The global sulphur cycle has implications for human health, climate change, biogeochemistry and bioremediation. The organosulphur compounds that participate in this cycle not only represent a vast reservoir of sulphur but are also used by prokaryotes as sources of energy and/or carbon. Closely linked to the inorganic sulphur cycle, it involves the interaction of prokaryotes, eukaryotes and chemical processes. However, ecological and evolutionary studies of the conversion of organic sulphur compounds are hampered by the poor conservation of the relevant pathways and their variation even within strains of the same species. In addition, several proteins involved in the conversion of sulphonated compounds are related to proteins involved in sulphur dissimilation or turnover of other compounds. Therefore, the enzymes involved in the metabolism of organic sulphur compounds are usually not correctly annotated in public databases. To address this challenge, we have developed HMSS2, a profiled Hidden Markov Model-based tool for rapid annotation and synteny analysis of organic and inorganic sulphur cycle proteins in prokaryotic genomes. Compared to its previous version (HMS-S-S), HMSS2 includes several new features. HMM-based annotation is now supported by nonhomology criteria and covers the metabolic pathways of important organosulphur compounds, including dimethylsulphoniopropionate, taurine, isethionate, and sulphoquinovose. In addition, the calculation speed has been increased by a factor of four and the available output formats have been extended to include iTol compatible data sets, and customized sequence FASTA files.

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy-Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package 'rCNV', which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon-Oncorhynchus tshawytscha), Rapture (American lobster-Homarus americanus), Exome-capture (Norway spruce-Picea abies) and WGS (Malaria mosquito-Anopheles gambiae).

The current biodiversity and climate crises highlight the need for efficient tools to monitor terrestrial ecosystems. Here, we provide evidence for the use of airborne eDNA analyses as a novel method for detecting terrestrial vertebrate communities in nature. Metabarcoding of 143 airborne eDNA samples collected during 3 days in a mixed forest in Denmark yielded 64 bird, mammal, fish and amphibian taxa, of which the detected 57 'wild' taxa represent over a quarter of the around 210 terrestrial vertebrates that occur in the overall area. We provide evidence for the spatial movement and temporal patterns of airborne eDNA and for the influence of weather conditions on vertebrate detections. This study demonstrates airborne eDNA for high-resolution biomonitoring of vertebrates in terrestrial systems and elucidates its potential to guide global nature management and conservation efforts in the ongoing biodiversity crisis.

Clonal propagation and extensive dispersal of seeds and asexual propagules are two important features of aquatic plants that help them adapt to aquatic environments. Accurate measurements of clonality and effective clonal dispersal are essential for understanding the evolution of aquatic plants. Here, we first assembled a high-quality chromosome-level genome of a widespread emergent aquatic plant Sparganium stoloniferum to provide a reference for its population genomic study. We then performed high-depth resequencing of 173 individuals from 20 populations covering different basins across its range in China. Population genomic analyses revealed three genetic lineages reflecting the northeast (NE), southwest (SW) and northwest (NW) of its geographical distribution. The NE lineage diverged in the middle Pleistocene while the SW and NW lineages diverged until about 2400 years ago. Clonal relationship analyses identified nine populations as monoclonal population. Dispersal of vegetative propagules was identified between five populations covering three basins in the NE lineage, and dispersal distance was up to 1041 km, indicating high dispersibility in emergent aquatic plant species. We also identified lineage-specific positively selected genes that are likely to be involved in adaptations to saline wetlands and high-altitude environments. Our findings accurately measure the clonality, determine the dispersal range and frequency of vegetative propagules, and detect genetic signatures of local adaptation in a widespread emergent aquatic plant species, providing new perspectives on the evolution of aquatic plants.

