The increasing climate-driven replacement of kelp forests by turf algae highlights the need for efficient biodiversity monitoring. Traditionally, monitoring turf communities involves species identification based on morphology, which is challenging due to their reduced dimensions and highly variable morphology. Molecular methods promise to revolutionize this field, but their real-world effectiveness needs to be evaluated. Here, we evaluate the performance of DNA metabarcoding (COI and rbcL markers) and morphological identification (in situ and photoquadrat identifications) to describe intertidal turf communities along the Portuguese coast. When comparing metabarcoding with in situ and photoquadrat identification, it was found that both COI and rbcL markers detected more taxa than the other two (277 and 140 vs 28 and 34 taxa, respectively). Metabarcoding also showed greater discrimination of turf communities between shores and regions, matching our knowledge of the geographical and climatic patterns for the region. However, certain taxa that were identified by in situ and photoquadrat approaches were not detected through metabarcoding, likely due to lack of reference barcodes or taxonomic resolution. Our multi-marker metabarcoding approach was more efficient than morphology-based methods in characterizing turf communities along the Portuguese coast, differentiating morphologically similar species, and detecting unicellular organisms. Additionally, although not the primary focus, the COI marker identified metazoans, which can be used in future ecological studies on species co-occurrence and algae-animal interactions. Metabarcoding emerges as a valuable tool for monitoring these communities, particularly in long-term programs requiring accuracy, speed, and reproducibility.

Comparative genomic studies of closely related taxa are important for our understanding of the causes of divergence on a changing Earth. This being said, the genomic resources available for marine intertidal molluscs are limited and currently, there are few publicly available high-quality annotated genomes for intertidal habitats and for molluscs in general. Here we report transcriptome assemblies for six species of Patellogastropoda and genome assemblies and annotations for three of these species (Scurria scurra, Scurria viridula, and Scurria zebrina). Comparative analysis using these genomic resources suggest that there was a large gene family contraction during the early evolutionary history of Patellogastropoda (140-170 Mya) and recently diverging lineages (10-20 Mya) have experienced similar amounts of contractions and expansions but across different gene families. Furthermore, differences among recently diverged species are reflected in variation in the amount of coding and noncoding material in genomes, such as amount of repetitive elements and lengths of transcripts and introns and exons. Additionally, functional ontologies of species-specific and duplicated genes together with demographic inference support the finding that recent divergence among members of the genus Scurria aligns with their unique ecological characteristics. Overall, the resources presented here will be extremely valuable for future studies of adaptation in molluscs and in intertidal habitats as a whole.

While a best practice for evaluating the behavior of genetic clustering algorithms on empirical data is to conduct parallel analyses on simulated data, these types of simulation techniques often involve sampling genetic data with replacement. In this paper we demonstrate that sampling with replacement, especially with large marker sets, inflates the perceived statistical power to correctly assign individuals (or the alleles that they carry) back to source populations—a phenomenon we refer to as resampling-induced, spurious power inflation (RISPI). To address this issue, we present gscramble a simulation approach in R for creating biologically informed individual genotypes from empirical data that: 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. This framework makes it possible to simulate admixed individuals in a way that respects the physical linkage between markers on the same chromosome and which does not suffer from RISPI. This is achieved in gscramble by allowing users to specify pedigrees of varying complexity in order to simulate admixed genotypes, segregating and tracking haplotype blocks from different source populations through those pedigrees, and then sampling—using a variety of permutation schemes—alleles from empirical data into those haplotype blocks. We demonstrate the functionality of gscramble with both simulated and empirical data sets and highlight additional uses of the package that users may find valuable.

Complete plastid genomes have recently been proposed as ”super-barcodes” for genetic identification and delineation in plants, for which standard DNA barcodes may fail. However, relatively few studies have fully considered the genetic mechanisms inherent to the taxonomic complexity of specious genera and how this may affect species resolution via complete plastome barcodes. Pedicularis is a highly diversified group of plants whose floral traits have undergone rapid radiation in the Himalaya-Hengduan Mountains, which can complicate identification and classification within the group based on morphological traits alone. In this study, we analyzed 292 plastomes representing 96 species of Pedicularis to compare species discrimination rates with those based on standard DNA barcodes. The results show that the traditional standard barcode (nrITS+matK+rbcL+trnH-psbA) and the large single copy (LSC) region of the plastid genome share the highest discrimination rates (80.21%), followed by the plastid genome, the supermatrixes of protein-coding genes and high variable regions (79.17%), and the matK gene and ycf1 gene alone could discriminate 78.13% of species. Further, we found that the sequence length and percentage of parsimony-informative sites significantly influence species discrimination rates, and genes with higher species discrimination rates are more likely to be conservative in selection. Given the extra costs and time required for generating such “super-barcodes” to try to discern species in taxonomically complex genera, this study questions their use and calls for more efforts to explore nuclear markers that may improve discrimination rates in future studies with relatively low-cost and effort.

