Both environmental DNA (eDNA) and environmental RNA (eRNA) have been widely adopted for biodiversity assessment. While eDNA often persists longer in environments, eRNA offers a more current view of biological activities. In eRNA metabarcoding, extracted eRNA is reverse transcribed into complementary DNA (cDNA) for metabarcoding. However, the efficacy of various reverse transcription strategies has not been evaluated. Here we compared the biodiversity recovery efficiency of three strategies: random priming with hexamers, oligo(dT) priming, and taxa-specific priming using Mifish-U for fish in both high- and low-biodiversity regions. Our results demonstrate that reverse transcription strategies significantly impact biodiversity recovery. Random priming consistently detected the highest number of taxa in both low- and high-biodiversity regions. In low-biodiversity areas, oligo(dT) performed comparably to random hexamers; however, in high-biodiversity regions, random hexamers outperformed oligo(dT), particularly in recovering rare taxa. While taxa-specific priming was comparative to the other strategies for high-abundance taxa, it was less effective for rare taxa, thus limiting its utility for comprehensive biodiversity assessment. These differences are largely due to the multiple binding sites for random hexamers compared to the fewer or absent sites with oligo(dT) and taxa-specific primers under high eRNA degradation. Combining random hexamers and oligo(dT) significantly improved taxa recovery, especially for low-abundance species, supporting its best practice in eukaryotes. For prokaryotes or genes lacking polyadenylation, random priming is favored over taxa- or gene-specific priming. Collectively, these findings underscore the critical importance of selecting appropriate reverse transcription strategies in eRNA metabarcoding, with significant implications for effective biodiversity monitoring and conservation efforts.

The ability to assign biological samples to source populations based on genetic variation with high accuracy and precision is important for numerous applications from ecological studies through wildlife conservation to epidemiology. However, population assignment when genetic differentiation is low is challenging, and methods to address this problem are lacking. The application of artificial neural networks to population assignment using genomic data is highly promising. Here we present popfinder: a new, easy to use Python-based artificial neural network pipeline for genetic population assignment. We tested popfinder both with simulated genetic data from populations connected by varying levels of gene flow, and with reduced-representation sequence data for three species of seabirds with weak to no population genetic structure. Popfinder was able to assign individuals to their source populations with high accuracy, precision and recall in most cases, including both simulated and empirical datasets, except in the weakest empirical population structure dataset, where the comparator programs also performed poorly. Compared to other available software, popfinder was slower on the simulated data sets due to hyperparameter tuning and the fact that it does not reduce the dimensionality of the data set; however, all programs ran in seconds on empirical data sets. Additionally, popfinder provides a perturbation ranking method to help develop optimized SNP panels for genetic population assignment, and is designed to be user-friendly. Finally, we caution users of all assignment programs to watch both for leakage of data during model training, and for unequal detection probabilities.

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 macro-evolution, comprehensively utilizing diverse phylogenomic signals, such as genes, SNPs, structural variations, as well as mitochondrial and chloroplast genomes. PhyloForge’s innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling 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 customizable 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).

Insect decline necessitates the development and use of standardized protocols for regular monitoring. These methods have to be rapid, efficient, and cost-effective to allow for large-scale implementation. Many insect sampling and molecular methods have been developed. This includes Malaise trapping, high-throughput DNA barcoding (“megabarcoding”), and metabarcoding. The latter allows for assessing the species diversity in whole samples using few steps, but sample heterogeneity in terms of body size remains a challenge since large insects contribute disproportionately more mtDNA than small ones, potentially overwhelming the template DNA from small species that then go undetected. Size-sorting can mitigate this problem, but no satisfying automated, rapid and non-destructive solutions are available. We introduce the EntoSieve, a low-cost and DIY motorized instrument that disentangles and sorts abundant insect bulk samples into several body-size fractions while minimizing damage to specimens thus reducing the risk of DNA contamination across size fractions (e.g., legs of large specimens in small body-size fraction). EntoSieve utilizes readily available components, 3D-printed parts and customizable meshes, thus enabling parallelization at low cost. We here show the efficiency of the EntoSieve for three samples with more than 10,000 specimens using three sieving protocols and assess the impact on specimen integrity. Efficiency ranged from 92-99% and achieved within 18-60 minutes and specimen damage was not significant for subsamples. By facilitating rapid pre-processing, the device contributes to producing morphologically valuable vouchers for megabarcoding and is likely to improve compositional diversity accuracy across size classes when using metabarcoding.

