Highlights
- •A Cloud-based system was developed to analyze next-generation sequencing data.
- •Data from a variety of bench top and portable DNA sequencers were analyzed.
- •Results are reported following ISFG guidelines and stored in a relational database.
- •Benefits included on-demand scalability, ease-of-use, access controls, and security.
Abstract
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. Here, the computing, data storage,
connectivity, and security resources of the Cloud were evaluated as a model for forensic
laboratory systems that produce NGS data. A complete front-to-end Cloud system was
developed 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.
Keywords
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Article info
Publication history
Published online: August 08, 2017
Accepted:
August 6,
2017
Received in revised form:
July 24,
2017
Received:
March 16,
2017
Identification
Copyright
© 2017 Elsevier B.V. All rights reserved.
