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Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Updated: Jul 21, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

估计单分子蛋白质测序实验的错误率.

Matthew Beauregard Smith^1,2, Kent VanderVelden³, Thomas Blom³

¹Oden Institute, The University of Texas at Austin, Austin, TX 78712.

bioRxiv : the preprint server for biology

|July 28, 2023

概括

此摘要是机器生成的。

准确的错误率估计对于单分子蛋白质测序 (SMPS) 技术至关重要. 我们开发了两种方法,包括隐藏马尔科夫模型 (HMM) 方法,以准确估计光测序应用的SMPS错误率.

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

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相关实验视频

Last Updated: Jul 21, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

科学领域:

生物技术是生物技术.
计算生物学计算生物学
蛋白质组学是指蛋白质组学

背景情况:

单分子蛋白质测序 (SMPS) 技术正在出现,但实际使用需要精确的错误率估计.
光测序是一种关键的SMPS技术,需要强大的方法来分析其相关的错误概况.

研究的目的:

开发和评估两种不同的参数估计方法来分析SMPS光测序读数.
准确量化SMPS数据中的错误率,包括错过的切割,染料损失,脱离和N端阻塞.

主要方法:

一个基于隐藏马尔科夫模型 (HMM) 的方法,扩展"什么"的概念,使用修改的姆-韦尔奇算法进行参数估计.
第二种方法采用了DIRECT和威尔优化技术的混合体,以最大限度地减少模拟和真实数据之间的根平均平方误差 (RMSE).

主要成果:

基于HMM的方法在模拟数据上表现出高准确度,并在纳入N终端封锁和预处理后为实验数据集提供了合理的参数化.
基于Baum-Welch的HMM方法在与模拟和真实数据集相比,在大多数标准上普遍优于DIRECT和威尔的混合方法.
两种开发的方法都为实验单分子测序数据提供了类似的误差率估计.

结论:

开发的参数估计方法,特别是基于HMM的方法,提供了评估SMPS光测序错误率的原则方法.
准确的错误率估计对于推进单分子蛋白质测序技术的实际应用和可靠性至关重要.