Mutations are the primary source of all genetic variation. Knowledge about their rates is critical for any evolutionary genetic analyses, but for a long time, that knowledge has remained elusive and indirectly inferred. In recent years, parent-offspring comparisons have yielded the first direct mutation rate estimates. The analyses are, however, challenging due to high rate of false positives and no consensus regarding standardized filtering of candidate de novo mutations. Here, we validate the application of a machine learning approach for such a task and estimate the mutation rate for the guppy (Poecilia reticulata), a model species in eco-evolutionary studies. We sequenced 4 parents and 20 offspring, followed by screening their genomes for de novo mutations. The initial large number of candidate de novo mutations was hard-filtered to remove false-positive results. These results were compared with mutation rate estimated with a supervised machine learning approach. Both approaches were followed by molecular validation of all candidate de novo mutations and yielded similar results. The ML method uniquely identified three mutations, but overall required more hands-on curation and had higher rates of false positives and false negatives. Both methods concordantly showed no difference in mutation rates between families. Estimated here the guppy mutation rate is among the lowest directly estimated mutation rates in vertebrates; however, previous research has also found low estimated rates in other teleost fishes. We discuss potential explanations for such a pattern, as well as future utility and limitations of machine learning approaches.

Previous studies have deciphered the genomic basis of host-symbiont metabolic complementarity in vestimentiferans, bathymodioline mussels, vesicomyid clams and Alviniconcha snails, yet little is known about the chemosynthetic symbiosis in Thyasiridae-a family of Bivalvia regarded as an excellent model in chemosymbiosis research due to their wide distribution in both deep-sea and shallow-water habitats. We report the first circular thyasirid symbiont genome, named Candidatus Ruthturnera sp. Tsphm01, with a size of 1.53 Mb, 1521 coding genes and 100% completeness. Compared to its free-living relatives, Ca. Ruthturnera sp. Tsphm01 genome is reduced, lacking components for chemotaxis, citric acid cycle and de novo biosynthesis of small molecules (e.g. amino acids and cofactors), indicating it is likely an obligate intracellular symbiont. Nevertheless, the symbiont retains complete genomic components of sulphur oxidation and assimilation of inorganic carbon, and these systems were highly and actively expressed. Moreover, the symbiont appears well-adapted to anoxic environment, including capable of anaerobic respiration (i.e. reductions of DMSO and nitrate) and possession of a low oxygen-adapted type of cytochrome c oxidase. Analysis of the host transcriptome revealed its metabolic complementarity to the incomplete metabolic pathways of the symbiont and the acquisition of nutrients from the symbiont via phagocytosis and exosome. By providing the first complete genome of reduced size in a thyasirid symbiont, this study enhances our understanding of the diversity of symbiosis that has enabled bivalves to thrive in chemosynthetic habitats. The resources will be widely used in phylogenetic, geographic and evolutionary studies of chemosynthetic bacteria and bivalves.

Here, we report a new multi-optical maps scaffolder (MOMS) aiming at utilizing complementary information among optical maps labelled by distinct enzymes. This pipeline was designed for data structure organization, scaffolding by path traversal, gap-filling and molecule reuse of optical maps. Our testing showed that this pipeline has uncapped enzyme tolerance in scaffolding. This means that there are no inbuilt limits as to the number of maps generated by different enzymes that can be utilized by MOMS. For the genome assembly of the human GM12878 cell line, MOMS significantly improved the contiguity and completeness with an up to 144-fold increase of scaffold N50 compared with initial assemblies. Benchmarking on the genomes of human and O. sativa showed that MOMS is more effective and robust compared with other optical-map-based scaffolders. We believe this pipeline will contribute to high-fidelity chromosome assembly and chromosome-level evolutionary analysis.

Topmouth culter (Culter alburnus) is an ecologically and economically important species belonging to the subfamily Culterinae that is native to and widespread in East Asia. Intraspecific variation of semi-buoyant and adhesive eggs in topmouth culter provides an ideal opportunity to investigate the genetic mechanisms of spawning habits underlying the adaptive radiation of cyprinids in East Asia. In this study, we present a chromosome-level genome assembly of topmouth culter and re-sequenced 158 individuals from six locations in China covering three geographical groups and two egg type variations. The topmouth culter genome size was 1.05 Gb, with a contig N50 length of 17.8 Mb and anchored onto 24 chromosomes. Phylogenetic analysis showed that the divergence time of the Culterinae was coinciding with the time of initiation of the Asian monsoon intensification. Gene family evolutionary analysis indicated that the expanded gene families in topmouth culter were associated with dietary adaptation. Population-level genetic analysis indicated clear differentiation among the six populations, which were clustered into three distinct clusters, consistent with their geographical divergence. The historical effective population size of topmouth culter correlated with the Tibetan Plateau uplifting according to the demographic history reconstruction. A selective sweep analysis between adhesive and semi-buoyant egg populations revealed the genes associated with the hydration and adhesiveness of eggs, indicating divergent selection towards different hydrological environments. This study offers a high-resolution genetic resource for further studies on evolutionary adaptation, genetic breeding and conservation of topmouth culter, providing insights into the molecular mechanisms for egg type variation of East Asian cyprinids.

