Foretova, L. | Search bioRxiv

bioRxiv2026

Systemic mutagen exposures reported by normal kidney cell genomes

Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. The mutational signatures of mutagens in normal tissues offer opportunities to detect such exposures and survey them at population level. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that normal kidney cell genomes report an extensive repertoire of somatic mutational signatures. Microdissection of kidney structures revealed that proximal tubules exhibit higher mutation rates than other components of the nephron and most normal cell types despite low cell division rates. This is explained by marked enrichment of mutational signatures due to known exogenous carcinogenic mutagens including the plant-derived aristolochic acids, as well as several signatures of unknown causes including an unknown agent prevalent in Japan (SBS12), and signatures of uncertain origins (SBS40b and SBS40c). The results suggest the existence of multiple, common, systemically circulating mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

genomics↗

bioRxiv2019

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

The emergence of Next-Generation Sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analyzing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub plateform: https://github.com/IARCbioinfo/needlestack.

bioinformatics↗