Nuriddinov Miroslav, Belokopytova Polina, Fishman Veniamin
Institute of Cytology and Genetics (ICG), Novosibirsk 630099, Russia.
Novosibirsk State University (NSU), Department of Natural Sciences, Novosibirsk 630099, Russia.
NAR Genom Bioinform. 2025 Jun 19;7(2):lqaf081. doi: 10.1093/nargab/lqaf081. eCollection 2025 Jun.
Identifying structural variants (SVs) remains a pivotal challenge within genomic studies. The recent advent of chromosome conformation capture (3C) techniques has emerged as a promising avenue for the accurate identification of SVs. However, development and validation of computational methods leveraging 3C data necessitate comprehensive datasets of well-characterized chromosomal rearrangements, which are presently lacking. In this study, we introduce Charm (https://github.com/genomech/Charm): a robust computational framework tailored for Hi-C data simulation. Our findings demonstrate Charm's efficacy in benchmarking both novel and established tools for SV detection. Additionally, we furnish an extensive dataset of simulated Hi-C maps, paving the way for subsequent benchmarking endeavors.
识别结构变异(SVs)仍然是基因组研究中的一个关键挑战。染色体构象捕获(3C)技术的最新出现,已成为准确识别SVs的一个有前景的途径。然而,利用3C数据的计算方法的开发和验证需要特征明确的染色体重排的综合数据集,而目前这些数据集尚不存在。在本研究中,我们引入了Charm(https://github.com/genomech/Charm):一个专为Hi-C数据模拟量身定制的强大计算框架。我们的研究结果证明了Charm在对用于SV检测的新工具和现有工具进行基准测试方面的有效性。此外,我们提供了一个广泛的模拟Hi-C图谱数据集,为后续的基准测试工作铺平了道路。