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Spontaneous and Induced Mutations

Spontaneous and Induced Mutations

Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).

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相关实验视频

Updated: Jul 18, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

使用SigProfilerMatrixGenerator可视化和探索大型突变事件的模式.

Azhar Khandekar^1,2,3, Raviteja Vangara^1,2,3, Mark Barnes^1,2,3

¹Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, 92093, USA.

|August 22, 2023

概括

一个新的生物信息学工具,SigProfilerMatrixGenerator,现在可以有效地探索和可视化癌症基因组中大规模的体质突变事件. 该工具支持副本数和结构变异,为癌症生物学提供了新的见解.

关键词:

副本编号签名副本编号签名突变模式突变模式结构变体签名的结构变体签名

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

相关实验视频

Last Updated: Jul 18, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

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.
癌症研究癌症研究

背景情况:

癌症的特征是体质突变,从小的基对变化到大的结构和副本数变异.
虽然存在用于小突变事件的工具,但缺乏分析癌症基因组中大规模突变事件的计算效率高的方法.
最近的分类方案强调了研究人类癌症中大规模突变事件的实用性.

研究的目的:

引入一种计算高效的生物信息学工具,用于可视化和探索癌症中大规模突变事件.
在已建立的分类体系中,提供综合能力来检查副本数量变体和结构变体.

主要方法:

开发新版本的SigProfilerMatrixGenerator,这是一个基于Python的生物信息学工具.
整合用于分析副本数量变体和结构变体的支持.
包括与两个已建立的分类方案和多个数据模式/算法的兼容性.
为R环境用户提供R包装包.

主要成果:

SigProfilerMatrixGenerator现在提供了集成的功能,用于分析大型突变事件,包括副本数和结构变异.
该工具支持来自各种算法和数据模式的数据,增强其适用性.
有一个带有R包装的Python实现可用,满足不同的用户偏好.

结论:

更新后的SigProfilerMatrixGenerator是第一个标准化生物信息学工具,用于探索和可视化使用已建立的方案的副本数和结构变体.
该工具有助于在癌症研究中优化对大规模突变事件的分析.
该软件是免费访问的,附有全面的文档.