DNA测序
计算生物学
突变
突变率
深度测序
生物
基因组
计算机科学
遗传学
基因
作者
Yang Luan,X. Z. You,Jin Yang
出处
期刊:PubMed
日期:2024-02-20
卷期号:46 (2): 126-139
标识
DOI:10.16288/j.yczz.23-309
摘要
Mutation accumulation in somatic cells contributes to cancer development, aging and many non-malignant diseases. The true mutation frequency in normal cells is extremely low, which presents a challenge in detecting these mutations at such low frequencies. The emergence of next-generation sequencing (NGS) technology enables direct detection of rare mutations across the entire genome of any species. This breakthrough overcomes numerous limitations of traditional mutation detection techniques that rely on specific detection models and sites. However, conventional NGS is limited in its application for detecting low-frequency mutations due to its high sequencing error rate. To address this challenge, high-accuracy NGS sequencing techniques based on molecular consensus sequencing strategies have been developed. These techniques have the ability to correct sequencing errors, resulting in error rates lower than 10
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