Molecular Ecology Resources最新文献

Large and highly repetitive genomes are common. However, research interests usually lie within the non-repetitive parts of the genome, as they are more likely functional, and can be used to answer questions related to adaptation, selection and evolutionary history. Exome capture is a cost-effective method for providing sequencing data from protein-coding parts of the genes. C0t-1 DNA blockers consist of repetitive DNA and are used in exome captures to prevent the hybridisation of repetitive DNA sequences to capture baits or bait-bound genomic DNA. Universal blockers target repetitive regions shared by many species, while species-specific c0t-1 DNA is prepared from the DNA of the studied species, thus perfectly matching the repetitive DNA contents of the species. So far, the use of species-specific c0t-1 DNA has been limited to a few model species. Here, we evaluated the performance of blocker treatments in exome captures of Pinus sylvestris, a widely distributed conifer species with a large (> 20 Gbp) and highly repetitive genome. We compared treatment with a commercial universal blocker to treatments with species-specific c0t-1 (30,000 and 60,000 ng). Species-specific c0t-1 captured more unique exons than the initial set of targets leading to increased SNP discovery and reduced sequencing of tandem repeats compared to the universal blocker. Based on our results, we recommend optimising exome captures using at least 60,000 ng of species-specific c0t-1 DNA. It is relatively easy and fast to prepare and can also be used with existing bait set designs.

Landscape genomic approaches for detecting genotype-environment associations (GEA), isolation by distance (IBD) and isolation by environment (IBE) have seen a dramatic increase in use, but there have been few thorough analyses of the influence of sampling strategy on their performance under realistic genomic and environmental conditions. We simulated 24,000 datasets across a range of scenarios with complex population dynamics and realistic landscape structure to evaluate the effects of the spatial distribution and number of samples on common landscape genomics methods. Our results show that common analyses are relatively robust to sampling scheme as long as sampling covers enough environmental and geographic space. We found that for detecting adaptive loci and estimating IBE, sampling schemes that were explicitly designed to increase coverage of available environmental space matched or outperformed sampling schemes that only considered geographic space. When sampling does not cover adequate geographic and environmental space, such as with transect-based sampling, we detected fewer adaptive loci and had higher error when estimating IBD and IBE. We found that IBD could be detected with as few as nine sampling sites, while large sample sizes (e.g., greater than 100 individuals) were crucial for detecting adaptive loci and IBE. We also demonstrate that, even with optimal sampling strategies, landscape genomic analyses are highly sensitive to landscape structure and migration-when spatial autocorrelation and migration are weak, common GEA methods fail to detect adaptive loci.

Despite substantial reductions in the cost of sequencing over the last decade, genetic panels remain relevant due to their cost-effectiveness and flexibility across a variety of sample types. In particular, single nucleotide polymorphism (SNP) panels are increasingly favoured for conservation applications. SNP panels are often used because of their adaptability, effectiveness with low-quality samples, and cost-efficiency for population monitoring and forensics. However, the selection of diagnostic SNPs for population assignment and individual identification can be challenging. The consequences of poor SNP selection are under-powered panels, inaccurate results, and monetary loss. Here, we develop a novel and user-friendly SNP selection pipeline (mPCRselect) that can be used to select SNPs for population assignment and/or individual identification. mPCRselect allows any researcher, who has sufficient SNP-level data, to design a successful and cost-effective SNP panel for a diploid species of conservation concern.

The dimensions of phylogenetic research have expanded to encompass the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge, a robust tool designed to seamlessly integrate the demands of both micro- and macroevolution, comprehensively utilising diverse phylogenomic signals, such as genes, SNPs, and structural variations, as well as mitochondrial and chloroplast genomes. PhyloForge's innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling the unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customisable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants and fungi, attests to its broad applicability in the field of phylogenetics. In summary, PhyloForge has significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life. PhyloForge codes could be found in GitHub (https://github.com/wangyayaya/PhyloForge/), and the program could be installed in Conda (https://anaconda.org/wangxiaobei/phyloforge).