Insects, the most numerous and diverse group of animal species on Earth, have important interactions with humans through providing resources, transmitting diseases, and damaging agricultural cultivars. Cytochrome P450 monooxygenases (P450s) are one of the most important protein families in insects implicated in the endogenous metabolism and detoxification of xenobiotics, including allelochemicals, insecticides, and environmental pollutants. To better understand the evolution and function of insect P450s and support the development and application of insecticides for pest control, an integrated bioinformatics platform is highly desirable. Here, we present the Insect Cytochrome P450 database (ICPD, http://www.insectp450.net/), which contains 66,513 P450s collected from public databases and predicted from the genomes of 680 insect species using a standardized bioinformatics protocol. Phylogenetic relationships between P450 genes are constructed for each species. The structures of all P450 proteins in the database are predicted using AlphaFold v2 and/or ESMFold, then visualized using WeView. Web services, such as BLAST, homogeneous modeling, and molecular docking, are provided for determining the catalytic activities of P450 proteins. The ICPD will facilitate systematic investigations of the evolution and functions of the complete insect P450 complement, and represents a powerful tool for guiding insecticide design and application.

Shiitake mushrooms (Lentinula edodes) hold significant cultural and economic value, particularly in Asia where they are extensively cultivated. The diversification of shiitake cultivars, driven by the need to adapt to various climatic conditions and cultivation methods, has resulted in over 200 distinct cultivars. Reliable identification of these cultivars is crucial for breeding, intellectual property protection, and effective genetic resource management. Traditional morphological methods are inadequate due to their subjectivity and labor-intensive nature. This study leverages nanopore high-throughput sequencing to comprehensively analyze the rDNA regions (SSU, ITS, LSU, IGS) of 41 shiitake strains from Taiwan’s Bioresource Collection and Research Center (BCRC), comprising 5 wild strains, 33 commercial strains, and 3 wild-commercial hybrids. Our results identified the IGS1 region as the most variable and suitable for cultivar differentiation. Consequently, we developed an interactive online database that integrates 317 IGS1 sequences from Taiwan, Japan, and China. This platform allows users to upload their IGS1 sequences and identify similar cultivars through a user-friendly interface, enhancing the precision and efficiency of shiitake cultivar identification.

Kinship estimation is widely used in ecological and evolutionary research, particularly in studies of human genealogy and genome-wide associations. In conservation, restoration, agriculture, and forestry, identifying relationships between individuals can be crucial for successful population management and can provide insight into inheritance patterns. Kinship estimation methods are typically designed for large datasets with hundreds of thousands of single-nucleotide polymorphisms. However, studies of non-model species often use much smaller datasets obtained using reduced-representation sequencing. To evaluate the performance of kinship estimation methods under these circumstances, we applied six algorithms to datasets from six non-model Australian flowering plant species (_Acacia terminalis_, _Acacia suaveolens_, _Banksia serrata_, _Banksia aemula_, _Hakea sericea_, and _Hakea teretifolia_), encompassing 3,390 individuals and 369 families. Our results show different performances of kinship methods on reduced-representation sequence data compared with prior evaluations. PC-Relate, RelateAdmix, and Goudet’s beta dosage exhibited limited precision, KING Homo and KING Robust demonstrated high precision with limited sensitivity, while PLINK displayed variable sensitivity and precision. The sensitivity and precision of the methods were affected in various ways by filtering parameters; each method showed its best performance under different thresholds for minor allele frequency and locus missingness. We also present a case study that illustrates a practical application of the methods, demonstrating how estimates of kinship can inform management of seed production areas of the broadleaf hopbush (_Dodonaea viscosa_). Based on our findings, we offer specific recommendations for utilizing kinship estimation methods in studies of reduced-representation sequence data from non-model species.

The advancement of next-generation sequencing (NGS) technologies has been revolutionary for the field of evolutionary biology. This technology has led to an abundance of available genomes and transcriptomes for researchers to mine. Specifically, researchers can mine for various types of molecular markers that are vital for phylogenetic, evolutionary, and ecological studies. Numerous tools have been developed to extract these molecular markers from NGS data. However, due to an insufficient number of well-annotated reference genomes for non-model organisms, it remains challenging to obtain these markers accurately and efficiently. Here, we present GeneMiner, an improved and expanded version of our previous tool, Easy353. GeneMiner combines the reference-guided de Bruijn graph assembly with seed self-discovery and greedy extension. Additionally, it includes a verification step using a parameter-bootstrap method to reduce the pitfalls associated with using a relatively distant reference. Our results using both experimental and simulation data showed GeneMiner can accurately acquires phylogenetic molecular markers for plants using transcriptomic, genomic, and other NGS data. GeneMiner is designed to be user-friendly, fast, and memory efficient. Further, it is compatible with Linux, Windows, and macOS. All source codes are publicly available on GitHub for easy accessibility and transparency (https://github.com/yyscu/GeneMiner).

