ArticlePLoS genetics2017
Extremely Rare Polymorphisms in Saccharomyces cerevisiae Allow Inference of the Mutational Spectrum.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MuRaL-indel: a deep learning framework for building insertion and deletion mutation rate maps.Briefings in bioinformatics · 2026Article
- Mutational biases and selection in mitochondrial genomes: insights from a comparative analysis of natural and laboratory populations of Caenorhabditis elegans.G3 (Bethesda, Md.) · 2026Article
- Intrinsic base substitution patterns in diverse species reveal links to cancer and metabolism.Genetics · 2022Article
- Role of gene regulation and inter species interaction as a key factor in gut microbiota adaptation.Archives of microbiology · 2022Review
- Article
- Cytosine Methylation Affects the Mutability of Neighboring Nucleotides in Germline and Soma.Genetics · 2020Article
- Deep sequencing of natural and experimental populations ofGenome research · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The characterization of mutational spectra is usually carried out in one of three ways-by direct observation through mutation accumulation (MA) experiments, through parent-offspring sequencing, or by indirect inference from sequence data. Direct observations of spontaneous mutations with MA experiments are limited, given (i) the rarity of spontaneous mutations, (ii) applicability only to laboratory model species with short generation times, and (iii) the possibility that mutational spectra under lab conditions might be different from those observed in nature. Trio sequencing is an elegant solution, but it is not applicable in all organisms. Indirect inference, usually from divergence data, faces no such technical limitations, but rely upon critical assumptions regarding the strength of natural selection that are likely to be violated. Ideally, new mutational events would be directly observed before the biased filter of selection, and without the technical limitations common to lab experiments. One approach is to identify very young mutations from population sequencing data. Here we do so by leveraging two characteristics common to all new mutations-new mutations are necessarily rare in the population, and absent in the genomes of immediate relatives. From 132 clinical yeast strains, we were able to identify 1,425 putatively new mutations and show that they exhibit extremely low signatures of selection, as well as display a mutational spectrum that is similar to that identified by a large scale MA experiment. We verify that population sequencing data are a potential wealth of information for inferring mutational spectra, and should be considered for analysis where MA experiments are infeasible or especially tedious.
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