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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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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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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.
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Next-generation Sequencing03:00

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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.
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Updated: May 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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不同变异类型的解卷和分化推断,将批量DNA-seq与单细胞RNA-seq集成在一起.

Nishat Anjum Bristy1, Russell Schwartz1,2

  • 1Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.

bioRxiv : the preprint server for biology
|February 20, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了TUSV-int,这是一种新的计算方法,它集成了大量DNA测序和单细胞RNA测序数据,以重建精确的瘤遗传学. TUSV-int通过分析单核酸变异,副本数量变化和结构变异来增强克隆子结构和突变史的分辨率.

关键词:
癌症 癌症 癌症 癌症 癌症整数线性编程的整数线性编程测序的测序是指测序的测序.一个单细胞的单细胞.身体进化的体质演变.瘤的原始发育过程

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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相关实验视频

Last Updated: May 27, 2025

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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科学领域:

  • 癌症基因组学 癌症基因组学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 重建克隆血统树对于理解癌症基因组学至关重要.
  • 传统的批量DNA测序 (DNA-seq) 缺乏分辨率,而单细胞DNA测序 (scDNA-seq) 是昂贵且有限的.
  • 单细胞RNA测序 (scRNA-seq) 是广泛可用的,但对检测结构变异的基因组覆盖范围有限.

研究的目的:

  • 开发一种结合大量DNA-seq和scRNA-seq数据的计算方法,以改善瘤遗传学.
  • 为了使单核酸变异 (SNVs),复制数变异 (CNAs) 和结构变异 (SVs) 的同时分析.
  • 通过结合不同基因组技术的优势,克服现有方法的局限性.

主要方法:

  • 开发了TUSV-int,一种使用整数线性编程 (ILP) 的方法.
  • 集成的大量DNA-seq和scRNA-seq数据用于解卷和遗传学推断.
  • 应用于一个乳腺癌数据集与现有的DNA-seq和scRNA-seq数据.

主要成果:

  • 与使用有限数据或变异类型的方法相比,TUSV-int表现出更好的解卷性能.
  • 该方法有效地解决了克隆结构和突变历史.
  • 成功应用于已发表的乳腺癌数据集,展示了增强的分辨率.

结论:

  • TUSV-int通过整合各种基因组数据,为癌症遗传学提供了一种强大的方法.
  • 该方法增强了克隆子结构和突变事件的分辨率.
  • 为癌症基因组学研究提供了宝贵的工具,特别是用于分析复杂的结构变异.