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相关概念视频

RNA-seq03:21

RNA-seq

9.8K
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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RNA Editing02:23

RNA Editing

8.9K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Sanger Sequencing01:57

Sanger Sequencing

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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...
753.7K
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

6.3K
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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Experimental RNAi02:15

Experimental RNAi

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Fixing Double-strand Breaks02:04

Fixing Double-strand Breaks

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The double-stranded structure of DNA has two major advantages. First, it serves as a safe repository of genetic information where one strand serves as the back-up in case the other strand is damaged. Second, the double-helical structure can be wrapped around proteins called histones to form nucleosomes, which can then be tightly wound to form chromosomes. This way, DNA chains up to 2 inches long can be contained within microscopic structures in a cell. A double-stranded break not only damages...
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相关实验视频

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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使用JIVE对单细胞RNA测序数据进行批量效应校正.

Joseph Hastings1, Donghyung Lee1, Michael J O'Connell1

  • 1Department of Statistics, Miami University, Oxford, OH 45056, United States.

Bioinformatics advances
|October 10, 2024
PubMed
概括

我们对大规模单细胞RNA测序数据增强了联合和个体变异解释 (JIVE) 方法. 我们改进的JIVE有效地纠正批量效应,保存生物信号用于下游分析.

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.
  • 计算生物学 计算生物学

背景情况:

  • 由于技术变异,单细胞RNA测序 (scRNA-seq) 数据中的批量效应会掩盖真正的生物信号.
  • 联合和个体变异解释 (JIVE) 方法可以从多来源数据中的批量效应中解脱共享的生物模式.
  • 现有的JIVE实现是计算密集型的,不适合大规模的scRNA-seq数据集.

研究的目的:

  • 为了提高 JIVE 对大规模 scRNA-seq 数据的计算效率.
  • 开发一个新的JIVE应用程序,用于跨多个scRNA-seq数据集的批量效应校正.
  • 提高生物变异性的提取,同时考虑技术噪声.

主要方法:

  • 实现了一个针对scRNA-seq数据量身定制的计算效率高的JIVE版本.
  • 应用了增强的JIVE来将scRNA-seq数据集分解为联合 (生物) 和单个 (技术) 结构.
  • 将增强的JIVE与已有的批次校正工具进行比较:Seurat v5,Harmony,LIGER和Combat-seq.

主要成果:

  • 与其他工具相比,增强的JIVE方法在保留细胞类型特异性的效果方面表现出卓越的性能.
  • 在批量效应纠正方面,JIVE表现出色,特别是在具有均衡批量大小的数据集中.

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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Detection of Copy Number Alterations Using Single Cell Sequencing

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Detection of Copy Number Alterations Using Single Cell Sequencing
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Detection of Copy Number Alterations Using Single Cell Sequencing

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  • 该方法成功地将生物变异性与批次内的技术变异性分开.

    • 结论:

    • 计算增强的JIVE是大规模scRNA-seq数据中批量效应校正的强大工具.
    • 这种方法有效地隔离了真正的生物信号,促进了更准确的下游分析.
    • 增强的JIVE为单细胞基因组学中的多数据集集成提供了强大的解决方案.