More than 30% of extant shark species are classified as threatened with extinction, yet reliable population-level data is often rare, frequently due to a lack of genomic resources. Here we present a new genome-wide marker set for endangered shark species. We developed a target gene capture bait set based on the transcriptome and genome of a Lamnid shark, and tested it on 36 shark specimens, representing seven species from three orders. Illumina read mapping and calling of Single Nucleotide Polymorphisms (SNPs) showed high target recovery rates, especially in the order Lamniformes, providing several thousand detected biallelic SNPs in each species tested. Our results show this marker set can be used for SNP-calling in a broad range of shark species, enabling detailed population assessments and other ecological and evolutionary studies.

Genome visualization tools are important for exploring genomic features and their interactions. Currently, visualization of the plant mitochondrial genomes (mitogenome) depends on those tools designed originally for animal mitogenomes and plant plastomes. These tools cannot faithfully present features unique to the plant mitogenomes, such as non-linear exon arrangement for genes, prevalence of functional non-coding features, and complex chromosomal architectures. To address these challenges, a software package plant mitochondrial genome map (PMGmap), was developed using Python programming language. PMGmap can draw genes at exon levels, draw cis- and trans-splicing gene maps, draw non-coding features, draw repetitive sequences, scale the genic regions using a scaling the genic regions on the genome (SGM) algorithm, and draw multiple chromosomes simultaneously. We compared PMGmap with other leading tools on 405 plant mitogenomes and found that PMGmap allowed the visualization of the above-mentioned features better than those tools. We believe PMGmap will become an invaluable tool for plant mitogenome research. The web and container versions and the source code of PMGmap can be accessed at http://www.1kmpg.cn/pmgmap.

Metazoa-level Universal Single-Copy Orthologs (mzl-USCOs) are universally applicable markers for DNA taxonomy in animals which can replace or supplement single-gene barcodes. While previously mzl-USCOs from target enrichment data were shown to reliably distinguish species, here we tested whether USCOs are an evenly distributed, representative sample of a given metazoan genome and therefore able to cope with past hybridization events and incomplete lineage sorting. This is relevant for coalescent-based species delimitation approaches, which critically depend on the assumption that the investigated loci do not exhibit autocorrelation due to physical linkage. Based on 239 assessed chromosome-level assembled genomes, we confirmed that mzl-USCOs are genetically unlinked for practical purposes and a representative sample of a genome in terms of reciprocal distances between USCOs on a chromosome and of distribution across chromosomes. We tested the suitability of mzl-USCOs extracted from genomes for species delimitation and phylogeny in four case studies: Anopheles mosquitos, Drosophila fruit flies, Heliconius butterflies, and Darwin’s finches. In almost all instances, USCOs allowed delineating species and yielded phylogenies that correspond to those generated from whole genome data. Our phylogenetic analyses demonstrate that USCOs may complement single-gene DNA barcodes and provide more accurate taxonomic inferences. Combining USCOs from sources that used different versions of ortholog reference libraries to infer marker orthology may be challenging and at times impact taxonomic conclusions. However, we expect this problem to become less severe as the rapidly growing number of reference genomes provides a better representation of the number and diversity of organismic lineages.

As sequencing technology continues to rapidly improve, studies investigating the microbial communities of host organisms (i.e., microbiomes) are becoming not only more popular but also more financially accessible. Across many taxa, microbiomes can have important impacts on organismal health and fitness. To evaluate the microbial community composition of a particular microbiome, microbial DNA must be successfully extracted. Fecal samples are often easy to collect and are a good source of gut microbial DNA. However, in birds and reptiles, microbial DNA extractions from fecal matter have proven to be difficult due to high concentrations of uric acid, an inhibitor of DNA extractions. Here, we present a new microbial DNA extraction method that is highly effective for avian species and displays higher efficiency and consistency than other commonly used methodologies. Further, our method is also effective in extracting microbial DNA from oils collected from the avian preen gland. Preen oil chemicals are important for many aspects of avian life, and the biosynthesis of these chemicals is dependent on the preen gland microbial community. We expect our method will facilitate microbial DNA extractions from multiple avian microbiome reservoirs, which have previously proved difficult and expensive. Our method therefore increases the feasibility of future studies of avian host microbiomes.

