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相关概念视频

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
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

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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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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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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.
18.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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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: Jun 3, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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BetaAlign:用于多个序列对齐的深度学习方法.

Edo Dotan1,2, Elya Wygoda1, Noa Ecker1

  • 1The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

Bioinformatics (Oxford, England)
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

使用自然语言处理 (NLP) 的人工智能 (AI) 为多重序列对齐 (MSA) 提供了一种新的方法. 这种基于人工智能的方法的准确性与当前的工具相当或超过,推动了生物信息学和家族遗传学的发展.

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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:

  • 多重序列对齐 (MSA) 对于生物序列分析至关重要,包括遗传学和蛋白质结构预测.
  • 传统的MSA方法面临着复杂的进化动态带来的挑战.
  • 人工智能 (AI) 的整合为改善MSA推断提供了一个新的途径.

研究的目的:

  • 使用自然语言处理 (NLP) 技术引入和评估基于人工智能的多次序对齐 (MSA) 方法.
  • 展示NLP算法的潜力,以解决传统MSA计算中的局限性.
  • 为了提高序列对齐的准确性和效率,用于各种生物应用.

主要方法:

  • 开发了一个基于AI的方法,BetaAlign,利用NLP变压器模型推断MSA.
  • 在模拟的对齐上训练了AI模型,以捕捉特定的进化动态.
  • 研究了训练数据大小,变压器架构和子空间学习对对齐精度的影响.

主要成果:

  • 在MSA推断中,BetaAlign实现了高准确度,性能与最先进的对齐工具相当,有时甚至超过了它们.
  • 该研究描述了BetaAlign的性能,确定了影响其准确性的关键因素.
  • 引入了一种新的技术,这使得AI对齐器的性能比以前的代更好.

结论:

  • 基于人工智能的方法,特别是那些使用NLP的方法,显示出革命性序列对齐的重大前景.
  • 这些NLP解决方案有可能取代或增强MSA和其他复杂的推理任务的传统算法.
  • 这些发现凸显了人工智能在促进生物信息学和比较基因组学的发展方面日益重要.