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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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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.
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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Updated: Jul 15, 2025

Novel Sequence Discovery by Subtractive Genomics
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Novel Sequence Discovery by Subtractive Genomics

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使用最长匹配子字符串进行基于参考的基因组压缩,同时考虑并行化.

Zhiwen Lu1, Lu Guo2, Jianhua Chen3

  • 1School of Information, Yunnan University, KunMing, China.

BMC bioinformatics
|September 30, 2023
PubMed
概括
此摘要是机器生成的。

由于大量的测序输出,高效的基因组数据压缩至关重要. 我们的新算法LMSRGC使用参考基因组和并行处理来实现竞争性压缩比率和时间.

关键词:
一个小小的注意事项基因组的压缩压缩.平行化是平行化的.以参考为基础的参考.后数组是一个后数组.

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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:

  • 高通量测序产生了大量的基因组数据,这给存储和传输带来了挑战.
  • 需要有效的基因组数据压缩算法来管理这种数据泛滥.
  • 利用多核计算进行并行处理是提高算法效率的关键.

研究的目的:

  • 开发一个高效的基因组数据压缩算法.
  • 为了应对存储和传输大规模基因组数据的挑战.
  • 通过并行处理提高基因组数据压缩的速度.

主要方法:

  • 提出了一种使用参考基因组序列的新型算法 (LMSRGC).
  • 使用后数组 (SA) 和最长常见前 (LCP) 数组来识别最长匹配子字符串 (LMS).
  • 使用GPU用于并行SA构建和多线程用于LCP阵列创建和LMS过.

主要成果:

  • 该LMSRGC算法有效地将基因组数据压缩成FASTA格式.
  • 该算法利用SA和LCP阵列特征进行最佳压缩.
  • 使用GPU和多线程并行处理显著加快了压缩过程.

结论:

  • 开发的算法证明了与最先进的方法相比具有竞争力的性能.
  • LMSRGC实现了有利的压缩比.
  • 该算法提供高效的压缩时间,使其适用于大型数据集.