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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...

Ribosome Profiling

Ribosome Profiling

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.
The technique...

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Updated: Jan 16, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

一个基于变压器的深度扩散模型,用于批量RNA-Seq解卷.

Yunqing Liu¹, Jinlei Sun¹, Huanli Li¹

¹School of Computer Science, Luoyang Institute of Science and Technology, Luoyang 471000, China.

|September 27, 2025

概括

此摘要是机器生成的。

DiffFormer是一种新的计算解卷工具,可以从大量RNA-seq数据中准确推断细胞类型比例. 它的变压器架构显著提高了复杂生物组织的精度.

关键词:

变压器变压器变压器生物信息学是一种生物信息学.大量的RNA-seqq.计算式解卷化解卷化深度学习是一种深度学习.扩散模型的扩散模型.

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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

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

相关实验视频

Last Updated: Jan 16, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

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

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

大量RNA-seq提供了平均基因表达,但缺乏单细胞分辨率,限制了细胞异质性的洞察力.
计算解卷方法旨在从大量RNA-seq中估计细胞类型比例,但准确性仍然是一个挑战,特别是在复杂的组织中.

研究的目的:

介绍DiffFormer,一个新的解卷模型,集成条件扩散和变压器架构.
评估DiffFormer的性能与现有方法和基线扩散模型相比.

主要方法:

开发了DiffFormer,这是一个结合条件扩散和变压器架构的新型解卷模型.
在四个伪批量数据集上进行系统评估,并在现实世界数据集上进行验证,以FACS为基础的地面真相.

主要成果:

在所有测试的数据集中,DiffFormer表现出优越且一致的性能.
超越了现有的解卷方法和基线基于MLP的扩散模型 (DiffMLP).
在真实数据上实现了根平均平方误差 (RMSE) 的显著降低和最高的皮尔森相关系数 (PCC).

结论:

DiffFormer提供了一个高精度,可复制的工具,用于细胞解卷.
变压器架构被认为是DiffFormer成功的关键,证明了它对复杂生物信息学问题的潜力.