Related Experiment Video
Updated: Sep 10, 2026

Development of Multiplex Real-Time RT-qPCR Assays for the Detection of SARS-CoV-2, Influenza A/B, and MERS-CoV
Published on: November 10, 2023
An enhanced Multisegment RT-PCR method for Influenza A Virus Sequencing: Improved Performance and Reduced Preparation
Shakiba Kazemian1, Stephen Pedroza1, Alinne L R Santana-Pereira1
1Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States; Center for Influenza Disease and Emergence Response (CIDER), Athens, GA, United States.
Abstract:
Influenza A viruses (IAVs) remain a major global health threat, affecting both human and animal populations. Whole-genome sequencing is essential for monitoring viral evolution, zoonotic transmission, and emerging variants. However, conventional RT-PCR methods often result in incomplete gene coverage, amplification biases, and reduced sequencing accuracy, particularly in clinical samples. We developed a robust In-house method for IAV full-genome sequencing using the Oxford Nanopore Technologies (ONT) long-read sequencing platform. This method integrates an in-house multisegment Reverse Transcription PCR (RT-PCR) method with a streamlined 2-pool primer design targeting all eight IAV gene segments. RNA extracted from clinical and stock virus samples was reverse-transcribed and amplified using Superscript IV-based chemistry, followed by magnetic bead purification to ensure high-quality amplicons. Sequencing libraries were prepared with the Native Barcoding Kit 24 (SQK-NBD114.24) and sequenced on R10.4.1 flow cells on the MinION MK1C device. Data analysis using the Iterative Refinement Meta-Assembler (IRMA) confirmed improved read depth, uniform coverage, and complete genome recovery. Compared to conventional methods, our In-House Multisegment 2-Pool (IH-MS2P) RT-PCR method generated higher numbers of matched read counts, minimized chimeric artifacts, and delivered superior genome coverage across human, swine, and avian isolates. This optimized RT-PCR method provides a high-performance, time-efficient, and portable solution for influenza genomics, demonstrating robust applicability even with clinical samples of low RNA yield.

