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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

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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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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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Leaky Scanning02:28

Leaky Scanning

5.1K
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...
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相关实验视频

Updated: Jun 7, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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TIdeS:一个全面的框架,用于准确的开放阅读框架识别和分类在真核生物转录组.

Xyrus X Maurer-Alcalá1, Eunsoo Kim1,2

  • 1Division of Invertebrate Zoology and Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA.

Genome biology and evolution
|November 21, 2024
PubMed
概括

一个新的框架,转录识别和选择 (TIdeS),改善了从真核细胞转录组的开放阅读框架 (ORF) 预测,即使有污染. TIdeS为分析复杂的生物相互作用和策划基因组数据集提供了强大的解决方案.

关键词:
在ORF预测预测.生物相互作用 生物相互作用污染污染污染污染的情况机器学习是机器学习.人类基因组学是什么?

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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

Published on: March 7, 2018

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相关实验视频

Last Updated: Jun 7, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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

  • 欧核生物基因组学
  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学

背景情况:

  • 分析真核生物遗传信息带来了污染和复杂的生物相互作用等挑战,特别是对于未培养的生物体.
  • 目前用于从转录组中预测开放阅读框架 (ORF) 的工具在这些复杂的场景中往往不足.

研究的目的:

  • 引入转录识别和选择 (TIdeS),这是一个新的框架,旨在克服ORF预测当前omics方法的局限性.
  • 为准确的ORF预测和随后的真核生物转录和基因组数据的净化提供强大的解决方案.

主要方法:

  • 开发和应用转录识别和选择 (TIdeS) 框架.
  • 在32种不同的真核生物种群的转录组上测试TIdeS.
  • 将TIdeS性能与传统的ORF预测方法 (如TransDecoder) 的比较.

主要成果:

  • TIdeS显著优于传统的ORF预测方法,识别了更高比例的完整和框架内ORF.
  • TIdeS使用最小的输入数据准确地对ORF进行分类,即使存在大量污染,也显示出有效性.
  • 该框架成功地处理复杂的生物相互作用,如宿主-共生体和猎物-掠食者关系.

结论:

  • TIdeS提供了一种灵活的,单一的解决方案,用于准确的ORF预测和数据集净化在真核转录学中.
  • 该框架促进了对生物相互作用的强有力的探索和可复制的数据集策划,用于家族基因组学研究及其他领域.