Estimates of gene flow resulting from landscape resistance inferences frequently inform conservation management decision-making processes. Therefore, results must be robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 32 individual-based genetic distance metrics on the robustness and accuracy of landscape resistance modelling results. We analysed three empirical microsatellite datasets and 36 simulated datasets that varied in landscape resistance and genetic spatial autocorrelation. We used ResistanceGA to generate optimised multi-feature resistance surfaces for each of these datasets using 32 different genetic distance metrics. Results of the empirical dataset demonstrated that the choice of genetic distance metric can have strong impacts on inferred optimised resistance surfaces. Simulations showed accurate parametrisation of resistance surfaces across most genetic distance metrics only when a small number of environmental features was impacting gene flow. Landscape scenarios with many features impacting gene flow led to a generally poor recovery of true resistance surfaces. Simulation results also emphasise that choosing a genetic distance metric should not be based on marginal R2 -based model fit. Until more robust methods are available, resistance surfaces can be optimised with different genetic distance metrics and the convergence of results needs to be assessed via pairwise matrix correlations. Based on the results presented here, high correlation coefficients across different genetic distance categories likely indicate accurate inference of true landscape resistance. Most importantly, empirical results should be interpreted with great caution, especially when they appear counter-intuitive in light of the ecology of a species.

Indigenous peoples have cultivated biodiverse agroecosystems since time immemorial. The rise of metagenomics and high-throughput sequencing technologies in biodiversity studies has rapidly expanded the scale of data collection from these lands. A respectful approach to the data life cycle grounded in the sovereignty of indigenous communities is imperative to not perpetuate harm. In this paper, we operationalize an indigenous data sovereignty (IDS) framework to outline realistic considerations for genomic data that span data collection, governance, and communication. As a case study for this framework, we use arthropod genomic data collected from diversified and simplified farm sites close to and far from natural habitats within a historic Kānaka 'Ōiwi (Indigenous Hawaiian) agroecosystem. Diversified sites had the highest Operational Taxonomic Unit (OTU) richness for native and introduced arthropods. There may be a significant spillover effect between forest and farm sites, as farm sites near a natural habitat had higher OTU richness than those farther away. We also provide evidence that management factors such as the number of Polynesian crops cultivated may drive arthropod community composition. Through this case study, we emphasize the context-dependent opportunities and challenges for operationalizing IDS by utilizing participatory research methods, expanding novel data management tools through the Local Contexts Hub, and developing and nurturing community partnerships-all while highlighting the potential of agroecosystems for arthropod conservation. Overall, the workflow and the example presented here can help researchers take tangible steps to achieve IDS, which often seems elusive with the expanding use of genomic data.

The rise of sedimentary ancient DNA (sedaDNA) studies has opened new possibilities for studying past environments. This groundbreaking area of genomics uses sediments to identify organisms, even in cases where macroscopic remains no longer exist. Managing this substrate in Indigenous Australian contexts, however, requires special considerations. Sediments and soils are often considered as waste by-products during archaeological and paleontological excavations and are not typically regulated by the same ethics guidelines utilised in mainstream 'western' research paradigms. Nevertheless, the product of sedaDNA work-genetic information from past fauna, flora, microbial communities and human ancestors-is likely to be of cultural significance and value for Indigenous peoples. This article offers an opinion on the responsibilities of researchers in Australia who engage in research related to this emerging field, particularly when it involves Indigenous communities. One aspect that deserves consideration in such research is the concept of benefit sharing. Benefit sharing refers to the practice of ensuring that the benefits that arise from research are shared equitably with the communities from which the research data were derived. This practice is particularly relevant in research that involves Indigenous communities, who may have unique cultural and spiritual connections to the research material. We argue that the integration of Traditional Knowledges into sedaDNA research would add enormous value to research and its outcomes by providing genomic outputs alongside and within the rich context of multimillennia oral histories.