The Heteroduplex mobility assay (HMA) has proven to be a robust tool for the detection of genetic variation. Here, we describe a simple and rapid application of the HMA by microfluidic capillary electrophoresis, for phylogenetics and population genetic analyses (pgHMA). We show how commonly applied techniques in phylogenetics and population genetics have equivalents with pgHMA: phylogenetic reconstruction with bootstrapping, skyline plots, and mismatch distribution analysis. We assess the performance and accuracy of pgHMA by comparing the results obtained against those obtained using standard methods of analyses applied to sequencing data. The resulting comparisons demonstrate that: (1) there is a significant linear relationship (R = 0.992) between heteroduplex mobility and genetic distance; (2) phylogenetic trees obtained by HMA and nucleotide sequences present nearly identical topologies; (3) clades with high pgHMA parametric bootstrap support also have high bootstrap support on nucleotide phylogenies; (4) skyline plots estimated from the UPGMA trees of HMA and Bayesian trees of nucleotide data reveal similar trends, especially for the median trend estimate of effective population size; and (5) optimized mismatch distributions of HMA are closely fitted to the mismatch distributions of nucleotide sequences. In summary, pgHMA is an easily-applied method for approximating phylogenetic diversity and population trends. KEYWORDS: bootstrap, heteroduplex mobility assay, mismatch distribution, phylogenetics, skyline plot

Functional annotation aims to assess the biochemical and biological functions of sets of genomic or transcriptomic sequences yielded after next-generation sequencing experiments. One common way to perform functional annotation of a set of sequences obtained from a next-generation sequencing experiment, is by searching for homologous sequences and accessing to the related functional information deposited in genomic databases. Functional annotation is especially challenging in de novo assemblies of transcriptomes of non-model organisms, like many plant species. In such cases, existing commercial and open access general purpose applications may not offer complete and accurate results. We present TOA (Taxonomy-oriented annotation), a user-friendly open-access application designed to establish functional annotation pipelines geared towards non-model plant species. TOA performs homology searches against proteins stored in the PLAZA platform databases, NCBI RefSeq Plant, Nucleotide Database and Non-Redundant Protein Sequence Database, and retrieves functional information for several gene ontology systems. The software performance was validated by comparing the runtimes, total number of annotated sequences and accuracy of the functional information obtained for several plant benchmark datasets with TOA and other open-access functional annotation solutions. TOA outperformed the other software in terms of number of annotated sequences and accuracy of the annotation, and constitutes a good alternative to improve functional annotation in plants. TOA is especially recommended for gymnosperms or for low quality sequence datasets of non-model plants.

Microbial diversity and community function are related, and can be highly specialized in different gut regions. The cloacal microbiome of Sceloporus virgatus provides antifungal protection to eggshells during oviposition – a specialized function that suggests a specialized microbial composition. Here, we describe the S. virgatus cloacal microbiome from tissue and swab samples, and compare it to tissue samples from the gastrointestinal (GI) tract and oviduct, adding to the growing body of evidence of microbiome localization in reptiles. We further assessed whether common methods of microbial sampling – cloacal swabs and feces – provide accurate representations of these microbial communities and whether feces might “seed” the cloacal microbiome or impact the accuracy of cloacal swab sampling. We found that different regions of the gut had unique microbial community structures. The cloacal community, in particular, showed extreme specialization averaging 99% Proteobacteria (Phylum) and 83% Enterobacteriacaea (Family). Cloacal swabs recovered communities similar to that of lower intestine and cloacal tissues, but fecal samples had much higher diversity and a distinct composition (62% Firmicutes and 39% Lachnospiraceae) relative to all gut regions. Finally, we found that feces and cloacal swabs recover different communities, but cloacal swabs may be contaminated with fecal matter if taken immediately after defecation. These results serve as a caution against the assumption that fecal samples provide an accurate representation of the gut, and that although cloacal swabs can reflect a portion of the lower GI tract microbiome, they may also result in a mixed community of gut and fecal microbes.

Polyploids are cells or organisms with a genome consisting of more than two sets of homologous chromosomes. Polyploid plants have important traits that facilitate speciation and are thus often model systems for evolutionary, molecular ecology and agricultural studies. However, due to their unusual mode of inheritance and dou-ble-reduction, diploid models of population genetic analysis cannot properly be ap-plied to polyploids. To overcome this problem, we developed a software package en-titled VCFPOP to perform a variety of population genetic analyses for autopolyploids, such as parentage analysis, analysis of molecular variance, principal coordinates analysis, hierarchical clustering analysis and Bayesian clustering. We make this soft-ware freely available, downloadable from http://github.com/huangkang1987/vcfpop.