javascript:void(0) The boll weevil, Anthonomus grandis grandis Boheman, and thurberia weevil, Anthonomus grandis thurberiae Pierce (Coleoptera: Curculionidae), together comprise a species complex that ranges throughout Mexico, the southwestern regions of the United States, and South America. The boll weevil is a historically damaging and contemporaneously threatening pest to commercial Upland cotton, Gossypium hirsutum L. (Malvales: Malvaceae), whereas the thurberia weevil is regarded as an innocuous non-pest subspecies that is mostly found on non-cultivated Gossypium species, e.g. Thurber’s or Arizona cotton, G. thurberi, throughout its native range in western parts of Mexico and the southwestern US. Recent independent analyses using mitochondrial COI and whole genome ddRADseq have suggested the independent evolution of these lineages is largely attributable to geographic isolation and not to host plant preference. We furthered this investigation by employing comparative genomic, population genetic, and pangenome methodologies to identify large and small polymorphisms within this complex and described their role in demography and adaptation. We also leveraged genetic differences to identify nearly 40,000 diagnostic loci between the subspecies, find genes under selection, and model the subspecies’ shared and unique evolutionary history. Interestingly, structural variations capture a large proportion of genes at the population level and demographic reconstruction suggests a split between these subspecies that coincides with cotton cultivation in the southern U.S. in the late 1800s. Observed polymorphisms are enriched for reproductive, regulatory, and metabolic genes which may be attributed to the boll weevil’s rapid expansion onto commercial cotton.

Motivation: One essential initial step in the analysis of ancient DNA is to authenticate that the DNA sequencing reads actually are from ancient DNA. This is done by assessing if the reads exhibit typical characteristics of postmortem damage, including cytosine deamination and nicks. We present a novel statistical method implemented in a fast multithreaded program, ngsBriggs, that enables rapid quantification of PMD by estimation of the Briggs ancient damage model parameters (Briggs parameters). Results: Using a multinomial model with maximum likelihood fit, ngsBriggs accurately estimates the parameters of the Briggs model, quantifying the PMD signal from single and double-stranded DNA regions.We extend the original Briggs model to capture PMD signals for contemporary sequencing platforms and show that ngs- Briggs accurately estimates the Briggs parameters across a variety of contamination levels. Classification of reads into ancient or modern reads, for the purpose of decontamination, is significantly more accurate using ngsBriggs than using other methods available. Furthermore, ngsBriggs is substantially faster than other state-of-the-art methods. ngsBriggs offers a practical and accurate method for researchers seeking to authenticate ancient DNA and improve the quality of their data.

Molecular Ecology News and Views

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.

Marine sponges have recently emerged as efficient natural environmental DNA (eDNA) samplers. The ability of sponges to accumulate eDNA provides an exciting opportunity to reconstruct contemporary communities and ecosystems with high temporal and spatial precision. However, the use of historical eDNA (heDNA), trapped within the vast number of specimens stored in scientific collections, opens up the opportunity to begin to reconstruct the communities and ecosystems of the past. Here, using a variety of Antarctic sponge specimens stored in an extensive marine invertebrate collection, we were able to recover information on Antarctic fish biodiversity from specimens up to 20 years old. We successfully recovered 64 fish heDNA signals from 27 sponge specimens. Alpha diversity measures did not differ among preservation methods, but sponges stored frozen had a significantly different fish community composition compared to those stored dry or in ethanol. Our results show that we were consistently and reliably able to extract the heDNA trapped within marine sponge specimens, thereby enabling the reconstruction and investigation of communities and ecosystems of the recent past with a spatial and temporal resolution previously unattainable. Future research into heDNA extraction from other preservation methods, as well as the impact of specimen age and collection method will strengthen and expand the opportunities for this novel resource to access new knowledge on ecological change during the last century.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS, Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

Single Nucleotide Polymorphism (SNP) panels are powerful tools for assessing genetic population structure and dispersal of fish that 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 amongst the top harvested and consumed fish species in northern Indigenous communities in Canada, contributing significantly to food security, culture, and economy. Genomic resources like SNP panels, however, have not been widely accessible to Indigenous fisheries managers. We developed Genotyping-in-Thousands by sequencing (GT-seq) panels for population assignment and mixed-stock analyses for three salmonids to support fisheries 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 used to design GT-seq panels enabling high-throughput genotyping. Three GT-seq panels yielded an average of 413 autosomal loci and were validated with an average assignment accuracy of 98.5%. These GT-seq panels emerge as powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed stock fisheries across multiple regions. Interweaving these novel genomic-derived tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in northern Indigenous communities, contributing to food security programs and the economy in northern Canada.

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.

not-yet-known not-yet-known not-yet-known unknown Emberiza buntings (Aves: Emberizidae) exhibit extensive diversity and rapid diversification within the Old World, particularly in the eastern Palearctic, making them valuable models for studying rapid radiation among sympatric species. Despite their ecological and morphological diversity, there remains a significant gap in understanding the genomic underpinnings driving their rapid speciation. To address this gap, we assembled high-quality chromosome-level genomes of five representative Emberiza species (E. aureola, E. pusilla, E. rustica, E. rutila, and E. spodocephala). Comparative genomic analysis revealed distinct migration-related evolutionary adaptations in their genomes, including variations in lipid metabolism, oxidative stress response, locomotor ability, and circadian regulation. These changes may facilitate the rapid occupation of emerging ecological niches and provide opportunities for species diversification. Additionally, these five species exhibited abnormal abundances of long terminal repeat retrotransposons (LTRs), comprising over 20% of their genomes, with insertion times corresponding to their divergence (~2.5 million years ago). The presence of LTRs influenced genome size, chromosomal structure, and single-gene expression, suggesting their role in promoting the rapid diversification of Emberiza species. These findings offer valuable insights into the adaptive radiation of Emberiza and establish a robust theoretical foundation for further exploration of the patterns and mechanisms underlying their diversification.

