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<h5>Description:</h5>
<p>Severo is a package designed for analysis and exploration of single-cell RNA-seq datasets. In order to be scalable, the implementation has been designed to minimize hardware resources such as memory while maximizing performance through parallelism and hardware optimizations. The package provides a toolbox of different algorithms and statistical methods from which the user can pick and choose.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/onderzoek/onderzoeksuitdagingen-2019-2023/ai-driven-data-science"[entry:58:/onderzoek/onderzoeksutidagingen-2019-2023/ai-driven-data-science]data-entry-id="58">GC1</a> - WP7-T7.1</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:roel.wuyts@imec.be" target="_blank" rel="noopener">Roel Wuyts</a></li>
<li><strong>Authors</strong>: <a href="mailto:tom.haber.ext@imec.be" target="_blank" rel="noopener">Tom Haber</a></li>
<li><strong>Programming Language</strong>: Julia</li>
<li><a href="https://github.com/ExaScience/Severo.jl" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories:</strong> Single cell and DNA sequence analysis</li>
</ul>

<p>Package designed for analysis and exploration of single-cell RNA-seq datasets.</p>