Long-read sequencing technology is a powerful approach with application in various genetic and genomic researches. Herein, we developed the pipeline for long amplicon high-fidelity (HiFi) sequencing and then applied it for sequencing mitochondrial DNA (mtDNA) genomes from pools of 79 Tibetan Mastiffs. We amplified the mtDNA genome with long-range PCR using two pairs of primers. Two rounds of circular consensus sequencing (CCS) were conducted and their accuracy was evaluated. The results indicate that the second round of CCS can improve the accuracy of HiFi reads. In addition, the analysis of 79 high-quality mtDNA genomes shows the Tibetan Mastiffs from outside of the Tibetan Plateau experienced hybridization with other dogs. The High Quality Reads Generator (HQGR) software is provided to facilitate data analyses, which is publicly accessible on GitHub (https://github.com/Caizf-script/HQGR). Our long amplicon HiFi sequencing pipeline can also be applied in various target enrichment strategies for small genomes and candidate genes.

Captive rearing in salmon hatcheries can have considerable impacts on both fish phenotype and fitness within a single generation, even in the absence of genetic change. Evidence for hatchery-induced changes in DNA methylation is becoming abundant, though questions remain on the sex-specificity of these effects, their persistence until spawning, and potential for transmission to future generations. Here we performed whole genome methylation sequencing of fin tissue for 16 hatchery and 16 wild Atlantic salmon (Salmo salar) returning to spawn in the Rimouski River, Québec. We identified two cohorts of hatchery-reared salmon through methylation analysis, one of which was epigenetically similar to wild fish, suggesting that supplementation efforts may be able to minimize the epigenetic effects of hatchery rearing. We found considerable sex-specific effects of hatchery rearing, with few genomic regions being affected in both males and females. We also analysed the methylome of 32 F1 offspring from four groups (pure wild, pure hatchery origin, and reciprocal hybrids). We found that few epigenetic changes due to parental hatchery rearing persisted in the F1 offspring though the patterns of inheritance appear to be complex, involving nonadditive effects. Our results suggest that the epigenetic effects of hatchery rearing can be minimal in F0. There may also be minimal epigenetic inheritance and rapid loss of epigenetic changes associated with hatchery rearing. However, due to sex-specificity and nonadditive patterns of inheritance, methylation changes due to captive rearing are rather complex and the field would benefit from further research on minimizing the epigenetic effects of captive rearing in conservation efforts.

Inserts of DNA from extranuclear sources, such as organelles and microbes, are common in eukaryote nuclear genomes. However, sequence similarity between the nuclear and extranuclear DNA, and a history of multiple insertions, make the assembly of these regions challenging. Consequently, the number, sequence, and location of these vagrant DNAs cannot be reliably inferred from the genome assemblies of most organisms. We introduce two statistical methods to estimate the abundance of nuclear inserts even in the absence of a nuclear genome assembly. The first (intercept method) only requires low-coverage (<1x) sequencing data, as commonly generated for population studies of organellar and ribosomal DNAs. The second method additionally requires that a subset of the individuals carry extra-nuclear DNA with diverged genotypes. We validated our intercept method using simulations and by re-estimating the frequency of human NUMTs (nuclear mitochondrial inserts). We then applied it to the grasshopper Podisma pedestris, exceptional for both its large genome size and reports of numerous NUMT inserts, estimating that NUMTs make up 0.056% of the nuclear genome, equivalent to >500 times the mitochondrial genome size. We also re-analysed a museomics dataset of the parrot Psephotellus varius, obtaining an estimate of only 0.0043%, in line with reports from other species of bird. Our study demonstrates the utility of low-coverage high-throughput sequencing data for the quantification of nuclear vagrant DNAs. Beyond quantifying organellar inserts, these methods could also be used on endosymbiont-derived sequences. We provide an R implementation of our methods called "vagrantDNA" and code to simulate test datasets.

