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相关概念视频

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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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...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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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相关实验视频

Updated: Jun 20, 2025

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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记忆有限的k-mer选择大和进化多样化的参考库.

Ali Osman Berk Şapcı, Siavash Mirarab

    bioRxiv : the preprint server for biology
    |July 19, 2024
    PubMed
    概括

    KRANK (K-mer RANKer) 是一种新的算法,可以有效地从大型基因组数据库中选择固定大小的k-mers子集. 这种k-mer选择方法最大限度地减少了内存的使用,同时保持了对元基因组分类的高精度.

    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:

    • 基于K-mer的序列匹配对于元基因组分类至关重要.
    • 越来越多的参考数据库带来了可扩展性挑战,因为对k-mers的内存需求很大.
    • 现有的子采样策略,如最小化器,对于不平衡的数据集是不够的.

    研究的目的:

    • 开发一种方法来从超大数据集中选择固定大小的k-mers子集,以尽量减少分类准确性损失.
    • 解决当前k-mer亚抽样方法的局限性,特别是对于分类学上不平衡的微生物库.
    • 提出一个高效的k-mer图书馆构建算法,以改善元基因组分析.

    主要方法:

    • 对超大数据集的各种k-mer选择策略的探索.
    • 开发和实施KRANK (K-mer RANKer) 算法.
    • 克兰克结合了层次选择,适应性尺寸限制和公平覆盖.
    • KRANK与CONSULT-II局部敏感哈希分类器的整合.

    主要成果:

    • 与现有的方法相比,KRANK显著降低了内存消耗.
    • 在KRANK.中观察到对分类学分类准确性的最小损失.

    更多相关视频

    Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
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    Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

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    Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples
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    Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

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    相关实验视频

    Last Updated: Jun 20, 2025

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
    07:49

    Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

    Published on: August 16, 2017

    7.0K
    Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
    13:03

    Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

    Published on: March 8, 2018

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    Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples
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    Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

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  • 基于KRANK的分类学概况优于其他k-mer替代方案.
  • 在CAMI基准中,KRANK的准确性与基于标记的方法相美.
  • 结论:

    • 在大规模的元基因组学中,KRANK为存储密集的k-mer选择提供了一个有效的解决方案.
    • 该算法在使用减少计算资源的分类学分析中表现出卓越的性能.
    • 克兰克为分析复杂微生物群落提供了可扩展和准确的方法.