Tracing the Evolutionary Pathway of SARS-CoV-2 Through RNA Sequencing Analysis

This release includes the full pipeline and data formatting instructions for the analysis of gene expression across multiple SARS-CoV-2 variants using bulk RNA-Seq data. The COVID-19 pandemic, driven by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has underscored the need to understand the virus’s evolution due to its global health impact. This study employed RNA sequencing (RNA-Seq) to analyze gene expression differences across multiple SARS-CoV-2 variants. We used publicly available datasets from the Gene Expression Omnibus (GEO) with IDs: GSE157103, GSE171110, GSE189039, GSE201530 (Training) GSE152418, PMC8202013, GSE161731, GSE166190, GSE294888, GSE239595 (Held-out test) These include RNA-Seq data extracted from white blood cells, whole blood, PBMCs, and nasopharyngeal tissues of individuals infected with various SARS-CoV-2 variants (Original Wuhan, French, Beta, Omicron) and COVID-negative controls. Our objectives: Analyze gene expression using GLMQL-MAS Perform GO and pathway analysis Identify variant-agnostic gene signatures using Cross-MAS Validate classification performance on held-out datasets Top genes (e.g., IFI27, CDC20, RRM2, HJURP, CDC45) demonstrated high classification accuracy, up to 100% in some test sets. Data Download and Preprocessing Download bulk RNA-Seq data from the following datasets: Dataset ID Cell Type Variant Sample Info Location & Time Period GSE157103 Leukocytes Original Wuhan 10 COVID-neg; 48 COVID-pos (ICU); 50 non-hospitalized New York, USA (2020) GSE171110 Whole blood French COVID 10 COVID-neg; 44 COVID-pos (severe) Créteil, France (2020) GSE189039 PBMCs Beta 8 COVID-neg; 9 COVID-pos South Tyrol, Italy (2021) GSE201530 PBMCs Omicron 8 COVID-neg; 38 COVID-pos Tyrol, Austria (2021–2022) GSE152418 PBMCs Original Wuhan 17 COVID-neg; 16 COVID-pos Atlanta, USA (2020) PMC8202013 Whole blood Original Wuhan 27 COVID-neg; 103 COVID-pos Lausanne, Switzerland (2020) GSE161731 Whole blood Original Wuhan 16 COVID-neg; 12 COVID-pos Durham, NC, USA (2020) GSE166190 Whole blood Original Wuhan 11 COVID-neg; 10 COVID-pos Geneva, Switzerland (2020) GSE294888 pDCs, DC2s Delta and Omicron BA.1 30 total (5 replicates each per condition) Paris, France (2025) GSE239595 NP lymphoid tissue Omicron 9 total (3 COVID-neg; 6 COVID-pos) Seoul, South Korea (2022–2023) File Format: Samples are columns, genes are rows. First row must contain column names. First column must be titled Gene Symbol. Sample names should follow consistent naming with (1), (2) etc. to indicate replicates. Included Scripts 0000000000-part 1-DE analysis.R – Differential gene expression analysis. 0000000000-part 2-classification.ipynb – Classification analysis using top-ranked genes. 0000000000-part 3-GO and Pathway analysis.ipynb – Functional enrichment analysis. Note: Place Pathway and GO.R in the same directory as the notebooks.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-23
DOI
https://doi.org/10.5281/zenodo.15722616
Primary Topic
COVID-19 Clinical Research Studies
Type
article
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article

Tracing the Evolutionary Pathway of SARS-CoV-2 Through RNA Sequencing Analysis

Zenodo (CERN European Organization for Nuclear Research)
COVID-19 Clinical Research Studies
article

Tracing the Evolutionary Pathway of SARS-CoV-2 Through RNA Sequencing Analysis

article en

Abstract

This release includes the full pipeline and data formatting instructions for the analysis of gene expression across multiple SARS-CoV-2 variants using bulk RNA-Seq data. The COVID-19 pandemic, driven by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has underscored the need to understand the virus’s evolution due to its global health impact. This study employed RNA sequencing (RNA-Seq) to analyze gene expression differences across multiple SARS-CoV-2 variants. We used publicly available datasets from the Gene Expression Omnibus (GEO) with IDs: GSE157103, GSE171110, GSE189039, GSE201530 (Training) GSE152418, PMC8202013, GSE161731, GSE166190, GSE294888, GSE239595 (Held-out test) These include RNA-Seq data extracted from white blood cells, whole blood, PBMCs, and nasopharyngeal tissues of individuals infected with various SARS-CoV-2 variants (Original Wuhan, French, Beta, Omicron) and COVID-negative controls. Our objectives: Analyze gene expression using GLMQL-MAS Perform GO and pathway analysis Identify variant-agnostic gene signatures using Cross-MAS Validate classification performance on held-out datasets Top genes (e.g., IFI27, CDC20, RRM2, HJURP, CDC45) demonstrated high classification accuracy, up to 100% in some test sets. Data Download and Preprocessing Download bulk RNA-Seq data from the following datasets: Dataset ID Cell Type Variant Sample Info Location & Time Period GSE157103 Leukocytes Original Wuhan 10 COVID-neg; 48 COVID-pos (ICU); 50 non-hospitalized New York, USA (2020) GSE171110 Whole blood French COVID 10 COVID-neg; 44 COVID-pos (severe) Créteil, France (2020) GSE189039 PBMCs Beta 8 COVID-neg; 9 COVID-pos South Tyrol, Italy (2021) GSE201530 PBMCs Omicron 8 COVID-neg; 38 COVID-pos Tyrol, Austria (2021–2022) GSE152418 PBMCs Original Wuhan 17 COVID-neg; 16 COVID-pos Atlanta, USA (2020) PMC8202013 Whole blood Original Wuhan 27 COVID-neg; 103 COVID-pos Lausanne, Switzerland (2020) GSE161731 Whole blood Original Wuhan 16 COVID-neg; 12 COVID-pos Durham, NC, USA (2020) GSE166190 Whole blood Original Wuhan 11 COVID-neg; 10 COVID-pos Geneva, Switzerland (2020) GSE294888 pDCs, DC2s Delta and Omicron BA.1 30 total (5 replicates each per condition) Paris, France (2025) GSE239595 NP lymphoid tissue Omicron 9 total (3 COVID-neg; 6 COVID-pos) Seoul, South Korea (2022–2023) File Format: Samples are columns, genes are rows. First row must contain column names. First column must be titled Gene Symbol. Sample names should follow consistent naming with (1), (2) etc. to indicate replicates. Included Scripts 0000000000-part 1-DE analysis.R – Differential gene expression analysis. 0000000000-part 2-classification.ipynb – Classification analysis using top-ranked genes. 0000000000-part 3-GO and Pathway analysis.ipynb – Functional enrichment analysis. Note: Place Pathway and GO.R in the same directory as the notebooks.

Zenodo (CERN European Organization for Nuclear Research)
Atrium Health Wake Forest Baptist (US), Wake Forest University (US)
Good health and well-being
Openalex Percentile: Top 45%
COVID-19 Clinical Research Studies
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