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RNA-seq

RNA-seq

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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Updated: Jul 1, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

SAW:一种高效准确的数据分析工作流程,用于立体序列空间转录学.

Chun Gong¹, Shengkang Li¹, Leying Wang¹

¹BGI-Shenzhen, Shenzhen, Guangdong, China.

GigaByte (Hong Kong, China)

|March 4, 2024

概括

此摘要是机器生成的。

一个新的空间转录学分析工作流,SAW,解决了大型数据集中的性能问题. 这种高性能工作流显著加快了空间数据分析的速度,提高了研究人员的效率.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

相关实验视频

Last Updated: Jul 1, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

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:

空间转录组学分析需要来自空间位置和单个细胞的基因表达数据.
目前的分析工具面临性能挑战,包括空间定位和RNA基因组对齐的高计算成本,以及对大数据集的过度内存使用.
这些局限性阻碍了空间转录学分析的效率和广泛应用.

研究的目的:

为了开发一个高性能和准确的空间转录学数据分析工作流量量身定制为立体-seq技术.
克服现有分析方法的计算瓶和内存低效率,用于大规模的空间转录数据.

主要方法:

开发了立体序列分析工作流 (SAW),用于空间转录组学数据的计算管道.
SAW集成了mRNA空间位置重建,基因组对齐,基因表达矩阵生成和细胞聚类.
工作流程旨在有效处理来自立体相继技术的数据.

主要成果:

SAW在空间转录组学数据分析中表现出高性能和准确性.
一个1GB的立体-seq芯片数据集 (1x1厘米) 的整个分析大约在148分钟内完成.
与未经优化的工作流相比,这意味着速度增加了1.8倍.

结论:

SAW提供了一种高效准确的解决方案,用于分析由Stereo-seq.q.生成的大型空间转录组数据集.
工作流的性能提高提高了空间转录学研究的适用性和效率.
SAW以通用格式输出数据,方便下游个性化分析.