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相关概念视频

Genomics02:02

Genomics

36.2K
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...
36.2K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
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.
18.8K
Synthetic Biology02:55

Synthetic Biology

4.7K
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
Golden rice is a genetically modified...
4.7K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
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...
3.9K

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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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针对多组学数据学习的先进计算工具.

Sheikh Mansoor1, Saira Hamid2, Thai Thanh Tuan3

  • 1Department of Plant Resources and Environment, Jeju National University, 63243, Republic of Korea.

Biotechnology advances
|September 9, 2024
PubMed
概括
此摘要是机器生成的。

生物信息学计算工具对于分析复杂的omics数据至关重要. 本综述详细介绍了处理,整合和分析多学科数据的方法,以推进生物医学和植物科学研究.

关键词:
生物信息学是一种生物信息学.生物医学科学 生物医学科学计算工具 计算工具多种omics数据的数据.植物系统的植物系统.预测分析是一种预测分析.

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 奥米克数据是异质的和高维的,需要专门的计算工具.
  • 多omics数据对于大数据研究中的预测分析至关重要.
  • 生物医学和植物科学在很大程度上依赖于先进的数据分析.

研究的目的:

  • 审查omics数据分析的各种计算方法.
  • 突出处理,规范化,整合和分析多omics数据的方法.
  • 强调计算工具在欧米克研究中的重要性.

主要方法:

  • 该评论讨论了各种计算方法,包括基于相似性,基于网络,基于相关性,贝叶斯式,基于融合和多变量技术.
  • 这些方法对于处理复杂的多omics数据至关重要.
  • 重点是这些工具在生物信息学中的应用.

主要成果:

  • 这里介绍了对omics数据计算方法的全面概述.
  • 详细介绍了多领域数据整合和分析的不同方法的独特功能.
  • 该评论强调了这些工具在科学发现中的关键作用.

结论:

  • 计算工具对于现代的数据分析是不可或缺的.
  • 需要复杂的方法来释放多omics数据的潜力.
  • 这些工具显著提升了对生命科学研究成果的理解和影响.