Genotyping-by-sequencing (GBS) is a widely used and cost-effective technique for obtaining large numbers of genetic markers from populations by sequencing regions adjacent to restriction cut sites. Although a standard reference-based pipeline can be followed to analyse GBS reads, a reference genome is still not available for a large number of species. Hence, reference-free approaches are required to generate the genetic variability information that can be obtained from a GBS experiment.Unfortunately, available tools to perform de novo analysis of GBS reads face issues of usability, accuracy and performance. Furthermore, few available tools are suitable for analysing data sets from polyploid species. In this manuscript, we describe a novel algorithm to perform reference-free variant detection and genotyping from GBS reads. Nonexact searches on a dynamic hash table of consensus sequences allow for efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of-theart variant detector already implemented in this tool. We performed benchmark experiments with three different empirical data sets of plants and animals with different population structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for many research groups conducting population genetic studies in a wide variety of species.

Building de novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. We present new algorithms for assembling long DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mer distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from haploid and diploid samples of different species. Our algorithms showed competitive accuracy and computational efficiency, compared with other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.

Genotype-by-sequencing (GBS) is a widely used cost-effective technique to obtain large numbers of genetic markers from populations. Although a standard reference-based pipeline can be followed to analyze these reads, a reference genome is still not available for a large number of species. Hence, several research groups require reference-free approaches to generate the genetic variability information that can be obtained from a GBS experiment. Unfortunately, tools to perform de-novo analysis of GBS reads are scarce and some of the existing solutions are difficult to operate under different settings generated by the existing GBS protocols. In this manuscript we describe a novel algorithm to perform reference-free variants detection and genotyping from GBS reads. Non-exact searches on a dynamic hash table of consensus sequences allow to perform efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of- the-art variants detector already implemented in this tool. We performed benchmark experiments with three different real populations of plants and animals with different structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for several research groups conducting population genetic studies in a wide variety of species.

PremiseTransposable elements (TEs) make up more than half of the genomes of complex plant species and can modulate the expression of neighboring genes, producing significant variability of agronomically relevant traits. The availability of long‐read sequencing technologies allows the building of genome assemblies for plant species with large and complex genomes. Unfortunately, TE annotation currently represents a bottleneck in the annotation of genome assemblies.Methods and ResultsWe present a new functionality of the Next‐Generation Sequencing Experience Platform (NGSEP) to perform efficient homology‐based TE annotation. Sequences in a reference library are treated as long reads and mapped to an input genome assembly. A hierarchical annotation is then assigned by homology using the annotation of the reference library. We tested the performance of our algorithm on genome assemblies of different plant species, including Arabidopsis thaliana, Oryza sativa, Coffea humblotiana, and Triticum aestivum (bread wheat). Our algorithm outperforms traditional homology‐based annotation tools in speed by a factor of three to >20, reducing the annotation time of the T. aestivum genome from months to hours, and recovering up to 80% of TEs annotated with RepeatMasker with a precision of up to 0.95.ConclusionsNGSEP allows rapid analysis of TEs, especially in very large and TE‐rich plant genomes.

The domestication process in Lima bean (Phaseolus lunatus L.) involves at least two independent events, within the Mesoamerican and Andean gene pools. Both processes produced similar phenotypic changes in landraces, making Lima bean an excellent model to understand convergent evolution. Despite recent research efforts, the mechanisms of adaptation followed by Mesoamerican and Andean landraces are largely unknown. The genes related to these adaptations can be selected by identification of selective sweeps within gene pools. Most of the previous genetic analyses in Lima bean have relied on Single Nucleotide Polymorphism (SNP) loci and have ignored transposable elements (TEs) which are a major source of variation in plant genomes. The current availability of high-throughput sequencing technologies enables the collection of whole-genome resequencing (WGS) data to approach intraspecies population dynamics of TEs. The present research collected WGS data from 60 wild and domesticated Lima bean accessions to generate the most complete characterization developed to date of transposable elements and SNP loci in the Lima bean genome. We generated an updated annotation of 223,780 transposable elements in the Lima bean genome. Furthermore, we identified genes and variable TEs affected by selective sweeps. Combining three different approaches, selective sweeps were predicted to generate a set of domestication candidate genes. A small percentage of genes under selection (1.6%) were shared among gene pools, suggesting that domestication followed different genetic avenues in both gene pools. Up to 25% of the genes with previously reported selective sweeps in common bean were also detected in Lima bean. We also built a catalog of 39,459 TEs with presence-absence variation (PAV). The fact that 75% of these TEs were located close to genes shows their potential to affect gene functions in Lima bean. The genetic structure inferred from variable TEs was consistent with that obtained from SNP markers, suggesting that TE dynamics can be related to the demographic history of wild and domesticated Lima bean and its adaptive processes, in particular selection processes during domestication.