Helminth diseases have long been a threat to the health of humans and animals. Roundworms are important organisms for studying parasitic mechanisms, disease transmission and prevention. The study of parasites in the giant panda is of importance for understanding how roundworms adapt to the host. Here, we report a high-quality chromosome-scale genome of Baylisascaris schroederi with a genome size of 253.60 Mb and 19,262 predicted protein-coding genes. We found that gene families related to epidermal chitin synthesis and environmental information processes in the roundworm genome have expanded significantly. Furthermore, we demonstrated unique genes involved in essential amino acid metabolism in the B. schroederi genome, inferred to be essential for the adaptation to the giant panda-specific diet. In addition, under different deworming pressures, we found that four resistance-related genes (glc-1, nrf-6, bre-4 and ced-7) were under strong positive selection in a captive population. Finally, 23 known drug targets and 47 potential drug target proteins (essential homologues linked to lethal phenotypes) were identified. The genome provides a unique reference for inferring the early evolution of roundworms and their adaptation to the host. Population genetic analysis and drug sensitivity prediction provide insights revealing the impact of deworming history on population genetic structure of importance for disease prevention.

Chironomidae, so-called non-biting midges, are considered key bioindicators of aquatic ecosystem variability. Data derived from morphologically identifying their chitinous remains in sediments documents chironomid larvae assemblages, which are studied to reconstruct ecosystem changes over time. Recent developments in sedimentary DNA (sedDNA) research have demonstrated that molecular techniques are suitable for determining past and present occurrences of organisms. Nevertheless, sedDNA records documenting alterations in chironomid assemblages remain largely unexplored. To close this gap, we examined the applicability of sedDNA metabarcoding to identify Chironomidae assemblages in lake sediments by sampling and processing three 21 – 35 cm long sediment cores from Lake Sempach in Switzerland. With a focus on developing analytical approaches, we compared an invertebrate-universal (FWH) and a newly designed Chironomidae-specific metabarcoding primer set (CH) to assess their performance in detecting Chironomidae DNA. We isolated and identified chitinous larval remains and compared the morphotype assemblages with the data derived from sedDNA metabarcoding. Results showed a good overall agreement of the morphotype assemblage-specific clustering among the chitinous remains and the metabarcoding datasets. Both methods indicated higher chironomid assemblage similarity between the two littoral cores in contrast to the deep lake core. Moreover, we experienced a pronounced primer bias effect resulting in more Chironomidae detections with the CH primer combination compared to the FWH combination. Overall, we conclude that sedDNA metabarcoding can supplement traditional remain identifications and potentially provide independent reconstructions of past chironomid assemblage changes. Furthermore, it has the potential of more efficient workflows, better sample standardisation and species-level resolution datasets.

Despite recent advances in high-throughput DNA sequencing technologies, a lack of locally relevant DNA reference databases may limit the potential for DNA-based monitoring of biodiversity for conservation and biosecurity applications. Museums and national collections represent a compelling source of authoritatively identified genetic material for DNA database development yet obtaining DNA barcodes from long-stored specimens may be difficult due to sample degradation. We demonstrate a sensitive and efficient laboratory and bioinformatic process for generating DNA barcodes from hundreds of invertebrate specimens simultaneously via the Illumina MiSeq system. Using this process, we recovered full-length (334) or partial (105) COI barcodes from 439 of 450 (98 %) national collection-held invertebrate specimens. This included full-length barcodes from 146 specimens which produced low-yield DNA and no visible PCR bands, and which produced as little as a single sequence per specimen, demonstrating high sensitivity of the process. In many cases, the identity of the most abundant sequences per specimen were not the correct barcodes, necessitating the development of a taxonomy-informed process for identifying correct sequences among the sequencing output. The recovery of only partial barcodes for some taxa indicates a need to refine certain PCR primers. Nonetheless, our approach represents a highly sensitive, accurate, and efficient method for targeted reference database generation, providing a foundation for DNA-based assessments and monitoring of biodiversity.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop ultrafast and memory-efficient software that performs over a dozen common phylogenomic manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line (CLI) and graphical user interface (GUI) applications, and as a programming language library for Rust, R, and Python, with possible support of other languages. The CLI and library versions, SEGUL, run native on Windows, Linux, and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS and Android operating systems. SEGUL offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL’s efficiency reduces economic barriers by enabling analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait in an outbreeding species across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

With the rapid growth of the number of sequenced ancient genomes, there has been increasing interest in using this new information to study past and present adaptation. Such an additional temporal component has the promise of providing improved power for the estimation of natural selection. Over the last decade, statistical approaches for detection and quantification of natural selection from ancient DNA (aDNA) data have been developed. However, most of the existing methods do not allow us to estimate the timing of natural selection along with its strength, which is key to understanding the evolution and persistence of organismal diversity. Additionally, most methods ignore the fact that natural populations are almost always structured, which can result in overestimation of the effect of natural selection. To address these issues, we propose a novel Bayesian framework for the inference of natural selection and gene migration from aDNA data with Markov chain Monte Carlo techniques, co-estimating both timing and strength of natural selection and gene migration. Such an advance enables us to infer drivers of natural selection and gene migration by correlating genetic evolution with potential causes such as the changes in the ecological context in which an organism has evolved. The performance of our procedure is evaluated through extensive simulations, with its utility shown with an application to ancient chicken samples.