Almost every animal trait is strongly associated with parasitic infection or the potential exposure to parasites. Despite this importance, one of the greatest challenges that researchers still face is to accurately determine the status and severity of the endoparasitic infection without killing and dissecting the host. Thus, the precise detection of infection with minimal handling of the individual will improve experimental designs in live animal research. Here, we quantified extracellular DNA from two species of endoparasitic worm that grow within the host body cavity, hairworms (phylum Nematomorpha) and mermithids (phylum Nematoda), from the frass of their insect host, a cave wētā (Orthoptera: Rhaphidophoridae) and an earwig (Dermaptera: Forficulidae), respectively. Frass collection was done at two successive time periods, to test if parasitic growth correlated with relative DNA quantity in the frass. We developed and optimised two highly specific TaqMan assays, one for each parasite-specific DNA amplification. We were able to detect infection prevalence with 100% accuracy in individuals identified as infected through post-study dissections. An additional infection in earwigs was detected with the TaqMan assay alone, likely because some worms were either too small or degraded to observe during dissection. No difference in DNA quantity was detected between sampling periods, although future protocols could be refined to support such a trend. This study demonstrates that a non-invasive and minimally stressful method can be used to detect endoparasitic infection with greater accuracy than dissection alone, helping improve protocols for live animal studies.

Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published data sets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.

Accurate taxonomic identification is foundational for effective species monitoring and management. When visual identifications are infeasible or inaccurate, genetic approaches provide a reliable alternative. However, these approaches are sometimes less viable (e.g., need for near real-time results, remote locations, funding concerns, molecular inexperience). In these situations, CRISPR-based genetic tools can fill an unoccupied niche between real-time, inexpensive, but error-prone visual identification and more expensive or time-consuming, but accurate genetic identification for taxonomic units that are difficult or impossible to visually identify. Herein, we use genomic data to develop CRISPR-based SHERLOCK assays capable of rapidly (<1 h), accurately (94%-98% concordance between phenotypic and genotypic assignments), and sensitively (detects 1-10 DNA copies/reaction) distinguishing ESA-listed Chinook salmon runs (winter- and spring-run) from each other and from unlisted runs (fall- and late fall-run) in California's Central Valley. The assays can be field deployable with minimally invasive mucus swabbing negating the need for DNA extraction (decreasing costs and labour), minimal and inexpensive equipment needs, and minimal training to conduct following assay development. This study provides a powerful genetic approach for a species of conservation concern that benefits from near real-time management decision-making but also serves as a precedent for transforming how conservation scientists and managers view genetic identification going forward. Once developed, CRISPR-based tools can provide accurate, sensitive, and rapid results, potentially without the prohibitive need for expensive specialty equipment or extensive molecular training. Further adoption of this technology will have widespread value for the monitoring and protection of our natural resources.

Lifespan is a key attribute of a species' life cycle and varies extensively among major lineages of animals. In fish, lifespan varies by several orders of magnitude, with reported values ranging from less than 1 year to approximately 400 years. Lifespan information is particularly useful for species management, as it can be used to estimate invasion potential, extinction risk and sustainable harvest rates. Despite its utility, lifespan is unknown for most fish species. This is due to the difficulties associated with accurately identifying the oldest individual(s) of a given species, and/or deriving lifespan estimates that are representative for an entire species. Recently it has been shown that CpG density in gene promoter regions can be used to predict lifespan in mammals and other vertebrates, with variable accuracy across taxa. To improve accuracy of lifespan prediction in a non-mammalian vertebrate group, here we develop a fish-specific genomic lifespan predictor. Our new model includes more than eight times the number of fish species included in the previous vertebrate model (n = 442) and uses fish-specific gene promoters as reference sequences. The model predicts fish lifespan from genomic CpG density alone (measured as CpG observed/expected ratio), explaining 64% of the variance between known and predicted lifespans. The predictions are highly robust to variation in genome quality and are applicable to all classes of fish; a taxonomically diverse and speciose group. The results demonstrate the value of promoter CpG density as a universal predictor of fish lifespan that can applied where empirical data are unavailable, or impracticable to obtain.

DNA barcoding approaches have greatly increased our understanding of biodiversity on the planet, and metabarcoding is widely used for classifying members of the phylum Nematoda. However, loci typically utilized in metabarcoding studies are often unable to resolve closely related species or are unable to recover all taxa present in a sample due to inadequate PCR primer binding. Mitochondrial metagenomics (mtMG) is an alternative approach utilizing shotgun sequencing of total DNA to recover the mitochondrial genomes of all species present in samples. However, this approach requires a comprehensive reference database for identification and currently available mitochondrial sequences for nematodes is highly dominated by sequences from the order Rhabditida and excludes many clades entirely. Here we analyzed the efficacy of mtMG for the recovery of nematode taxa and the generation of mitochondrial genomes. We first developed a curated reference database of nematode mitochondrial sequences and expanded it with 40 newly sequenced taxa. We then tested the mito-metagenomics approach using a series of nematode mock communities consisting of morphologically identified nematode species representing various feeding traits, life stages, and phylogenetic relationships. We were able to identify all but two species through the de novo assembly of COX1 genes. We were also able to recover additional mitochondrial protein coding genes (PCGs) for 23 of the 24 detected species including a full array of 12 PCGs from 5 of the species. We conclude that mtMG offers a potential for the effective recovery of nematode biodiversity but remains limited by the breadth of the reference database.

