Stripenn detects architectural stripes from chromatin conformation data using computer vision

Sora Yoon, Aditi Chandra and Golnaz Vahedi ()
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Sora Yoon: Pennsylvania Perelman School of Medicine
Aditi Chandra: Pennsylvania Perelman School of Medicine
Golnaz Vahedi: Pennsylvania Perelman School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Architectural stripes tend to form at genomic regions harboring genes with salient roles in cell identity and function. Therefore, the accurate identification and quantification of these features are essential for understanding lineage-specific gene regulation. Here, we present Stripenn, an algorithm rooted in computer vision to systematically detect and quantitate architectural stripes from chromatin conformation measurements using various technologies. We demonstrate that Stripenn outperforms existing methods and highlight its biological applications in the context of B and T lymphocytes. By comparing stripes across distinct cell types and different species, we find that these chromatin features are highly conserved and form at genes with prominent roles in cell-type-specific processes. In summary, Stripenn is a computational method that borrows concepts from widely used image processing techniques to demarcate and quantify architectural stripes.

Date: 2022
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DOI: 10.1038/s41467-022-29258-9

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