Orchestrating an Optimized Next-Generation Sequencing-Based Cloud Workflow for Robust Viral Identification during Pandemics

SIMPLE SUMMARY: The recent infectious disease, coronavirus disease 2019, has become the novel pandemic event in the last decade after swine flu, which happened in 2009. While dealing with the pandemic, the challenge of gaining accurate identification results from abundant samples in a timely manner has still persisted. Here, in this study, we show the implementation of an optimized cloud workflow for a robust, yet accurate, identification process from these two latest pandemics events. This is a great example of how we integrate two current available technologies, next-generation sequencing and cloud computing, in practice into an applicable workflow for pandemics to tackle the issue of obtaining satisfactory results in a shorter time, while the abundant samples are available. Hopefully, the methods used in this study will intrigue more healthcare professionals to implement the cloud workflow as a part of the current identification method during the current or future pandemic and other infectious diseases as well. ABSTRACT: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become a novel pandemic event following the swine flu that occurred in 2009, which was caused by the influenza A virus (H1N1 subtype). The accurate identification of the huge number of samples during a pandemic still remains a challenge. In this study, we integrate two technologies, next-generation sequencing and cloud computing, into an optimized workflow version that uses a specific identification algorithm on the designated cloud platform. We use 182 samples (92 for COVID-19 and 90 for swine flu) with short-read sequencing data from two open-access datasets to represent each pandemic and evaluate our workflow performance based on an index specifically created for SARS-CoV-2 or H1N1. Results show that our workflow could differentiate cases between the two pandemics with a higher accuracy depending on the index used, especially when the index that exclusively represented each dataset was used. Our workflow substantially outperforms the original complete identification workflow available on the same platform in terms of time and cost by preserving essential tools internally. Our workflow can serve as a powerful tool for the robust identification of cases and, thus, aid in controlling the current and future pandemics.