Corrigendum: NGS implementation for monitoring SARS-CoV-2 variants in Chicagoland: an institutional perspective, successes and challenges.

[This corrects the article DOI: 10.3389/fpubh.2023.1177695.].

NGS implementation for monitoring SARS-CoV-2 variants in Chicagoland: An institutional perspective, successes and challenges.

Identification of SARS-CoV-2 lineages has shown to provide invaluable information regarding treatment efficacy, viral transmissibility, disease severity, and immune evasion. These benefits provide institutions with an expectation of high informational upside with little insight in regards to practicality with implementation and execution of such high complexity testing in the midst of a pandemic. This article details our institution's experience implementing and using Next Generation Sequencing (NGS) to monitor SARS-CoV-2 lineages in the northern Chicagoland area throughout the pandemic. To date, we have sequenced nearly 7,000 previously known SARS-CoV-2 positive samples from various patient populations (e.g., outpatient, inpatient, and outreach sites) to reduce bias in sampling. As a result, our hospital was guided while making crucial decisions about staffing, masking, and other infection control measures during the pandemic. While beneficial, establishing this NGS procedure was challenging, with countless considerations at every stage of assay development and validation. Reduced staffing prompted transition from a manual to automated high throughput workflow, requiring further validation, lab space, and instrumentation. Data management and IT security were additional considerations that delayed implementation and dictated our bioinformatic capabilities. Taken together, our experience highlights the obstacles and triumphs of SARS-CoV-2 sequencing.

SILO: A Computational Method for Detecting Copy Number Gain in Clinical Specimens Analyzed on a Next-Generation Sequencing Platform.

Next-generation sequencing (NGS) has proved to be a beneficial approach for genotyping solid tumor specimens and for identifying clinically actionable mutations. However, copy number variations (CNVs), which can be equally important, are often challenging to detect from NGS data. Current bioinformatics methods for CNV detection from NGS often require comparison of tumor/normal pairs and/or the sequencing of whole genome or whole exome. These approaches are currently impractical for routine clinical practice. However, clinical practice does involve repeated use of the same gene panel on a large number of specimens over a long period of time. We take advantage of this repetitiveness and present SILO: a procedure for CNV detection based on NGS on a gene panel. The SILO algorithm analyzes coverage depth of the aligned reads from a sample and predicts CNV by comparing this depth to the average depth seen in a large training set of other samples. Such comparison is robust and can reliably detect copy number gain, although it is found to be unreliable in detecting copy number losses. Successful validation of SILO on NGS data from the Ion Torrent platform with two panels is presented: a small hotspot panel and a larger cancer gene panel.