Increased human activity and climate change have significantly impacted wild habitats and increased the number of endangered species. Exploring evolutionary history and predicting adaptive potential using genomic data will facilitate species conservation and biodiversity recovery. Here, we examined the genome evolution of a critically endangered tree Pellacalyx yunnanensis, a plant species with extremely small populations (PSESP) that is narrowly distributed in Xishuangbanna, China. The species has neared extinction due to economic exploitation in recent decades. We assembled a chromosome-level genome of 334 Mb, with the N50 length of 20.5 Mb. Using the genome, we discovered that P. yunnanensis has undergone several population size reductions, leading to excess deleterious mutations. The species may possess low adaptive potential due to reduced genetic diversity and the loss of stress-responsive genes. We estimate that P. yunnanensis is the basal species of its genus and diverged from its relatives during global cooling, suggesting it was stranded in unsuitable environments during periods of dramatic climate change. In particular, the loss of seed dormancy leads to germination under unfavourable conditions and reproduction challenges. This dormancy loss may have occurred through genetic changes that suppress ABA signalling and the loss of genes involved in seed maturation. The high-quality genome has also enabled us to reveal phenotypic trait evolution in Rhizophoraceae and identify divergent adaptation to intertidal and inland habitats. In summary, our study elucidates mechanisms underlying the decline and evaluates the adaptive potential of P. yunnanensis to future climate change, informing future conservation efforts.

Single nucleotide polymorphism (SNP) panels are powerful tools for assessing the genetic population structure and dispersal of fishes and can enhance management practices for commercial, recreational and subsistence mixed-stock fisheries. Arctic Char (Salvelinus alpinus), Brook Trout (Salvelinus fontinalis) and Lake Whitefish (Coregonus clupeaformis) are among the most harvested and consumed fish species in Northern Indigenous communities in Canada, contributing significantly to food security, culture, tradition and economy. However, genetic resources supporting Indigenous fisheries have not been widely accessible to northern communities (e.g. Inuit, Cree, Dene). Here, we developed Genotyping-in-Thousands by sequencing (GT-seq) panels for population assignment and mixed-stock analyses of three salmonids, to support fisheries stewardship or co-management in Northern Canada. Using low-coverage Whole Genome Sequencing data from 418 individuals across source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest Territories), James Bay (Québec) and Mistassini Lake (Québec), we developed a bioinformatic SNP filtering workflow to select informative SNP markers from genotype likelihoods. These markers were then used to design GT-seq panels, thus enabling high-throughput genotyping for these species. The three GT-seq panels yielded an average of 413 autosomal loci and were validated using 525 individuals with an average assignment accuracy of 83%. Thus, these GT-seq panels are powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed-stock fisheries across multiple regions. Interweaving genomic data derived from these tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in Indigenous communities, contributing to food security programmes and the economy in Northern Canada.

The ammonia monooxygenase subunit A (amoA) gene has been used to investigate the phylogenetic diversity, spatial distribution and activity of ammonia-oxidising archaeal (AOA) and bacterial (AOB), which contribute significantly to the nitrogen cycle in various ecosystems. Amplicon sequencing of amoA is a widely used method; however, it produces inaccurate results owing to the lack of a 'universal' primer set. Moreover, currently available primer sets suffer from amplification biases, which can lead to severe misinterpretation. Although shotgun metagenomic and metatranscriptomic analyses are alternative approaches without amplification bias, the low abundance of target genes in heterogeneous environmental DNA restricts a comprehensive analysis to a realisable sequencing depth. In this study, we developed a probe set and bioinformatics workflow for amoA enrichment sequencing using a hybridisation capture technique. Using metagenomic mock community samples, our approach effectively enriched amoA genes with low compositional changes, outperforming amplification and meta-omics sequencing analyses. Following the analysis of metatranscriptomic marine samples, we predicted 80 operational taxonomic units (OTUs) assigned to either AOA or AOB, of which 30 OTUs were unidentified using simple metatranscriptomic or amoA gene amplicon sequencing. Mapped read ratios to all the detected OTUs were significantly higher for the capture samples (50.4 ± 27.2%) than for non-capture samples (0.05 ± 0.02%), demonstrating the high enrichment efficiency of the method. The analysis also revealed the spatial diversity of AOA ecotypes with high sensitivity and phylogenetic resolution, which are difficult to examine using conventional approaches.

Symbiotic microbiota strongly impact host physiology. Amphibians and reptiles occupy a pivotal role in the evolutionary history of Animalia, and they are of significant ecological, economic, and scientific value. Many prior studies have found that symbiotic microbiota in herpetofaunal species are closely associated with host phylogeny, physiological traits, and environmental factors; however, insufficient integrated databases hinder researchers from querying, accessing, and reanalyzing these resources. To rectify this, we built the first herpetofaunal microbiota database (HMicroDB; https://herpdb.com/) that integrates 11,697 microbiological samples from 337 host species (covering 23 body sites and associated with 23 host phenotypic or environmental factors), and we identified 11,084 microbial taxa by consistent annotation. The standardised analysis process, cross-dataset integration, user-friendly interface, and interactive visualisation make the HMicroDB a powerful resource for researchers to search, browse, and explore the relationships between symbiotic microbiota, hosts, and environment. This facilitates research in host-microbiota coevolution, biological conservation, and resource utilisation.