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Main Author:	Mostafa, Rezapour
Format:	Recurso digital
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Published:	Zenodo 2025
Online Access:	https://doi.org/10.5281/zenodo.15722617
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_version_	1866901465155502080
author	Mostafa, Rezapour
author_facet	Mostafa, Rezapour
contents	<p>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.</p> <p>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.</p> <p>We used publicly available datasets from the Gene Expression Omnibus (GEO) with IDs:</p> <ul> <li> <p><strong>GSE157103</strong>, <strong>GSE171110</strong>, <strong>GSE189039</strong>, <strong>GSE201530</strong> (Training)</p> </li> <li> <p><strong>GSE152418</strong>, <strong>PMC8202013</strong>, <strong>GSE161731</strong>, <strong>GSE166190</strong>, <strong>GSE294888</strong>, <strong>GSE239595</strong> (Held-out test)</p> </li> </ul> <p>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.</p> <p>Our objectives:</p> <ul> <li> <p>Analyze gene expression using GLMQL-MAS</p> </li> <li> <p>Perform GO and pathway analysis</p> </li> <li> <p>Identify variant-agnostic gene signatures using Cross-MAS</p> </li> <li> <p>Validate classification performance on held-out datasets</p> </li> </ul> <p>Top genes (e.g., IFI27, CDC20, RRM2, HJURP, CDC45) demonstrated high classification accuracy, up to 100% in some test sets.</p> <h3>Data Download and Preprocessing</h3> <p>Download bulk RNA-Seq data from the following datasets:</p> <div> <div> <table> <thead> <tr> <th>Dataset ID</th> <th>Cell Type</th> <th>Variant</th> <th>Sample Info</th> <th>Location & Time Period</th> </tr> </thead> <tbody> <tr> <td>GSE157103</td> <td>Leukocytes</td> <td>Original Wuhan</td> <td>10 COVID-neg; 48 COVID-pos (ICU); 50 non-hospitalized</td> <td>New York, USA (2020)</td> </tr> <tr> <td>GSE171110</td> <td>Whole blood</td> <td>French COVID</td> <td>10 COVID-neg; 44 COVID-pos (severe)</td> <td>Créteil, France (2020)</td> </tr> <tr> <td>GSE189039</td> <td>PBMCs</td> <td>Beta</td> <td>8 COVID-neg; 9 COVID-pos</td> <td>South Tyrol, Italy (2021)</td> </tr> <tr> <td>GSE201530</td> <td>PBMCs</td> <td>Omicron</td> <td>8 COVID-neg; 38 COVID-pos</td> <td>Tyrol, Austria (2021–2022)</td> </tr> <tr> <td>GSE152418</td> <td>PBMCs</td> <td>Original Wuhan</td> <td>17 COVID-neg; 16 COVID-pos</td> <td>Atlanta, USA (2020)</td> </tr> <tr> <td>PMC8202013</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>27 COVID-neg; 103 COVID-pos</td> <td>Lausanne, Switzerland (2020)</td> </tr> <tr> <td>GSE161731</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>16 COVID-neg; 12 COVID-pos</td> <td>Durham, NC, USA (2020)</td> </tr> <tr> <td>GSE166190</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>11 COVID-neg; 10 COVID-pos</td> <td>Geneva, Switzerland (2020)</td> </tr> <tr> <td>GSE294888</td> <td>pDCs, DC2s</td> <td>Delta and Omicron BA.1</td> <td>30 total (5 replicates each per condition)</td> <td>Paris, France (2025)</td> </tr> <tr> <td>GSE239595</td> <td>NP lymphoid tissue</td> <td>Omicron</td> <td>9 total (3 COVID-neg; 6 COVID-pos)</td> <td>Seoul, South Korea (2022–2023)</td> </tr> </tbody> </table> <div> <div> </div> </div> </div> </div> <p><strong>File Format:</strong></p> <ul> <li> <p>Samples are columns, genes are rows.</p> </li> <li> <p>First row must contain column names.</p> </li> <li> <p>First column must be titled <code>Gene Symbol</code>.</p> </li> <li> <p>Sample names should follow consistent naming with <code>(1)</code>, <code>(2)</code> etc. to indicate replicates.</p> </li> </ul> <h3>Included Scripts</h3> <ol> <li> <p><code>0000000000-part 1-DE analysis.R</code> – Differential gene expression analysis.</p> </li> <li> <p><code>0000000000-part 2-classification.ipynb</code> – Classification analysis using top-ranked genes.</p> </li> <li> <p><code>0000000000-part 3-GO and Pathway analysis.ipynb</code> – Functional enrichment analysis.</p> </li> </ol> <blockquote> <p><strong>Note:</strong> Place <code>Pathway and GO.R</code> in the same directory as the notebooks.</p> </blockquote>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_15722617
