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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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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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Phylogeny01:23

Phylogeny

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Speciation Rates01:07

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RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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相关实验视频

Updated: Jul 14, 2025

A Practical Guide to Phylogenetics for Nonexperts
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适应性RAxML-NG:在使用数据集难度的最大概率下加速基因推理.

Anastasis Togkousidis1, Oleksiy M Kozlov1, Julia Haag1

  • 1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.

Molecular biology and evolution
|October 7, 2023
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概括
此摘要是机器生成的。

这项研究引入了一个适应式树搜索启发式的基因推理推理,通过根据数据集难度调整搜索彻底性来优化计算效率. 这种新方法显著加快了家族遗传树的搜索速度,特别是对于简单和困难的数据集,同时保持了高准确度.

关键词:
难度 预测 预测 难度启发式听觉学是一种启发式听觉学.这是最大的可能性.人类遗传学 遗传学

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相关实验视频

Last Updated: Jul 14, 2025

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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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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科学领域:

  • 计算生物学 计算生物学
  • 人类遗传学 是一个学科.
  • 机器学习在生物信息学中的应用

背景情况:

  • 使用最大概率的家族遗传推断依赖于启发式树搜索,这可能是计算密集的.
  • 数据集的难度,与最佳树拓的数量和分辨率有关,影响了搜索效率和趋同.
  • 机器学习方法可以预测遗传学数据集的难度,为优化推理策略提供了潜力.

研究的目的:

  • 开发和实施RAxML-NG中的自适应树搜索启发式,根据预测的族系遗传数据集难度调整搜索彻底性.
  • 通过将搜索策略定制为数据集特征来提高家族遗传树推断的计算效率.
  • 在大量实证和模拟数据集中评估适应式启发式的性能和准确性.

主要方法:

  • 在RAxML-NG中实现了自适应树搜索启发式,根据预测的数据集难度修改了搜索强度.
  • 利用机器学习对数据集难度的预测来指导自适应性搜索策略.
  • 在9515个经验和5000个模拟的多个序列对齐 (MSAs) 上测试了适应式启发式,难度不同.

主要成果:

  • 适应式启发式实现了实质性的加速度,特别是在容易和困难的数据集上 (53%的MSA),平均加速度超过10倍.
  • 使用适应策略推断的约94%的树在统计学上与使用标准RAxML-NG策略获得的树无法区分.
  • 适应性策略有效地利用数据集难度预测来优化树搜索效率,而不影响拓准确性.

结论:

  • 适应式树搜索启发式 (adaptive tree search heuristic) 在计算效率上显著提高了族系推理的效率,特别是对于极其困难的数据集.
  • 这种方法提供了一种实用的方法,可以通过智能地根据数据属性分配计算资源来加速家族遗传学分析.
  • 适应性策略证明了将基于机器学习的难度预测集成到用于大规模的族群学研究的启发式搜索算法中的价值.