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Genomics

Genomics

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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深度卷积和条件神经网络用于大规模的基因组数据生成.

Burak Yelmen^1,2, Aurélien Decelle^1,3, Leila Lea Boulos^1,4

¹Université Paris-Saclay, CNRS, INRIA, LISN, Paris, France.

PLoS computational biology

|October 30, 2023

概括

生成神经网络创建人工基因组 (AG),模仿真实基因组数据,解决可扩展性问题. 这些模型为研究提供高质量的,保护隐私的基因组数据.

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科学领域:

基因组学就是基因组学.
计算生物学计算生物学
机器学习机器学习

背景情况:

生成模型越来越多地用于基因组数据分析和生成.
之前的研究表明,生成对抗网络 (GAN) 和受限制的博尔兹曼机器 (RBM) 可以创建人工基因组 (AG).
由于其庞大的特征空间,可扩展性仍然是全基因组数据的挑战.

研究的目的:

为高SNP数的人工基因组生成开发可扩展的生成模型.
评估生成的单元类型的质量,并评估隐私泄露.
使使用基因组数据替代品的伦理研究成为可能.

主要方法:

实现一个新的卷积式Wasserstein GAN (WGAN).
开发一种新的有条件RBM (CRBM) 框架.
对哈普洛型质量和隐私泄露的比较分析.

主要成果:

新的WGAN和CRBM模型有效地产生具有高SNP数量的AG.
这些模型捕获复杂的基因组相关性,并产生多样化,可信的单元类型.
生成的人工基因组段显示,训练数据的隐私泄露最小.

结论:

可扩展的生成神经网络可以产生高质量的人工基因组,具有保存的特征.
这些方法为保护基因组数据中的遗传隐私提供了有希望的方法.
大规模的人工基因组数据库可以促进伦理基因组研究.