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相关概念视频

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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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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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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.
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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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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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相关实验视频

Updated: Jun 10, 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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DNASimCLR:一种基于对比学习的深度学习方法,用于基因序列数据分类.

Minghao Yang1,2, Zehua Wang2, Zizhuo Yan2

  • 1Shandong University, Weihai, People's Republic of China.

BMC bioinformatics
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

无监督深度学习框架DNASimCLR有效地从微生物基因序列中提取特征. 这种方法超越了目前基因序列分类的技术,为基因组学提供了强大的解决方案.

关键词:
生物序列数据的数据.相反的学习学习.卷积神经网络是一种卷积神经网络.代表性的学习学习.序列分类 序列分类 序列分类这是一个SimCLR.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 深度神经网络推进了微生物序列数据分析.
  • 标记微生物数据的稀缺性阻碍了监督学习.
  • 无监督学习对于复杂的生物数据至关重要.

研究的目的:

  • 介绍DNASimCLR,一种用于基因序列特征提取的无监督框架.
  • 用有限的标记微生物数据解决监督学习的局限性.
  • 提高微生物序列数据的分析.

主要方法:

  • 使用卷积神经网络 (CNN) 和SimCLR框架.
  • 采用对比学习来从各种微生物基因序列中提取特征.
  • 在大规模未标记的元基因组和病毒基因数据集上进行预训练.

主要成果:

  • DNASimCLR的性能与最先进的方法相美.
  • 优于现有的基于CNN的特征提取技术.
  • 在各种生物序列分析任务中表现出强大的适应性和卓越的性能.

结论:

  • DNASimCLR为基因序列分类提供了一个强大的,数据库无关的解决方案.
  • 对于新的和未见的基因序列是有效的,对于各种基因组学应用是有价值的.
  • 在微生物基因组学中推进无监督学习.