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).

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

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.

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.

Fish ear bones, known as otoliths, are often collected by fisheries to assist in management, and are a common sample type in museum and national archives. Beyond their utility for aging, morphological and trace element analysis, otoliths are a repository of valuable genomic information. Previous work has shown that DNA can be extracted from the trace quantities of tissue remaining on the surface of otoliths, despite the fact that they are often stored dry at room temperature. However, much of this work has used reduced-representation sequencing methods in clean lab conditions, to achieve adequate yields of DNA, libraries, and ultimately single-nucleotide polymorphisms (SNPs). Here, we pioneer the use of small-scale (spike-in) sequencing to screen otolith samples prepared in regular molecular biology laboratories for contamination and quality levels, submitting for whole-genome resequencing only samples above a defined endogenous DNA threshold. Despite the typically low quality and quantity of DNA extracted from otoliths, we are able to produce whole-genome libraries and ultimately sets of filtered, unlinked and even adaptive SNPs of ample size for downstream uses in population, climate, and conservation genomics. By comparing with a set of tissue samples from the same species, we are able to highlight the quality and efficacy of otolith DNA samples from DNA extraction and library preparation, to bioinformatic preprocessing and SNP calling. We provide detailed schematics, protocols, and scripts of our approach, such that it can be adopted widely by the community, improving the use of otoliths as a source of valuable genomic data.

Recent declines in insect abundances, especially populations of wild pollinators, pose a threat to many natural and agricultural ecosystems. Traditional species monitoring relies on morphological character identification and is inadequate for efficient and standardized surveys. DNA barcoding has become a standard approach for molecular identification of organisms, aiming to overcome the shortcomings of traditional species monitoring. However, its efficacy depends on the completeness of reference databases. Large DNA barcoding efforts are (almost entirely) lacking in many European countries, and such patchy data limit Europe-wide analyses of precisely how to apply DNA barcoding in wild bee identification. Here, we advance towards an effective molecular identification of European wild bees. We conducted a high-effort survey of wild bees in Slovenia, a country where central Europe meets the Balkan peninsula, and DNA barcoded all collected morpho-species. For global analyses, we complemented our DNA barcode dataset with all relevant European species and conducted a global analyses of species delimitation, general and genus-specific barcoding gaps, and examined the error-rate in DNA data repositories. We found that i) a sixth of all specimens from Slovenia could not be reliably identified, ii) species delimitation methods show numerous systematic problems, iii) there is no general barcoding gap across all bees, iv) the barcoding gap is genus-specific, but only after curating for errors in DNA data repositories. Intense sampling and barcoding efforts in underrepresented regions and strict curation of DNA barcode repositories are needed to enhance the use of DNA barcoding for identification of wild bees.

There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage paleogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1K to 50K) of shared SNPs (with minor allele frequency ≥0.01) are available. The performance of all four tools was comparable using ≥20K SNPs. We found that first-degree related pairs can be accurately classified even with 1K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 >90%) with 5K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69% and 79%, respectively). Meanwhile, noise in population allele frequencies and inbreeding (first cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low coverage genomes.

1. The use of environmental DNA (eDNA) as a new method of ecological monitoring is widely applied. Although eDNA can provide important information on the distribution and biomass of particular taxa, an organism’s DNA sequences remain unaltered throughout its existence, which complicates identifying crucial events, including reproduction, with high accuracy. We thus examined DNA methylation as a novel source of information from eDNA, considering that methylation patterns of eggs and sperm released during reproduction differ from those of somatic tissues. 2. Despite its potential applications, little is known about eDNA methylation, including its stability and methods for detection and quantification. Therefore, we conducted tank experiments and performed methylation analysis targeting 18S rDNA through bisulfite amplicon sequencing. 3. Methylation of eDNA was not affected by degradation and was equivalent to the rate of genomic DNA from somatic tissues. Unmethylated DNA, which is abundant in the ovary, was detected in eDNA during reproductive activity of fish. 4. These results indicate that eDNA methylation is a stable signal reflecting genomic methylation and demonstrate that germ cell-specific methylation patterns can be used as markers for detecting reproductive activity.

Chironomidae, so-called non-biting midges, are considered key bioindicators of aquatic ecosystem variability. Data derived from morphologically identifying their chitinous remains in sediments documents chironomid larvae assemblages, which are studied to reconstruct ecosystem changes over time. Recent developments in sedimentary DNA (sedDNA) research have demonstrated that molecular techniques are suitable for determining past and present occurrences of organisms. Nevertheless, sedDNA records documenting alterations in chironomid assemblages remain largely unexplored. To close this gap, we examined the applicability of sedDNA metabarcoding to identify Chironomidae assemblages in lake sediments by sampling and processing three 21 – 35 cm long sediment cores from Lake Sempach in Switzerland. With a focus on developing analytical approaches, we compared an invertebrate-universal (FWH) and a newly designed Chironomidae-specific metabarcoding primer set (CH) to assess their performance in detecting Chironomidae DNA. We isolated and identified chitinous larval remains and compared the morphotype assemblages with the data derived from sedDNA metabarcoding. Results showed a good overall agreement of the morphotype assemblage-specific clustering among the chitinous remains and the metabarcoding datasets. Both methods indicated higher chironomid assemblage similarity between the two littoral cores in contrast to the deep lake core. Moreover, we experienced a pronounced primer bias effect resulting in more Chironomidae detections with the CH primer combination compared to the FWH combination. Overall, we conclude that sedDNA metabarcoding can supplement traditional remain identifications and potentially provide independent reconstructions of past chironomid assemblage changes. Furthermore, it has the potential of more efficient workflows, better sample standardisation and species-level resolution datasets.

Heterozygosity is frequently used to describe variation in genetic diversity amongst populations and is often estimated using single nucleotide polymorphisms (SNPs). However, methods of calculating heterozygosity from SNPs have been shown to be affected study design and filtering parameters, reducing their utility and comparability across studies. Though solutions have been proposed to account for identified problems, in our own data, we continued to see inconsistent results. Here, we aimed to further improve methods of reducing inconsistency in these results, specifically by investigating how sample size and missing data thresholds influenced autosomal estimates of heterozygosity (heterozygosity calculated from across the genome, i.e., both fixed and variable sites). We also investigated how the exclusion of tri- and tetra-allelic sites, which is generally standard practice in such studies, could affect eventual estimates of heterozygosity. Across three distinct taxa (a frog, Litoria rubella; a tree, Eucalyptus microcarpa; and a grasshopper, Keyacris scurra) we found autosomal heterozygosity estimates to be affected by samples size when missing data is not allowed and show that this is partly due to the exclusion of tri- and tetra-allelic loci. We also show that the biases introduced by these factors are not consistent between species, or even populations, with higher levels of actual heterozygosity tending to result in larger adverse effects. We propose a modified framework for calculating heterozygosity to reduce these inherent issues and highlight the need for further development in methods such that tri- and tetra-allelic sites can be included in the calculation of population genomics statistics.