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Background: As the number of sequenced genomes rapidly increases, chromosome assembly is becoming an even more crucial step of any genome study. Since de novo chromosome assemblies are confounded by repeat-mediated artifacts, reference-assisted assemblies that use comparative inference have become widely used, prompting the development of several reference-assisted assembly programs for prokaryotic and eukaryotic genomes. Findings: We developed Chromosomer - a reference-based genome arrangement tool, which rapidly builds chromosomes from genome contigs or scaffolds using their alignments to a reference genome of a closely related species. Chromosomer does not require mate-pair libraries and it offers a number of auxiliary tools that implement common operations accompanying the genome assembly process. Conclusions: Despite implementing a straightforward alignment-based approach, Chromosomer is a useful tool for genomic analysis of species without chromosome maps. Putative chromosome assemblies by Chromosomer can be used in comparative genomic analysis, genomic variation assessment, potential linkage group inference and other kinds of analysis involving contig or scaffold mapping to a high-quality assembly. [ABSTRACT FROM AUTHOR]

Background Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. Results Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets. Conclusions We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species. [ABSTRACT FROM AUTHOR]

Solenodons are insectivores that live in Hispaniola and Cuba. They form an isolated branch in the tree of placental mammals that are highly divergent from other eulipothyplan insectivores The history, unique biology, and adaptations of these enigmatic venomous species could be illuminated by the availability of genome data. However, a whole genome assembly for solenodons has not been previously performed, partially due to the difficulty in obtaining samples from the field. Island isolation and reduced numbers have likely resulted in high homozygosity within the Hispaniolan solenodon (Solenodon paradoxus). Thus, we tested the performance of several assembly strategies on the genome of this genetically impoverished species. The string graph–based assembly strategy seemed a better choice compared to the conventional de Bruijn graph approach due to the high levels of homozygosity, which is often a hallmark of endemic or endangered species. A consensus reference genome was assembled from sequences of 5 individuals from the southern subspecies (S. p. woodi). In addition, we obtained an additional sequence from 1 sample of the northern subspecies (S. p. paradoxus). The resulting genome assemblies were compared to each other and annotated for genes, with an emphasis on venom genes, repeats, variable microsatellite loci, and other genomic variants. Phylogenetic positioning and selection signatures were inferred based on 4,416 single-copy orthologs from 10 other mammals. We estimated that solenodons diverged from other extant mammals 73.6 million years ago. Patterns of single-nucleotide polymorphism variation allowed us to infer population demography, which supported a subspecies split within the Hispaniolan solenodon at least 300 thousand years ago. [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]