Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity.

Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain. Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing (NGS) data. In addition, as somatic SNV detection can be impacted by multiple mutations, with germline mutations and co-occurrence of copy number variations (CNVs) common in organisms, we used the novel approach to distinguish somatic and germline mutations based on the NGS data from individual tumor samples. In summary, svmSomatic: (1) considers the influence of CNV co-occurrence in detecting somatic mutations; and (2) trains a support vector machine algorithm to distinguish between somatic and germline mutations, without requiring normal matched samples. We further tested and compared svmSomatic with other common methods. Results showed that svmSomatic performance, as measured by F1-score, was significantly better than that of others using both simulation and real NGS data.