institution	Zenodo
language
publishDate	2025
publisher	Zenodo
record_format	zenodo
spellingShingle	Tracing the Evolutionary Pathway of SARS-CoV-2 Through RNA Sequencing Analysis Mostafa, Rezapour <p>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.</p> <p>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.</p> <p>We used publicly available datasets from the Gene Expression Omnibus (GEO) with IDs:</p> <ul> <li> <p><strong>GSE157103</strong>, <strong>GSE171110</strong>, <strong>GSE189039</strong>, <strong>GSE201530</strong> (Training)</p> </li> <li> <p><strong>GSE152418</strong>, <strong>PMC8202013</strong>, <strong>GSE161731</strong>, <strong>GSE166190</strong>, <strong>GSE294888</strong>, <strong>GSE239595</strong> (Held-out test)</p> </li> </ul> <p>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.</p> <p>Our objectives:</p> <ul> <li> <p>Analyze gene expression using GLMQL-MAS</p> </li> <li> <p>Perform GO and pathway analysis</p> </li> <li> <p>Identify variant-agnostic gene signatures using Cross-MAS</p> </li> <li> <p>Validate classification performance on held-out datasets</p> </li> </ul> <p>Top genes (e.g., IFI27, CDC20, RRM2, HJURP, CDC45) demonstrated high classification accuracy, up to 100% in some test sets.</p> <h3>Data Download and Preprocessing</h3> <p>Download bulk RNA-Seq data from the following datasets:</p> <div> <div> <table> <thead> <tr> <th>Dataset ID</th> <th>Cell Type</th> <th>Variant</th> <th>Sample Info</th> <th>Location & Time Period</th> </tr> </thead> <tbody> <tr> <td>GSE157103</td> <td>Leukocytes</td> <td>Original Wuhan</td> <td>10 COVID-neg; 48 COVID-pos (ICU); 50 non-hospitalized</td> <td>New York, USA (2020)</td> </tr> <tr> <td>GSE171110</td> <td>Whole blood</td> <td>French COVID</td> <td>10 COVID-neg; 44 COVID-pos (severe)</td> <td>Créteil, France (2020)</td> </tr> <tr> <td>GSE189039</td> <td>PBMCs</td> <td>Beta</td> <td>8 COVID-neg; 9 COVID-pos</td> <td>South Tyrol, Italy (2021)</td> </tr> <tr> <td>GSE201530</td> <td>PBMCs</td> <td>Omicron</td> <td>8 COVID-neg; 38 COVID-pos</td> <td>Tyrol, Austria (2021–2022)</td> </tr> <tr> <td>GSE152418</td> <td>PBMCs</td> <td>Original Wuhan</td> <td>17 COVID-neg; 16 COVID-pos</td> <td>Atlanta, USA (2020)</td> </tr> <tr> <td>PMC8202013</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>27 COVID-neg; 103 COVID-pos</td> <td>Lausanne, Switzerland (2020)</td> </tr> <tr> <td>GSE161731</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>16 COVID-neg; 12 COVID-pos</td> <td>Durham, NC, USA (2020)</td> </tr> <tr> <td>GSE166190</td> <td>Whole blood</td> <td>Original Wuhan</td> <td>11 COVID-neg; 10 COVID-pos</td> <td>Geneva, Switzerland (2020)</td> </tr> <tr> <td>GSE294888</td> <td>pDCs, DC2s</td> <td>Delta and Omicron BA.1</td> <td>30 total (5 replicates each per condition)</td> <td>Paris, France (2025)</td> </tr> <tr> <td>GSE239595</td> <td>NP lymphoid tissue</td> <td>Omicron</td> <td>9 total (3 COVID-neg; 6 COVID-pos)</td> <td>Seoul, South Korea (2022–2023)</td> </tr> </tbody> </table> <div> <div> </div> </div> </div> </div> <p><strong>File Format:</strong></p> <ul> <li> <p>Samples are columns, genes are rows.</p> </li> <li> <p>First row must contain column names.</p> </li> <li> <p>First column must be titled <code>Gene Symbol</code>.</p> </li> <li> <p>Sample names should follow consistent naming with <code>(1)</code>, <code>(2)</code> etc. to indicate replicates.</p> </li> </ul> <h3>Included Scripts</h3> <ol> <li> <p><code>0000000000-part 1-DE analysis.R</code> – Differential gene expression analysis.</p> </li> <li> <p><code>0000000000-part 2-classification.ipynb</code> – Classification analysis using top-ranked genes.</p> </li> <li> <p><code>0000000000-part 3-GO and Pathway analysis.ipynb</code> – Functional enrichment analysis.</p> </li> </ol> <blockquote> <p><strong>Note:</strong> Place <code>Pathway and GO.R</code> in the same directory as the notebooks.</p> </blockquote>
title	Tracing the Evolutionary Pathway of SARS-CoV-2 Through RNA Sequencing Analysis
url	https://doi.org/10.5281/zenodo.15722617

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