The ITS-2-rRNA has been particularly useful for nematode metabarcoding. but does not resolve all phylogenetic relationships and reference sequences are not available for many nematode species. This is a particular issue when metabarcoding complex communities such as wildlife parasites or terrestrial and aquatic free-living nematode communities. We have used markerDB to produce four databases of distinct regions of the rRNA cistron; the 18S rRNA gene, the 28S rRNA gene, the ITS-1 intergenic spacer and the region spanning ITS-1_5.8S_ITS-2. These databases comprise 2645, 254, 13461, and 10107 unique full-length sequences representing 1391, 204, 1837, and 1322 nematode species, respectively. The comparative analysis illustrates the complementary value but also reveals a better representation of Clade III, IV, and V than Clade I and Clade II nematodes in each case. Although the ITS-1 database includes the largest number of unique full-length sequences, the 18S rRNA database provides the widest taxonomic coverage. We also developed, PrimerTC; a tool to assess primer sequence conservation across any reference sequence database and have applied it to evaluate a large number of previously published rRNA cistron primers. We identified sets of primers that currently provide the broadest taxonomic coverage for each rRNA marker across the nematode phylum. These new resources will facilitate more comprehensive metabarcoding of nematode communities using either short-read or long-read sequencing platforms. Further, PrimerTC is available as a simple WebApp to guide or assess PCR primer design for any genetic marker and/or taxonomic group beyond the nematode phylum.

Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user-friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide- and amino acid-based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies, and community diversity metrics. The vAMPirus analytical framework leverages 16 different opensource tools and provides optional approaches that can increase the ratio of biological signal-to-noise and thereby reveal patterns that would have otherwise been masked. Here, we validate the vAMPirus analytical framework and illustrate its implementation as a general virus amplicon sequencing workflow by recapitulating findings from two previously published double-stranded DNA virus datasets. As a case study, we also apply the program to explore the diversity and distribution of a coral reef-associated RNA virus. vAMPirus is streamlined within Nextflow, offering straightforward scalability, standardization, and communication of virus lineage-specific analyses. The vAMPirus framework is designed to be adaptable; community-driven analytical standards will continue to be incorporated as the field advances. vAMPirus supports researchers in revealing patterns of virus diversity and population dynamics in nature, while promoting study reproducibility and comparability.

Organelle genomes serve as crucial datasets for investigating the genetics and evolution of plants and animals, genome diversity, and species identification. To enhance the collection, analysis, and visualization of such data, we have developed a novel open-source software tool named Organelle Genome Utilities (OGU). The software encompasses three modules designed to streamline the handling of organelle genome data. The data collection module is dedicated to retrieving, validating, and organizing sequence information. The evaluation module assesses sequence variance using a range of methods, including novel metrics termed stem and terminal phylogenetic diversity, as well as observed resolution. The primer module could design universal primers for downstream applications. Finally, a visualization pipeline has been developed to present comprehensive insights into organelle genomes across different lineages rather than focusing solely on individual species. The performance, compatibility, and stability of OGU have been rigorously evaluated through benchmarking with four datasets, including one million mixed GenBank records, plastid genomic data from the Lamiaceae family, mitochondrial data from rodents, and 308 plastid genomes sourced from various angiosperm families. Based on software capabilities, we have identified 30 plastid intergenic spacers that exhibit a moderate evolutionary rate and offer practical utility comparable to coding regions, which highlights the potential applications of intergenic spacers in organelle genomes. We anticipate that OGU will substantially enhance the efficient utilization of organelle genomic data and broaden the prospects for related research endeavors.

Molecular techniques like metabarcoding, while promising for exploring diversity of communities, are often impeded by the lack of reference DNA sequences available for taxonomic annotation. Our study explores the benefits of combining targeted DNA barcoding and morphological taxonomy to improve metabarcoding efficiency, using beach meiofauna as a case study. Beaches are globally important ecosystems and are inhabited by meiofauna, microscopic animals living in the interstitial space between the sand grains, which play a key role in coastal biodiversity and ecosystem dynamics. However, research on meiofauna faces challenges due to limited taxonomic expertise and sparse sampling. We generated 775 new cytochrome c oxidase I DNA barcodes from meiofauna specimens collected along the Netherlands’ west coast and combined them with the NCBI GenBank database. We analysed alpha and beta diversity in 561 metabarcoding samples from 24 North Sea beaches, a region extensively studied for meiofauna, using both the enriched reference database and the NCBI database without the additional reference barcodes. Our results show a 2.5-fold increase in sequence annotation and a doubling of species-level Operational Taxonomic Units (OTUs) identification when annotating the metabarcoding data with the enhanced database. Additionally, our analyses revealed a bell-shaped curve of OTU richness across the intertidal zone, aligning more closely with morphological analysis patterns, and more defined community dissimilarity patterns between supralittoral and intertidal sites. Our research highlights the importance of expanding molecular reference databases and combining morphological taxonomy with molecular techniques for biodiversity assessments, ultimately improving our understanding of coastal ecosystems.