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相关概念视频

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 Glycosylation01:25

Protein Glycosylation

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Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Genomics02:02

Genomics

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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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Proteoglycans01:05

Proteoglycans

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Glycans, a class of complex heterogeneous molecules, can be covalently attached to proteins to form glycosylated proteins that regulate various physiological and pathological processes. Glycosylated proteins or glycoproteins comprise N-linked and O-linked oligosaccharides. O-glycosylation is the most common type of protein glycosylation. Here, glycans attach to the oxygen atom of the hydroxyl groups of Serine or Threonine residues. O-linked glycosylation occurs later in protein processing,...
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相关实验视频

Updated: Jun 4, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Petagraph:一个大规模的统一知识图框架,用于整合生物分子和生物医学数据.

Benjamin J Stear1, Taha Mohseni Ahooyi1, J Alan Simmons2

  • 1Department of Biomedical and Health Informatics (DBHI), The Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Scientific data
|December 18, 2024
PubMed
概括

Petagraph是一个生物医学知识图,集成复杂的多omics数据. 该工具帮助研究人员分析和理解大型数据集中的关系,推动生物医学研究.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 基因组学就是基因组学.
  • 数据科学数据科学数据科学

背景情况:

  • 生物医学数据量和复杂性的快速增长带来了重大整合挑战.
  • 多omics数据分析需要复杂的工具来充分利用其潜力.

研究的目的:

  • 开发Petagraph,一个全面的生物医学知识图表,用于高效的多主题数据集成和分析.
  • 为研究人员提供一个连贯的数据环境,以探索复杂的生物关系.

主要方法:

  • 开发Petagraph,一个拥有超过3200万个节点和1.18亿个关系的知识图.
  • 在统一生物医学知识图 (UBKG) 中利用超过180个本体和标准.
  • 将数百万个定量基因组学数据点嵌入到图形结构中.

主要成果:

  • Petagraph成功地整合了各种生物医学数据,包括定量基因组学.
  • 知识图方便在跨多主题数据集的高效分析,注释和关系发现.
  • 证明了Petagraph查询在为各种研究场景产生有意义的见解方面的实用性.

结论:

  • Petagraph提供了一种强大的解决方案,用于管理和分析大规模的复杂生物医学数据.
  • UBKG的注释支架支持增强的数据解释和发现.
  • 佩塔格拉夫是推动多学科研究和生物医学数据科学的宝贵资源.