Dataset Information

Implementation of an In-House Platform for Rapid Screening of SARS-CoV-2 Genome Variations.

ABSTRACT:

Background

Global real-time monitoring of SARS-CoV-2 variants is crucial to controlling the COVID-19 outbreak. The purpose of this study was to set up a Sanger-based platform for massive SARS-CoV-2 variant tracking in laboratories in low-resource settings.

Methods

We used nested RT-PCR assay, Sanger sequencing and lineage assignment for 930-bp of the SARS-CoV-2 spike gene, which harbors specific variants of concern (VOCs) mutations. We set up our platform by comparing its results with whole genome sequencing (WGS) data on 137 SARS-CoV-2 positive samples. Then, we applied it on 1028 samples from March-September 2021.

Results

In total, 125 out of 137 samples showed 91.24% concordance in mutation detection. In lineage assignment, 123 out of 137 samples demonstrated 89.78% concordance, 65 of which were assigned as VOCs and showed 100% concordance. Of 1028 samples screened by our in-house method, 78 distinct mutations were detected. The most common mutations were: S:D614G (21.91%), S:P681R (12.19%), S:L452R (12.15%), S:T478K (12.15%), S:N501Y (8.91%), S:A570D (8.89%), S:P681H (8.89%), S:T716I (8.74%), S:L699I (3.50%) and S:S477N (0.28%). Of 1028 samples, 980 were attributed as VOCs, which include the Delta (B.1.617.2) and Alpha (B.1.1.7) variants.

Conclusion

Our proposed in-house Sanger-based assay for SARS-CoV-2 lineage assignment is an accessible strategy in countries with poor infrastructure facilities. It can be applied in the rapid tracking of SARS-CoV-2 VOCs in the SARS-CoV-2 pandemic.

SUBMITTER: Zare Ashrafi F

PROVIDER: S-EPMC10685895 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Implementation of an In-House Platform for Rapid Screening of SARS-CoV-2 Genome Variations.

Zare Ashrafi Farzane F Mohseni Marzieh M Beheshtian Maryam M Fattahi Zohreh Z Ghodratpour Fatemeh F Keshavarzi Fatemeh F Behravan Hanieh H Kalhor Marzieh M Jalalvand Khadijeh K Azad Maryam M Koshki Mahdieh M Jafarpour Ali A Ghaziasadi Azam A Abdollahi Alireza A Kiani Seyed Jalal SJ Ataei-Pirkooh Angila A Rezaei Azhar Iman I Bokharaei-Salim Farah F Haghshenas Mohammad Reza MR Babamahmoodi Farhang F Mokhames Zakiye Z Soleimani Alireza A Ziaee Masood M Javanmard Davod D Ghafari Shokouh S Ezani Akram A Ansari Moghaddam Alireza A Shahraki-Sanavi Fariba F Hashemi Shahri Seyed Mohammad SM Azaran Azarakhsh A Yousefi Farid F Moattari Afagh A Moghadami Mohsen M Fakhim Hamed H Ataei Behrooz B Nasri Elahe E Poortahmasebi Vahdat V Varshochi Mojtaba M Mojtahedi Ali A Jalilian Farid F Khazeni Mohammad M Moradi Abdolvahab A Tabarraei Alijan A Piroozmand Ahmad A Yahyapour Yousef Y Bayani Masoumeh M Aboofazeli Amir A Ghafari Parsa P Keramat Fariba F Tavakoli Mahsa M Jalali Tahmineh T Pouriayevali Mohammad Hassan MH Salehi-Vaziri Mostafa M Khorram Khorshid Hamid Reza HR Najafipour Reza R Malekzadeh Reza R Kahrizi Kimia K Jazayeri Seyed Mohammad SM Najmabadi Hossein H

Archives of Iranian medicine 20230201 2

<h4>Background</h4>Global real-time monitoring of SARS-CoV-2 variants is crucial to controlling the COVID-19 outbreak. The purpose of this study was to set up a Sanger-based platform for massive SARS-CoV-2 variant tracking in laboratories in low-resource settings.<h4>Methods</h4>We used nested RT-PCR assay, Sanger sequencing and lineage assignment for 930-bp of the SARS-CoV-2 spike gene, which harbors specific variants of concern (VOCs) mutations. We set up our platform by comparing its results ...[more]

PMID: 37543926

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Project description:Background: The ongoing COVID-19 pandemic is a global health crisis caused by the spread of SARS-CoV-2. Establishing links between known cases is crucial for the containment of COVID-19. In the healthcare setting, the ability to rapidly identify potential healthcare-associated COVID-19 clusters is critical for healthcare worker and patient safety. Increasing sequencing technology accessibility has allowed routine clinical diagnostic laboratories to sequence SARS-CoV-2 in clinical samples. However, these laboratories often lack specialized informatics skills required for sequence analysis. Therefore, an on-site, intuitive sequence analysis tool that enables clinical laboratory users to analyze multiple genomes and derive clinically relevant information within an actionable timeframe is needed. Results: We propose CalmBelt, an integrated framework for on-site whole genome characterization and outbreak tracking. Nanopore sequencing technology enables on-site sequencing and construction of draft genomes for multiple SARS-CoV-2 samples within 12 h. CalmBelt's interactive interface allows users to analyse multiple SARS-CoV-2 genomes by utilizing whole genome information, collection date, and additional information such as predefined potential clusters from epidemiological investigations. CalmBelt also integrates established SARS-CoV-2 nomenclature assignments, GISAID clades and PANGO lineages, allowing users to visualize relatedness between samples together with the nomenclatures. We demonstrated multiple use cases including investigation of potential hospital transmission, mining transmission patterns in a large outbreak, and monitoring possible diagnostic-escape. Conclusions: This paper presents an on-site rapid framework for SARS-CoV-2 whole genome characterization. CalmBelt interactive web application allows non-technical users, such as routine clinical laboratory users in hospitals to determine SARS-CoV-2 variants of concern, as well as investigate the presence of potential transmission clusters. The framework is designed to be compatible with routine usage in clinical laboratories as it only requires readily available sample data, and generates information that impacts immediate infection control mitigations.

Dataset Information

Implementation of an In-House Platform for Rapid Screening of SARS-CoV-2 Genome Variations.

Background

Methods

Results

Conclusion

Publications

Implementation of an In-House Platform for Rapid Screening of SARS-CoV-2 Genome Variations.

Similar Datasets

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