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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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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Novel Sequence Discovery by Subtractive Genomics
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Novel Sequence Discovery by Subtractive Genomics

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在长时间读取的基因组数据中,使用序列组成解共生物和污染.

Claudia C Weber1

  • 1Tree of Life, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK.

G3 (Bethesda, Md.)
|August 16, 2024
PubMed
概括

这项研究引入了一种新的计算方法,用于从环境样本中分离混合基因组. 它利用序列组成来识别共生物和污染物,减少对参考数据库的依赖.

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:

  • 基因组测序项目正在扩展到各种生命形式,在分析混合物种样本方面创造了计算挑战.
  • 环境样本经常含有目标生物与共生伙伴 (共生体) 和污染物一起,需要有效的序列分离方法.
  • 目前基于参考的方法与代表性不足的真核生物种群作斗争,突出了需要替代方法的需求.

研究的目的:

  • 在测序数据中开发和评估一种计算策略,以区分目标生物,共生物和污染物.
  • 通过利用内在序列组合差异,尽量减少对综合参考数据库的依赖.
  • 为了分析复杂的环境样本,包括来自达尔文生命之树项目的样本.

主要方法:

  • 使用变量自编码器来学习读取四核酸组成的二维表示.
  • 可视化这些学习的嵌入,以识别样本内的不同的有机成分.
  • 整合额外的数据,如编码密度,覆盖范围和分类学标签,用于注释和评估.
  • 将该方法应用于来自达尔文生命之树项目的大规模昆虫基因组数据.

主要成果:

  • 代表不同生物体的不同集群在序列组合嵌入中成功可视化.
  • 该方法证明了可扩展到数百万个序列的可扩展性,使得未组装的读取集的分析成为可能.
关键词:
数据可视化数据可视化长时间阅读序列排序.没有监督的学习学习.变量自动编码器变量自动编码器

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Transposon-insertion Sequencing as a Tool to Elucidate Bacterial Colonization Factors in a Burkholderia gladioli Symbiont of Lagria villosa Beetles
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  • 交互式可视化有助于快速评估和识别样本组件.
  • 通过基于参考的查识别的大量共生生物得到了证实,并检索了新的基因组.

    • 结论:

    • 序列组合分析,特别是使用像变异自编码器这样的维度减小技术,为剖析复杂的环境测序数据提供了强大的参考光方法.
    • 这种方法有效地识别了共生物和污染物,并有助于检索缺乏稳健参考数据的基因组.
    • 这种方法是可扩展的,对于像达尔文生命之树项目这样的大规模基因组测序计划来说是有价值的.