Epigenetic processes have taken center stage for the investigation of many biological processes and epigenetic modifications have shown to influence phenotype, morphology and behavioral traits such as stress resistance by affecting gene regulation and expression without altering the underlying genomic sequence. The multiple molecular layers of epigenetics synergistically construct the cell type-specific gene regulatory networks. DNA methylation occurring on the 5’ carbon of cytosines in different genomic sequence contexts is the most studied epigenetic modification. DNA methylation has been shown to provide a molecular record of a large variety of environmental factors, which might be persistent through the entire lifetime of an organisms and even be passed onto the offspring. Animals might display altered phenotypes mediated by epigenetic modifications depending on the developmental stage or the environmental conditions as well as during evolution. Therefore, the analysis of DNA methylation patterns might allow deciphering previous exposures, explaining ecologically relevant phenotypic diversity and predicting evolutionary trajectories enabling accelerated adaption to changing environmental conditions. Despite the explanatory potential of DNA methylation. studies of DNA methylation are still scarce in the field of ecology. This might be at least partly due to the complexity of DNA methylation analysis and the interpretation of the acquired data. In the current issue of Molecular Ecology Resources, Laine and colleagues (2023) provide a detailed summary of guidelines and valuable recommendations for researchers in the field of ecology to avoid common pitfalls and perform interpretable genome-wide DNA methylation analyses.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the Major Histocompatibility Complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, using MHC class IIβ datasets of three-spined stickleback gDNA, cDNA, and “artificial” plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the same configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA genotyping and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA genotyping of MHC-I revealed novel variants and haplotype-based allelic segregation with a higher-than-previously-reported individual allelic diversity for MHC-I in sticklebacks. In conclusion, we here provide novel genotyping protocols for MHC-I and -II genes of the three-spined stickleback, but also evaluate the performance of popular NGS-genotyping pipelines and highlight the need for template-specific optimization for reliable multi-locus genotyping.

Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the expected neutral levels of genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island model and the two-dimensional stepping stone model under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence between demes can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, and migration rates between individual demes can be specified. We identify the different factors that can cause a deviation from the theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that Fst, a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. We also warn that predicting genetic diversity from coalescence times requires a much longer run time than needed for the estimation of Fst. Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the Last Glacial Maximum, using different degrees of spatio-temporal variation in deme size.

Managing endangered species in fragmented landscapes requires estimating dispersal rates between populations over contemporary timescales. Here we develop a new method for quantifying recent dispersal using genetic pedigree data for close and distant kin. Specifically, we describe an approach that infers missing shared ancestors between pairs of kin in habitat patches across a fragmented landscape. We then apply a stepping-stone model to assign unsampled individuals in the pedigree to probable locations based on minimizing the number of movements required to produce the observed locations in sampled kin pairs. Finally, we use all pairs of reconstructed parent-offspring sets to estimate dispersal rates between habitat patches under a Bayesian model. Our approach measures connectivity over the timescale represented by the small number of generations contained within the pedigree and so is appropriate for estimating the impacts of recent habitat changes due to human activity. We used our method to estimate recent movement between newly discovered populations of threatened Eastern Massasauga Rattlesnakes (Sistrurus catenatus) using data from 2996 RAD-based genetic loci. Our pedigree analyses found no evidence for contemporary connectivity between five genetic groups, but, as validation of our approach, showed high dispersal rates between sample sites within a single genetic cluster. We conclude that these five genetic clusters of Eastern Massasauga Rattlesnakes have small numbers of resident snakes and are demographically isolated conservation units. More broadly, our methodology can be widely applied to determine contemporary connectivity rates, independent of bias from shared genetic similarity due to ancestry that impacts other approaches.

