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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Multi-species Conserved Sequences
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Evolutionary Relationships through Genome Comparisons
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
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多DGD:一个多功能深度生成模型,用于多omics数据.
Viktoria Schuster1,2, Emma Dann3, Anders Krogh4,5
1Department of Computer Science, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark.
Nature communications
|November 20, 2024
概括
我们开发了multiDGD,这是一个可扩展的深度生成模型,用于整合单细胞多omics数据,改进基因表达和染色质可访问性的分析. 这种方法增强了数据的重建,并揭示了基因和调控元素之间的关联.
更多相关视频
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 单细胞基因组学使得基因表达和染色体可访问性的联合分析成为可能.
- 越来越复杂的多omics数据需要可扩展的集成方法.
- 现有的模型往往缺乏用于多模式数据分析的功能或可扩展性.
研究的目的:
- 介绍multiDGD,这是一个可扩展的深度生成模型,用于多模式单细胞数据集成.
- 为学习转录组和染色质可访问性的共享表示提供一个概率框架.
- 促进下游分析,如数据集成和关联检测.
主要方法:
- 开发了一个可扩展的深度生成模型 (multiDGD).
- 利用一个概率框架来学习联合表示.
- 采集了样本共变量的概率建模,用于后期整合.
- 将模型应用于人类和小鼠单细胞数据集.
主要成果:
- 在没有特征选择的情况下,multiDGD在数据重建方面表现出色.
- 该模型学习了转录组和染色质可访问性的集群的联合表示.
- 概率共变量建模允许在没有微调的情况下进行后期数据集成.
- 多DGD有效地检测基因和调节区域之间的统计关联.
结论:
- multiDGD提供了一个可扩展和有效的解决方案,用于整合多模式单细胞基因组学数据.
- 概率框架促进了强大的数据集成和下游分析.
- 该模型推进了基因表达和染色体可访问性关系的分析.


