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相关概念视频

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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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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单细胞RNA-seq数据的表示学习.

Constantin Ahlmann-Eltze1, Florian Barkmann2, Jan Lause3

  • 1University College London.

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概括
此摘要是机器生成的。

单细胞RNA测序 (scRNA-seq) 数据的表示学习方法解决了诸如高维度和噪声等挑战. 本综述对关键方法进行了分类,有助于未来对单细胞转录组学分析的研究.

关键词:
一个基准的基准.基金会模型 基金会模型审查 审查 审查 审查单细胞机是一种单细胞机.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 数据科学数据科学数据科学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 产生高维度,稀疏和杂的基因表达数据.
  • 超过1亿个单细胞转录组是公开的,需要先进的分析方法.
  • 代表性学习旨在创建scRNA-seq数据的有效的低维表示.

研究的目的:

  • 对scRNA-seq数据的主要表示学习范式进行审查和分类.
  • 阐述这些方法的概念基础,假设和区别.
  • 确定当前的挑战和该领域的未来研究方向.

主要方法:

  • 这些是因子模型.
  • 自动编码器 自动编码器
  • 相反的学习方法对比的学习方法.
  • 基于变压器的基础模型

主要成果:

  • 这些方法学习了对下游分析 (如集群和可视化) 的低维表示.
  • 新兴的方法可以从跨实验的聚合scRNA-seq数据中学习潜在的表示.
  • 提供了一个分类学,澄清了scRNA-seq.q.的表示学习格局.

结论:

  • 代表性学习对于从复杂的scRNA-seq数据集中提取有意义的生物学见解至关重要.
  • 了解不同的范式是选择特定研究问题的适当方法的关键.
  • 未来的工作应该集中在解决现有的基准和开放挑战,以推进该领域.