相关实验视频

Updated: May 22, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

PON-P3:准确预测氨基酸替代品的致病性

Muhammad Kabir¹, Saeed Ahmed¹, Haoyang Zhang¹

¹Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden.

International journal of molecular sciences

|March 13, 2025

概括

我们开发了PON-P3,这是一种用于变体解释的AI工具,其性能优于现有的预测器. 它将变异分为良性,致病性和不确定的意义 (VUSs) 的变异,改善现实世界的临床预测.

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:

变体解释对于遗传疾病诊断至关重要.
当前的病原性预测器往往提供二元预测,限制了临床效用.
现有的工具在复杂的变异分类中扎.

研究的目的:

开发一种基于人工智能 (AI) 的新,准确的工具,用于变体解释.
改进超越二元预测的遗传变异的分类.
为了提高对人类遗传变异的病原性预测.

主要方法:

开发了PON-P3,一种使用随机增强方法的AI工具.
利用了一个训练数据集,其中包含1526个描述变异,序列,结构,基因和蛋白质的特征.
将PON-P3与其他23个预测器进行比较,包括使用大型语言模型和进化数据的预测器.

主要成果:

与包括先进方法在内的现有预测指标相比,PON-P3表现优越.
PON-P3准确地将变异分为三类:良性,致病性和意义不明的变异 (VUS).
应用于人类蛋白质和ClinVar数据,PON-P3预测了12.9%的致病变体和49.9%的良性变体.

结论:

在变体解释准确性和分类方面,PON-P3提供了显著的进步.
与其他方法相比,该工具在现实世界的临床场景中提供了更可靠的预测.
PON-P3有助于对人类蛋白质组和数据库中的遗传变异进行全面分析.

关键词:

生物信息学是一种生物信息学.遗传变异是一种遗传变异.机器学习是机器学习.病原性病原性病原性变化的解释解释变化.

更多相关视频

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

相关概念视频

Mutations

Mutations

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...