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

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.

Machine learning and deep learning applications in microbiome research.

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data - being compositional, sparse, and high-dimensional - necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

Diploidy within a Haploid Genus of Entomopathogenic Fungi.

Fungi in the genus Metarhizium are soil-borne plant-root endophytes and rhizosphere colonizers, but also potent insect pathogens with highly variable host ranges. These ascomycete fungi are predominantly asexually reproducing and ancestrally haploid, but two independent origins of persistent diploidy within the Coleoptera-infecting Metarhizium majus species complex are known and has been attributed to incomplete chromosomal segregation following meiosis during the sexual cycle. There is also evidence for infrequent sexual cycles in the locust-specific pathogenic fungus Metarhizium acridum (Hypocreales: Clavicipitaceae), which is an important entomopathogenic biocontrol agent used for the control of grasshoppers in agricultural systems as an alternative to chemical control. Here, we show that the genome of the M. acridum isolate ARSEF 324, which is formulated and commercially utilized is functionally diploid. We used single-molecule real-time sequencing technology to complete a high-quality assembly of ARSEF 324. K-mer frequencies, intragenomic collinearity between contigs and single nucleotide variant read depths across the genome revealed the first incidence of diploidy described within the species M. acridum. The haploid assembly of 44.7 Mb consisted of 20.8% repetitive elements, which is the highest proportion described of any Metarhizium species. The long-read diploid genome assembly sheds light on past research on this strain, such as unusual high UVB tolerance. The data presented here could fuel future investigation into the fitness landscape of fungi with infrequent sexual reproduction and aberrant ploidy levels, not least in the context of biocontrol agents.