Search

The Puma lineage within the family Felidae consists of 3 species that last shared a common ancestor around 4.9 million years ago. Whole-genome sequences of 2 species from the lineage were previously reported: the cheetah (Acinonyx jubatus) and the mountain lion (Puma concolor). The present report describes a whole-genome assembly of the remaining species, the jaguarundi (Puma yagouaroundi). We sequenced the genome of a male jaguarundi with 10X Genomics linked reads and assembled the whole-genome sequence. The assembled genome contains a series of scaffolds that reach the length of chromosome arms and is similar in scaffold contiguity to the genome assemblies of cheetah and puma, with a contig N50 = 100.2 kbp and a scaffold N50 = 49.27 Mbp. We assessed the assembled sequence of the jaguarundi genome using BUSCO, aligned reads of the sequenced individual and another published female jaguarundi to the assembled genome, annotated protein-coding genes, repeats, genomic variants and their effects with respect to the protein-coding genes, and analyzed differences of the 2 jaguarundis from the reference mitochondrial genome. The jaguarundi genome assembly and its annotation were compared in quality, variants, and features to the previously reported genome assemblies of puma and cheetah. Computational analyzes used in the study were implemented in transparent and reproducible way to allow their further reuse and modification. [ABSTRACT FROM AUTHOR]

Background The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Results Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history. [ABSTRACT FROM AUTHOR]