Unfolding the Genome: The Case Study of P. falciparum.

IF 1.2 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Nelle Varoquaux
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引用次数: 0

Abstract

The development of new ways to probe samples for the three-dimensional (3D) structure of DNA paves the way for in depth and systematic analyses of the genome architecture. 3C-like methods coupled with high-throughput sequencing can now assess physical interactions between pairs of loci in a genome-wide fashion, thus enabling the creation of genome-by-genome contact maps. The spreading of such protocols creates many new opportunities for methodological development: how can we infer 3D models from these contact maps? Can such models help us gain insights into biological processes? Several recent studies applied such protocols to P. falciparum (the deadliest of the five human malaria parasites), assessing its genome organization at different moments of its life cycle. With its small genomic size, fairly simple (yet changing) genomic organization during its lifecyle and strong correlation between chromatin folding and gene expression, this parasite is the ideal case study for applying and developing methods to infer 3D models and use them for downstream analysis. Here, I review a set of methods used to build and analyse three-dimensional models from contact maps data with a special highlight on P. falciparum's genome organization.

展开基因组:恶性疟原虫的案例研究。
研究DNA三维结构的新方法的发展为深入和系统地分析基因组结构铺平了道路。类似3c的方法与高通量测序相结合,现在可以以全基因组的方式评估基因座对之间的物理相互作用,从而能够创建逐基因组的接触图谱。这种协议的传播为方法论的发展创造了许多新的机会:我们如何从这些接触图中推断出3D模型?这些模型能帮助我们深入了解生物过程吗?最近的几项研究将这种方案应用于恶性疟原虫(五种人类疟疾寄生虫中最致命的一种),评估了其生命周期不同时刻的基因组组织。由于其基因组大小较小,在其生命周期中相当简单(但不断变化)的基因组组织以及染色质折叠与基因表达之间的强相关性,该寄生虫是应用和开发推断3D模型并将其用于下游分析的方法的理想案例研究。在这里,我回顾了一组用于从接触图数据中构建和分析三维模型的方法,特别强调了恶性疟原虫的基因组组织。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY
CiteScore
2.10
自引率
8.30%
发文量
28
审稿时长
>12 weeks
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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