A document by Samuel Speak. Click on the document to view its contents.

Accurate and efficient genotyping of microsatellite loci is essential for their application in population genetics and various demographic analysis. Protocols for next generation sequencing of microsatellite loci generate high-throughput and cross-compatible allele scoring characteristics: common issues associated with size separation on conventional capillary-based protocols. As a result, we have developed a novel, ultra-fast, all-in-one software Seq2Sat in C++ to support accurate automated microsatellite genotyping. It directly takes raw reads of microsatellite amplicons and subsequently performs read quality control before inferring genotypes based on depth of read, sequence composition and length. It does not produce any intermediate files, making I/O very efficient. Additionally, we developed a module in Seq2Sat for sex identification based on sex locus amplicons. We further developed a user-friendly website-based platform SatAnalyzer to conduct reads-to-report analyses by calling Seq2Sat to generate genotype tables and interactive genotype graphs for manual editing. SatAnalyzer also allows visualization of read quality and distribution across loci and samples to troubleshoot multiplex optimization and high-quality library preparation. To evaluate its performance, we benchmarked SatAnalyzer against conventional capillary gel electrophoresis and an existing microsatellite genotyping software MEGASAT. Results show that SatAnalyzer can achieve > 0.993 genotyping accuracy and Seq2Sat is ~ 5 times faster than MEGASAT despite many more informative tables and figures generated. Seq2Sat and SatAnalyzer are freely available at github (https://github.com/ecogenomicscanada/Seq2Sat) and dockerhub (https://hub.docker.com/r/rocpengliu/satanalyzer).

Metabarcoding is an increasingly popular and accessible method for assessing bacterial communities across a wide range of environments, and as the sequence data archives grow, sequence data reuse will likely become an important source of novel insights into the ecology of microbes. While literature on the benefits of longer read lengths for the study of microbial communities, little is known about the (re)usability of shorter (<200 bp) read lengths, but this information is essential to improve the reuse and comparability of metabarcoding data across studies. This study reanalyzed three 16S rRNA datasets targeting aquatic, animal-associated, and soil microbiomes, and evaluated how processing the sequence data across a range of read lengths affected the resulting taxonomic assignments, biodiversity metrics, and differential (i.e., before-after treatment) analyses. Short read lengths successfully recovered ecological patterns, and limited increases in resolution were observed beyond 100 bp reads across environments. Furthermore, abundance-weighted diversity metrics (e.g., Inverse Simpson index or Bray-Curtis dissimilarities) were more robust to variation in read lengths. Importantly, the total number of ASVs detected increased with read length, highlighting the need to consider metabarcoding-derived diversity estimates within the context of the bioinformatics parameters selected. This study provides evidence-based guidelines for the processing of short reads.

Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman’s rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel’s λ model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, R2, p-value, Pagel’s λ, Blomberg et al.’s K, and the estimated λ in Pagel’s λ model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher λ or K value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.

Current methodologies of genome-wide Single Nucleotide Polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on Self-Organizing Maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. We follow a classical approach that explores genotype datasets to select SNP loci for each query missing SNP genotype to build training sets, and that initializes and trains the neural networks to finally use the SOM-derived clustering to impute the best genotype. To automate the imputation process, we have implemented GTIMPUTATION, an open source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

Continued advancements in environmental DNA (eDNA) research have made it possible to access intraspecific variation from eDNA samples, opening new opportunities to expand non-invasive genetic studies of wild animal populations. However, the use of eDNA samples for individual genotyping, as typically performed in non-invasive genetics, still remained unachieved. We present the first successful individual genotyping of eDNA obtained from snow tracks of three large carnivores: brown bear (Ursus arctos), European lynx (Lynx lynx) and wolf (Canis lupus). DNA was extracted using a protocol for isolating water eDNA and genotyped using amplicon sequencing of short tandem repeats (STR) and, for brown bear, a sex marker, on a high-throughput sequencing platform. Individual genotypes were obtained for all species, but genotyping performance differed among samples and species. Multilocus genotyping success for individual identification was higher for brown bear samples (6 over 7), than for wolf (7 over 10) and lynx (4 over 9) samples. The sex marker was genotyped in 5 out of 7 brown bear samples. Results for three species show that reliable individual genotyping, including sex identification, is now possible from eDNA in snow tracks, underlining its vast potential to complement the non-invasive genetic methods used for wildlife. To fully leverage the application of snow track eDNA, improved understanding of the ideal species- and site-specific sampling conditions, as well as laboratory methods promoting genotyping success are needed. This will also inform efforts to retrieve and type nuclear DNA from other eDNA samples, thereby advancing eDNA–based individual and population level studies.