HMMploidy: inference of ploidy levels from short-read sequencing data

Soraggi, Samuele; Rhodes, Johanna; Altinkaya, Isin; Tarrant, Oliver; Balloux, François; Fisher, Matthew C.; Fumagalli, Matteo

doi:10.1101/2021.06.29.450340

Cited by 5 publications

(4 citation statements)

References 69 publications

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“…Nucleotide, genotype and allele frequency likelihoods ngsJulia provides utilities to calculate nucleotide and genotype likelihoods, i.e. the probability of observed sequencing data given a specific nucleotide or genotype, 21 for an arbitrary ploidy level, as in Soraggi et al 22 We now describe how such quantities are calculated in ngsJulia.…”

Section: Methodsmentioning

confidence: 99%

“…Following the notation in Soraggi et al, 22 for one sample and one site, we let O be the observed NGS data, Y the ploidy, and G the genotype. Therefore, G has values in 0, 1,…, Y f g , i.e.…”

Section: Methodsmentioning

confidence: 99%

“…In the simplest form, genotype likelihoods can be calculated by considering individual base qualities as probabilities of observing an incorrect nucleotide. 21 We adopt the calculation of genotype likelihoods for an arbitrary ploidy level P OjG, Y ð Þas proposed in Soraggi et al 22 From the genotype likelihoods with P OjG,Y ¼ 1 ð Þ , the two most likely alleles are identified by sorting P OjG, Y ¼ 1 ð Þvalues after pooling all sequencing reads together across all samples. This operation will restrict the range of possible genotypes to biallelic variation only.…”

Section: Methodsmentioning

confidence: 99%

“…ngsPloidy: analysis of sequencing data from polyploid genomes We further utilised ngsJulia to implement an additional program, ngsPloidy, for the estimation of ploidy from unknown genotypes. The method implemented is similar to the one proposed by Soraggi et al 22 with some notable differences on the calculation of genotype probabilities (see Methods Following Equation 2, the genotype probabilities for each tested ploidy are pre-calculated using a script provided in ngsPloidy. This script takes as input the value of parameter K (the shape of the expected SFS), the effective population size, and the probability that the major allele is the ancestral allele.…”

Section: Estimation Of Allele Frequenciesmentioning

confidence: 99%

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ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

et al. 2022

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A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicabil- ity of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Estimation Of Allele Frequenciesmentioning

confidence: 99%

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ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

et al. 2022

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Fast and accurate estimation of multidimensional site frequency spectra from low-coverage high-throughput sequencing data

et al. 2022

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Background The site frequency spectrum summarizes the distribution of allele frequencies throughout the genome, and it is widely used as a summary statistic to infer demographic parameters and to detect signals of natural selection. The use of high-throughput low-coverage DNA sequencing data can lead to biased estimates of the site frequency spectrum due to high levels of uncertainty in genotyping. Results Here we design and implement a method to efficiently and accurately estimate the multidimensional joint site frequency spectrum for large numbers of haploid or diploid individuals across an arbitrary number of populations, using low-coverage sequencing data. The method maximizes a likelihood function that represents the probability of the sequencing data observed given a multidimensional site frequency spectrum using genotype likelihoods. Notably, it uses an advanced binning heuristic paired with an accelerated expectation-maximization algorithm for a fast and memory-efficient computation, and can generate both unfolded and folded spectra and bootstrapped replicates for haploid and diploid genomes. On the basis of extensive simulations, we show that the new method requires remarkably less storage and is faster than previous implementations whilst retaining the same accuracy. When applied to low-coverage sequencing data from the fungal pathogen Neonectria neomacrospora, results recapitulate the patterns of population differentiation generated using the original high-coverage data. Conclusion The new implementation allows for accurate estimation of population genetic parameters from arbitrarily large, low-coverage datasets, thus facilitating cost-effective sequencing experiments in model and non-model organisms.

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Hybridisation and chloroplast capture between ancient Themeda triandra ecotypes in Australia

Dunning

Oloffson²,

Papadopulos³

et al. 2021

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Ecotypes are distinct populations within a species which are adapted to specific environmental conditions. Understanding how these ecotypes become established, and how they interact when reunited, is fundamental to elucidating how ecological adaptations are maintained. This study focuses on Themeda triandra, a dominant grassland species across Asia, Africa and Australia. It is the most widespread plant in Australia, where it has distinct ecotypes that are usually restricted to either wetter and cooler coastal regions or the drier and hotter interior. We use whole genome sequencing for over 80 Themeda accessions to reconstruct the evolutionary history of T. triandra and related taxa. A chloroplast phylogeny confirms that Australia was colonised by T. triandra twice, with the division between ecotypes predating their arrival in Australia. The nuclear genome provides evidence of gene-flow among the ecotypes, largely restricted to two geographic areas. In northern Queensland there appears to be a hybrid zone with admixed nuclear genomes and shared plastid haplotypes. Conversely, in the cracking claypans of Western Australia, there is cytonuclear discordance with individuals possessing the coastal plastid and interior clade nuclear genomes. This chloroplast capture is potentially a result of adaptive introgression, with selection detected in the rpoC2 gene which is associated with water use efficiency. A stable hybrid zone in the east, and the displacement of one ecotype in the west, highlights the unpredictable nature of hybrid zones, with repeated contacts between the same ecotypes producing different outcomes.

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HMMploidy: inference of ploidy levels from short-read sequencing data

Cited by 5 publications

References 69 publications

ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

Fast and accurate estimation of multidimensional site frequency spectra from low-coverage high-throughput sequencing data

Hybridisation and chloroplast capture between ancient Themeda triandra ecotypes in Australia

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