrieval. The results of this study demonstrate the feasibility of using Cloud-based systems for secured NGS data analysis, storage, databasing, and multi-user distributed connectivity. (publisher abstract modified)
Report (Grant Sponsored)
Date Published: November 1, 2017