We used long read sequencing data generated from Knightia excelsaI R.Br, a nectar producing Proteaceae tree endemic to Aotearoa New Zealand, to explore how sequencing data type, volume and workflows can impact final assembly accuracy and chromosome construction. Establishing a high-quality genome for this species has specific cultural importance to Māori, the indigenous people, as well as commercial importance to honey producers in Aotearoa New Zealand. Assemblies were produced by five long read assemblers using data subsampled based on read lengths, two polishing strategies, and two Hi-C mapping methods. Our results from subsampling the data by read length showed that each assembler tested performed differently depending on the coverage and the read length of the data. Assemblies that used longer read lengths (>30 kb) and lower coverage were the most contiguous, kmer and gene complete. The final genome assembly was constructed into pseudo-chromosomes using all available data assembled with FLYE, polished using Racon/Medaka/Pilon combined, scaffolded using SALSA2 and AllHiC, curated using Juicebox, and validated by synteny with Macadamia. We highlighted the importance of developing assembly workflows based on the volume and type of sequencing data and establishing a set of robust quality metrics for generating high quality assemblies. Scaffolding analyses highlighted that problems found in the initial assemblies could not be resolved accurately by utilizing Hi-C data and that scaffolded assemblies were more accurate when the underlying contig assembly was of higher accuracy. These findings provide insight into what is required for future high-quality de-novo assemblies of non-model organisms.

Mesozooplankton is a very diverse group of small animals ranging in size from 0.2 to 20 mm not able to swim against ocean currents. It is a key component of pelagic ecosystems through its roles in the trophic networks and the biological carbon pump. Traditionally studied through microscopes, recent methods have been however developed to rapidly acquire large amounts of data (morphological, molecular) at the individual scale, making it possible to study mesozooplankton using a trait-based approach. Here, combining quantitative imaging with metabarcoding time-series data obtained in the Sargasso Sea at the Bermuda Atlantic Time-series Study (BATS) site, we showed that organisms’ transparency might be an important trait to also consider regarding mesozooplankton impact on carbon export, contrary to the common assumption that just size is the master trait directing most mesozooplankton-linked processes. Three distinct communities were defined based on taxonomic composition, and succeeded one another throughout the study period, with changing levels of transparency among the community. A co-occurrences’ network was built from metabarcoding data revealing six groups of taxa. These were related to changes in the functioning of the ecosystem and/or in the community’s morphology. The importance of Diel Vertical Migration at BATS was confirmed by the existence of a group made of taxa known to be strong migrators. Finally, we assessed if metabarcoding can provide a quantitative approach to biomass and/or abundance of certain taxa. Knowing more about mesozooplankton diversity and its impact on ecosystem functioning would allow to better represent them in biogeochemical models.

Understanding the genetic changes associated with the evolution of biological diversity is of fundamental interest to molecular ecologists. The assessment of genetic variation at hundreds or thousands of unlinked genetic loci forms a sound basis to address questions ranging from micro- to macro-evolutionary timescales, and is now possible thanks to advances in sequencing technology. Major difficulties are associated with i) the lack of genomic resources for many taxa, especially from tropical biodiversity hotspots, ii) scaling the numbers of individuals analyzed and loci sequenced, and iii) building tools for reproducible bioinformatic analyses of such datasets. To address these challenges, we developed a set of target capture probes for phylogenomic studies of the highly diverse, pantropically distributed and economically significant rosewoods (Dalbergia spp.), explored the performance of an overlapping probe set for target capture across the legume family (Fabaceae), and built a general-purpose bioinformatics pipeline. Phylogenomic analyses of Dalbergia species from Madagascar yielded highly resolved and well supported hypotheses of evolutionary relationships. Population genomic analyses identified differences between closely related species and revealed the existence of a potentially new species, suggesting that the diversity of Malagasy Dalbergia species has been underestimated. Analyses at the family level corroborated previous findings by the recovery of monophyletic subfamilies and many well-known clades, as well as high levels of gene tree discordance, especially near the root of the family. The new genomic and bioinformatics resources will hopefully advance systematics and ecological genetics research in legumes, and promote conservation of the highly diverse and endangered Dalbergia rosewoods.

Ark shells are commercially important clam species that inhabit in muddy sediments of shallow coasts in East Asia. For a long time, the lack of genome resources has hindered scientific research of ark shells. Here, we reported a high-quality chromosome-level genome assembly of Scapharca kagoshimensis, with an aim to unravel the molecular basis of heme biosynthesis, and develop genomic resources for genetic breeding and population genetics in ark shells. Nineteen scaffolds corresponding to 19 chromosomes were constructed from 938 contigs (contig N50=2.01 Mb) to produce a final high-quality assembly with a total length of 1.11 Gb and scaffold N50 around 60.64 Mb. The genome assembly represents 93.4% completeness via matching 303 eukaryota core conserved genes. A total of 24,908 protein-coding genes were predicted and 24,551 genes (98.56%) of which were functionally annotated. The enrichment analyses suggested that genes in heme biosynthesis pathways were expanded and positive selection of the hemoglobin genes was also found in the genome of S. kagoshimensis, which gives important insights into the molecular mechanisms and evolution of the heme biosynthesis in mollusca. The valuable genome assembly of S. kagoshimensis would provide a solid foundation for investigating the molecular mechanisms that underlie the diverse biological functions and evolutionary adaptations of S. kagoshimensis.