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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...

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...

Types of RNA

Types of RNA

Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...

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Updated: Jun 29, 2025

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Seq-RBPPred:从序列中预测RNA结合蛋白

Yuyao Yan¹, Wenran Li¹, Sijia Wang¹

¹CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200021, China.

|March 25, 2024

概括

此摘要是机器生成的。

使用序列数据,Seq-RBPPred准确地识别了RNA结合蛋白 (RBPs). 这种计算方法为实验预测提供了更快,更具成本效益的替代方案,改善了跨物种的RBP识别.

更多相关视频

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

Published on: September 30, 2016

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

相关实验视频

Last Updated: Jun 29, 2025

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

Published on: September 30, 2016

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

科学领域:

分子生物学分子生物学
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:

RNA结合蛋白 (RBPs) 是RNA代谢的关键调节者,影响翻译,修改和拼接.
准确的RBP识别对于理解基本的生物过程至关重要.
实验RBP预测是耗时且昂贵的,需要高效的计算方法.

研究的目的:

开发一种新的计算方法,Seq-RBPPred,用于根据蛋白序列预测RNA结合蛋白 (RBPs).
提高RBP识别的准确性和效率,特别是目前在各种物种中未识别的RBP.

主要方法:

Seq-RBPPred采用特征表示方法,将生物物理特性与来自蛋白质序列的隐藏状态特征集成.
该方法在测试数据集上使用全面的性能指标进行了评估.

主要成果:

Seq-RBPPred实现了高性能,整体精度为0.922.
关键性能指标包括灵敏度 (0.926),特异性 (0.903) 和0.757.75的马修相关系数 (MCC).
与现有的最先进的方法相比,Seq-RBPPred证明了它的优越性.

结论:

Seq-RBPPred为RBP预测提供了一个强大而准确的计算工具.
该方法增强了RBPs的识别,有助于更深入地了解基因调节和生物的功能.
开发的工具和相关数据是公开的,以促进进一步的研究.