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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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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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...
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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
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DNA Isolation01:24

DNA Isolation

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DNA isolation protocols can be fast and straightforward or complex and time-consuming depending on the type and quality of DNA required for further processing. For example, plasmid DNA extraction is a bit more complicated than genomic DNA extraction because of the need for an appropriate lysis method to separate plasmid DNA from gDNA during isolation. However, for specific applications, such as long-range DNA sequencing that require a good yield of high- quality DNA samples, we need to follow...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Next-generation Sequencing03:00

Next-generation Sequencing

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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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相关实验视频

Updated: Jul 3, 2025

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

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一种随机的最佳k-mer索引方法,用于有效的并行基因组序列压缩.

Subhankar Roy1, Anirban Mukhopadhyay2

  • 1Department of Computer Science and Engineering, Academy of Technology, Adisaptagram, Hooghly 712121, West Bengal, India.

Gene
|February 11, 2024
PubMed
概括

有效的基因组压缩对于处理大规模的下一代测序 (NGS) 数据至关重要. 我们的新算法RGCOK显著减少了为压缩大型基因组序列找到最佳k-mer长度所需的时间.

关键词:
这是一个很快的速度.基因组序列格式的格式.哈希希 (Hashing) 是一个叫做哈希希的公司.这是下一代测序.最佳的 k-mer 长度随机化方法是一种随机化方法.以参考为基础的参考.在 k-mer 索引中使用 k-mer 指数.

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相关实验视频

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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:

  • 下一代测序 (NGS) 生成大量的基因组数据,需要高效的压缩方法.
  • 目前的压缩算法面临着处理,存储,传输和分析大规模基因组序列的挑战.
  • 由于它们的决策过程,现有的k-mer哈希索引系统可能会耗时.

研究的目的:

  • 提出使用最佳k-mer长度 (RGCOK) 的高效双相参考基因组压缩算法.
  • 为了利用染色体间的相似性来改进基于参考的压缩.
  • 为了减少在基因组压缩中找到最佳k-mer长度所需的计算时间.

主要方法:

  • 开发了一个双相参考基因组压缩算法 (RGCOK).
  • 采用随机化方法和散列,以确定顺序匹配的最佳k-mer长度.
  • 在使用云计算的SARS-CoV-2,Homo sapiens和其他物种序列等多种数据集上评估性能.

主要成果:

  • RGCOK显著缩短了最佳k-mer长度确定时间.
  • 使用RGCOK的最佳k-mer发现时间约为45.28分钟.
  • 这比现有的最先进的算法 (HiRGC,SCCG,HRCM) 快得多,它们的时间范围从58分钟到8.97小时.

结论:

  • RGCOK提供了一种更节省时间的方法来进行基因组压缩.
  • 该算法有效地利用最佳的k-mer长度识别来实现更快的压缩.
  • RGCOK为管理和分析大规模基因组数据集提供了一个有前途的解决方案.