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相关概念视频

RNA-seq03:21

RNA-seq

10.0K
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...
10.0K
Leaky Scanning02:28

Leaky Scanning

5.2K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.2K
Next-generation Sequencing03:00

Next-generation Sequencing

89.9K
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....
89.9K
Alternative RNA Splicing02:18

Alternative RNA Splicing

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相关实验视频

Updated: Jul 16, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

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一种基于f-分歧的新型生成对抗性归算方法用于scRNA-seq数据分析.

Tong Si, Zackary Hopkins, John Yanev

    bioRxiv : the preprint server for biology
    |September 11, 2023
    PubMed
    概括

    sc-f GAIN有效地使用新型生成对抗网络在单细胞RNA测序 (scRNA-seq) 数据中赋值缺失值. 这种方法克服了传统方法的局限性,改善了细胞多样性分析和个性化疗法开发.

    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:

    • 单细胞RNA测序 (scRNA-seq) 对于理解细胞多样性和开发个性化疗法至关重要.
    • 在scRNA-seq数据中缺失的值或丢失存在重大分析挑战.
    • 传统的归算方法通常依赖于限制性的分布假设,在高缺失率下表现不佳.

    结论:

    • sc-f GAIN提供了一个通用且强大的解决方案,用于在scRNA-seq数据中赋值缺失的值.
    • 精确的归算增强了细胞异质性的分析,并支持精准医学倡议.
    • 这种先进的归算技术有望在转录基因数据分析中得到更广泛的应用.

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