Detection of simple and complexde novomutations without, with, or with multiple reference sequences

Abstract

The characterization of de novo mutations in regions of high sequence and structural diversity from whole genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, where short-reads do not capture the long-range context required for resolution, and mapping approaches, where improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multi-sample, coloured de Bruijn graphs from shortread data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four Plasmodium falciparum experimental crosses, using long-read data on the parents to inform reconstructions of the progeny and to detect several known and novel non-allelic homologous recombination events.

Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur

“…By characterizing mutation rate variation across the genome and between generations, we may be able to shed light on the impacts of biological processes such as sex and parental age biases. Ultimately, by quantifying the variation in de novo mutation rates across the Tree of Life, we can refine hypotheses regarding the relationship between mutation rates and life history characteristics (Agarwal and Przeworski, 2019;Fazalova and Nevado, 2020;Garimella et al, 2020;Wang et al, 2020;Wu et al, 2020).…”

Mycobacterium tuberculosis complex lineage 5 exhibits high levels of within-lineage genomic diversity and differing gene content compared to the type strain H37Rv

“…This can take two forms: a composite genome containing all the genes found in strains of all lineages and sub-lineages of the MTBC (i.e. both the core and accessory genome) [86], represented as a graph instead of a single sequence [87][88][89][90][91][92], or a selection of reference genomes, with mapping to all or a subset undertaken, as has been done with strains of Mycobacterium chimaera and other pathogens [93,94]. Other authors have also reported that, because of the genetic variability between strains, using a single strain genome as reference genome lacks accuracy [95][96][97].…”

Mycobacterium tuberculosiscomplex lineage 5 exhibits high levels of within-lineage genomic diversity and differing gene content compared to the type strain H37Rv

“…is usually represented as a graph instead of a single sequence (Rakocevic et al 2019;Paten et al 2017;Martiniano et al 2019;Marschall et al 2018;Church et al 2015). Alternatively, it can be a selection of reference genomes, with mapping to all or a subset undertaken, as has been done with M. chimaera and other pathogens (van Ingen et al 2017;Garimella et al 2019). Other authors have also reported that because of the genetic variability between strains, using a single strain genome as reference genome lacks accuracy (Ballouz, Dobin, and Gillis 2019;Sherman et al 2019;Li et al 2010).…”