Stripenn detects architectural stripes from chromatin conformation data using computer vision.
Stripenn detects architectural stripes from chromatin conformation data using computer vision.
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DOI：
10.1038/s41467-022-29258-9
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发表时间：
2022-03-24
影响因子：
16.6
通讯作者：
Vahedi G
中科院分区：
文献类型：
--
作者：
Yoon S;Chandra A;Vahedi G
结构条纹倾向于形成在基因组区域，该区域包含在细胞身份和功能中具有突出作用的基因。因此，这些特征的准确识别和定量对于理解谱系特异性基因调控至关重要。在这里，我们提出了Stripenn，一种植根于计算机视觉的算法，使用各种技术从染色质构象测量中系统地检测和定量建筑条纹。我们证明Stripenn优于现有的方法，并强调其在B和T淋巴细胞的背景下的生物学应用。通过比较不同细胞类型和不同物种的条纹，我们发现这些染色质特征是高度保守的，并且在细胞类型特异性过程中具有突出作用的基因上形成。总之，Stripenn是一种从广泛使用的图像处理技术中借用概念来标定和量化建筑条纹的计算方法。染色体构象捕获技术最近揭示了染色质环以外的功能，如建筑条纹。在这里，作者提出了他们的条纹检测工具'Stripenn'，以检测和定量条纹从任何类型的染色质构象捕获数据。他们表明，结构条纹在转录活性和可接近的基因组区域富集。
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. Chromosome conformation capture techniques have recently revealed features beyond chromatin loops such as architectural stripes. Here the authors present their stripe detection tool ‘Stripenn’ to detect and quantitate stripes from any type of chromatin conformation capture data. They show that architectural stripes are enriched at transcriptionally active and accessible genomic regions.
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影响因子：
14.9
作者：
Gene Ontology Consortium
通讯作者：
Gene Ontology Consortium
影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
30.8
作者：
Galan S;Machnik N;Kruse K;Díaz N;Marti-Renom MA;Vaquerizas JM
通讯作者：
Vaquerizas JM
影响因子：
64.5
作者：
Hnisz D;Abraham BJ;Lee TI;Lau A;Saint-André V;Sigova AA;Hoke HA;Young RA
通讯作者：
Young RA
影响因子：
7.8
作者：
Brown MG;Scalzo AA
通讯作者：
Scalzo AA