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相关概念视频

Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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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.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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MCPNet:一个基于最大容量的基因组规模基因网络构建框架.

Tony C Pan1,2, Sriram P Chockalingam2, Maneesha Aluru3

  • 1Department of Biomedical Informatics, Emory University, Woodruff Memorial Research Building 101 Woodruff Circle, 4th Floor East, Atlanta, GA 30322, United States.

Bioinformatics (Oxford, England)
|June 8, 2023
PubMed
概括
此摘要是机器生成的。

MCPNet是一种新的基因网络重建工具,可以有效地识别基因与基因之间的相互作用. 它实现了大数据集的高质量,性能和可扩展性,优于现有方法.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 从表达数据中重建基因网络是计算密集和数据依赖的.
  • 像皮尔森相关和贝叶斯网络这样的现有方法在效率,可扩展性或准确性方面存在局限性.
  • 需要一种方法来平衡计算效率,可扩展性和高质量的网络输出.

研究的目的:

  • 开发一种新型指标,即最大容量路径 (MCP) 评分,用于量化基因相互作用.
  • 推出MCPNet,一个高效并行软件用于基因网络重建.
  • 与现有工具相比,展示MCPNet在质量,速度和可扩展性方面的卓越性能.

主要方法:

  • 开发了最大容量路径 (MCP) 评分来测量直接和间接的基因相互作用.
  • 创建了MCPNet,这是一个并行软件,实现了MCP得分,用于无监督和集体网络推断.
  • 使用Saccharomyces cerevisiae和Arabidopsis thaliana的合成和真实数据集验证了MCPNet.

主要成果:

  • MCPNet实现了更高的网络质量,正如AUPRC (精度召回曲线下的区域) 所示.
  • 与所有经过测试的基因网络重建软件相比,MCPNet的计算时间明显更快.
  • 该软件表现出极好的可扩展性,有效地处理数万个基因和数百个CPU核心.

结论:

  • MCPNet是一种有效的基因网络重建工具,克服了以前方法的局限性.
  • 该软件为分析大规模基因表达数据提供了强大的解决方案.
  • 在基因网络推断中,MCPNet在实现质量,性能和可扩展性方面取得了重大进展.