Multiplexed amplicon sequencing offers a cost-effective and rapid solution for phylogenomic studies that include a large number of individuals. Selecting informative genetic markers is a critical initial step in designing such multiplexed amplicon panels, but screening various genomic data and selecting markers that are informative for the question at hand can be laborious. Here, we present a flexible and user-friendly tool, HiMAP2, for identifying, visualizing, and filtering phylogenetically informative loci from diverse genomic and transcriptomic resources. This bioinformatics pipeline includes orthology prediction, exon extraction, and filtering of aligned exon sequences according to user-defined specifications. Additionally, HiMAP2 facilitates exploration of the final filtered exons by incorporating phylogenetic inference of individual exon trees with RAxML-NG as well as the estimation of a species tree using ASTRAL. Finally, results of the marker selection can be visualized and refined with an interactive Bokeh application that can be used to generate publication-quality figures. Source code and user instructions for HiMAP2 are available at https://github.com/popphylotools/HiMAP_v2.

Clonorchis sinensis is a carcinogenic liver fluke that causes clonorchiasis-a neglected tropical disease (NTD) affecting ~35 million people worldwide. No vaccine is available, and chemotherapy relies on one anthelmintic, praziquantel. This parasite has a complex life history and is known to infect a range of species of intermediate (freshwater snails and fish) and definitive (piscivorous) hosts. Despite this biological complexity and the impact of this biocarcinogenic pathogen, there has been no previous study of molecular variation in this parasite on a genome-wide scale. Here, we conducted the first extensive nuclear genomic exploration of C. sinensis individuals (n = 152) representing five distinct populations from mainland China, and one from Far East Russia, and revealed marked genetic variation within this species between "northern" and "southern" geographical regions. The discovery of this variation indicates the existence of biologically distinct variants within C. sinensis, which may have distinct epidemiology, pathogenicity and/or chemotherapic responsiveness. The detection of high heterozygosity within C. sinensis specimens suggests that this parasite has developed mechanisms to readily adapt to changing environments and/or host species during its life history/evolution. From an applied perspective, the identification of invariable genes could assist in finding new intervention targets in this parasite, given the major clinical relevance of clonorchiasis. From a technical perspective, the genomic-informatic workflow established herein will be readily applicable to a wide range of other parasites that cause NTDs.

Several methods have been published to estimate per capita predation rates from molecular gut content analysis relying on intuitive understanding of predation, but none have been formally derived. We provide a theoretical framework for estimating predation rates to identify an accurate method and lay bare its assumptions. Per capita predation can be estimated by multiplying the prey decay rate and the prey quantity in the predators. This assumes that variation in per capita predation rate is approximately normally distributed, prey decay occurs exponentially, and predation is in steady state. We described several ways to estimate steady state predation, including using only qualitative presence-absence data to estimate the decay rate and in addition, we provided a method for estimating per capita predation rate when predation is not in steady state. We used previously published data on aphid consumption by a ladybird beetle in a feeding trial to calculate the predation rate and compare published methods with this theoretically derived method. The estimated predation rate (3.29 ± 0.27 aphids/h) using our derived method was not significantly different from the actual predation rate, 3.11 aphids/h. In contrast, previously published methods were less accurate, underestimating the predation rate (0.33 ± 0.02 to 1.66 ± 0.8 aphids/h) or overestimating it (3.64 ± 0.30 aphids/h). In summary, we provide methods to estimate predation rates even when variation in predation rates is not normally distributed and not in steady state and demonstrate that the prey decay rate, and not the prey detection period, is required.

Genotype environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the weighted-Z analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST . Performing GEA in analysis windows has the advantage that it takes advantage of the increased linkage disequilibrium expected surrounding sites subject to local adaptation. We analyze simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single-SNP based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes was sampled. Particularly troubling, we found that some GEA methods exhibit very high false positive rates. We apply the WZA to previously published data from lodgepole pine and identified candidate loci that were identified in the original study alongside numerous loci that were not found in the original study.