Efficient genome monomer higher-order structure annotation and identification using the GRMhor algorithm.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-11-28 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae191
Matko Glunčić, Domjan Barić, Vladimir Paar
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Abstract

Motivation: Tandem monomeric units, integral components of eukaryotic genomes, form higher-order repeat (HOR) structures that play crucial roles in maintaining chromosome integrity and regulating gene expression and protein abundance. Given their significant influence on processes such as evolution, chromosome segregation, and disease, developing a sensitive and automated tool for identifying HORs across diverse genomic sequences is essential.

Results: In this study, we applied the GRMhor (Global Repeat Map hor) algorithm to analyse the centromeric region of chromosome 20 in three individual human genomes, as well as in the centromeric regions of three higher primates. In all three human genomes, we identified six distinct HOR arrays, which revealed significantly greater differences in the number of canonical and variant copies, as well as in their overall structure, than would be expected given the 99.9% genetic similarity among humans. Furthermore, our analysis of higher primate genomes, which revealed entirely different HOR sequences, indicates a much larger genomic divergence between humans and higher primates than previously recognized. These results underscore the suitability of the GRMhor algorithm for studying specificities in individual genomes, particularly those involving repetitive monomers in centromere structure, which is essential for proper chromosome segregation during cell division, while also highlighting its utility in exploring centromere evolution and other repetitive genomic regions.

Availability and implementation: Source code and example binaries freely available for download at github.com/gluncic/GRM2023.

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基于GRMhor算法的高效基因组单体高阶结构标注与识别。
动机:串联单体单位是真核生物基因组的组成部分,形成高阶重复序列(HOR)结构,在维持染色体完整性、调节基因表达和蛋白质丰度方面发挥着至关重要的作用。考虑到它们对进化、染色体分离和疾病等过程的重大影响,开发一种敏感和自动化的工具来识别不同基因组序列中的HORs至关重要。结果:在本研究中,我们应用GRMhor (Global Repeat Map hor)算法分析了3个人类个体基因组中20号染色体的着丝粒区域,以及3种高等灵长类动物的着丝粒区域。在所有三个人类基因组中,我们鉴定了六个不同的HOR阵列,这些阵列显示出在规范拷贝和变异拷贝的数量以及它们的整体结构上的显著差异,比人类之间99.9%的遗传相似性所期望的要大得多。此外,我们对高等灵长类动物基因组的分析显示,人类和高等灵长类动物之间的基因组差异比之前认识到的要大得多。这些结果强调了GRMhor算法在研究个体基因组特异性方面的适用性,特别是那些涉及着丝粒结构中重复单体的研究,这对于细胞分裂过程中正确的染色体分离至关重要,同时也强调了它在探索着丝粒进化和其他重复基因组区域方面的实用性。可用性和实现:源代码和示例二进制文件可从github.com/gluncic/GRM2023免费下载。
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