Molecular Ecology Resources最新文献

Identification of conserved genomic sequences and their utilisation as anchor points for clade detection and/or characterisation is a mainstay in ecological studies. For environmental DNA (eDNA) assays, effective processing of large genomic datasets is crucial for reliable species detection in biodiversity monitoring. While considerable focus has been on developing robust species-targeted assays, eDNA assays with broader taxonomic coverage (e.g., detecting any species within a taxonomic group such as fish), can significantly streamline environmental monitoring, especially when detecting individual species' DNA proves challenging. Designing such assays requires identifying conserved regions representing the target taxonomic group, a chiefly manual task that is often labor-intensive and error-prone, particularly when working with large sequence datasets. To address these challenges, we present unikseq2, an enhanced, alignment-free, k-mer-based tool for identifying unique and conserved sequences. It introduces a new functionality to identify sequence conservation among target species, enabling more informed marker selection for applications such as universal primer design. This automates sequence selection in large-scale mitochondrial genome datasets eliminating the need for manual inspection of computationally costly multiple sequence alignments. Herein, we demonstrate unikseq2's capabilities by developing and validating eDNA assays for various taxa, including Osteichthyes (bony fishes), the Salmonidae family (salmon and trout), Myotis bats and Cervus deer. Unikseq2-based eDNA assays allow for accurate detection across multiple taxonomic levels, from genus to class, enhancing the flexibility, scalability and reliability of eDNA tools in environmental monitoring. By leveraging genomic data from public repositories, unikseq2 supports efficient, reproducible assay design, making it an invaluable tool for a wide range of ecological and biodiversity research applications.

High-grading bias is the overestimation power in a subset of loci caused by model overfitting. Using both empirical and simulated datasets, we show that high-grading bias can cause severe overestimation of population structure, and thus mislead investigators, whenever highly informative or high-F_ST markers are chosen (i.e., ascertained) and used for subsequent assessments, a common practice in population genetic studies. This problem can occur in panmictic populations with no local adaptation. Biased results from choosing high-F_ST markers may have severe downstream implications for management and conservation, such as erroneous conservation unit delineation, which could squander limited conservation resources to protect incorrectly defined ‘populations’. Furthermore, we caution that high-grading is not limited to F_ST approaches; high-grading bias is a concern whenever a small subset of markers are first chosen to explain differences among groups based on their degree of difference and are subsequently reused to estimate the degree of difference among those groups. For example, selecting high F_ST loci for use in a GT-seq panel or using differentially expressed genes to plot sample membership in multivariate space can both result in spurious structure when none exists. We illustrate that using statistically based outlier tests in place of arbitrary F_ST cut-offs can reduce bias. Alternatively, permutation tests or cross-evaluation can be used to detect high-grading bias. We provide an R package, PCAssess, to help researchers detect and prevent high-grading bias in genetic datasets by automating permutation tests and principal component analyses (https://github.com/hemstrow/PCAssess).

New approaches are urgently needed to enrich rare or low-abundant DNA in complex samples. Soil-transmitted helminths (STHs) inhabit heterogeneous environments, including the gastrointestinal tract of their host as adults and are excreted as eggs and larvae in faeces, complicating our understanding of their biology and the use of genetic tools for species monitoring and population tracking. We have developed a hybridisation capture approach to enrich mitochondrial genome sequences of two STH species, the roundworm Ascaris lumbricoides and whipworm Trichuris trichiura, from extracted DNA from faecal material and worm specimens. Employing ~1000 targeted probes, we achieved > 6000 and > 12,000 fold enrichment for A. lumbricoides and T. trichiura, respectively, relative to direct whole genome shotgun (WGS) sequencing. Sequencing coverage was highly concordant with probe targets and correlated with the number of eggs per gram (EPG) of parasites present, from which DNA from as few as 336 EPG for Ascaris and 48 EPG for Trichuris were efficiently captured and sufficient to provide effective mitochondrial genome data. Finally, allele frequencies were highly concordant between WGS and hybridisation capture, suggesting little genetic information is lost with additional sample processing required for enrichment. Our hybridisation capture design and approach enable sensitive and flexible STH mitochondrial genome sampling from faecal DNA extracts and pave the way for broader hybridisation capture-based genome-wide applications and molecular epidemiology studies of STHs.

Ongoing advances in population genomic methodologies have recently enabled the study of millions of loci across hundreds of genomes at a relatively low cost, by leveraging a combination of low-coverage shotgun sequencing and innovative genotype imputation methods. This approach has the potential to provide abundant genotype information at low costs comparable to another widely used cost-effective genotyping approach—that is, SNP panels—while avoiding potential issues related to loci being ascertained in distantly related populations. Nonetheless, the wide adoption of imputation methods in humans and other species is currently constrained by the lack of publicly available reference panels that capture diversity representative of the target genomes—though the recent development of ‘joint’ imputation approaches, which allow genetic information from the target population to be used in genotype calling, may potentially mitigate this shortcoming. Here, we assess the performance of multiple genotyping approaches on eight low coverage genomes (range ~3× to ~5×) sourced from different Indonesian populations—including a joint imputation approach that leverages 248 additional low coverage genomes (mean ~2.4×) from related populations. The inclusion of these related genomes in the joint imputation process resulted in more accurate genotype calls and produced population genetic inferences with similar accuracy but improved precision compared to pseudohaploid calls—even though the reference panel was only weakly representative of the target genomes. These results highlight the enormous potential of joint imputation to enable economical population genetic research for taxa that are currently poorly represented in publicly available reference panels.

Identifying environmental gradients driving genetic adaptation is one of the major goals of ecological genomics. We present RDAforest, a methodology that leverages the predominantly polygenic nature of adaptation and harnesses the versatility of random forest regression to solve this problem. Instead of computing individual SNP-environment associations, RDAforest seeks to explain the overall genetic covariance structure based on multiple environmental predictors. By relying on random forest instead of linear regression, this method can detect non-linear and non-monotonous dependencies as well as all possible interactions between predictors. It also incorporates a novel procedure to select the best predictor out of several correlated ones, and uses jackknifing to model uncertainty of genetic structure determination. Lastly, our methodology incorporates delineation and plotting of “adaptive neighbourhoods”—areas on the landscape that are predicted to harbour differentially adapted individuals. Such maps can be used as a guide for planning conservation and ecological restoration efforts. We demonstrate the use of RDAforest in two simulated scenarios and one real dataset (North American grey wolves).

Technological advances are producing genome assemblies of increasing quality at steadily decreasing costs. These assemblies enable the extraction of rich biological information from previously inaccessible genomic regions (e.g., repeat-rich regions) and from diverse organisms underrepresented in genomic research. Gaining functional insights from new assemblies often requires generating additional data sets, experimental approaches and complex analysis. Novel analytical methods that substantially shorten the path to biological insights are valuable, particularly if they draw conclusions from the direct analysis of assemblies. In this issue of Molecular Ecology Resources, Elphinstone et al. (2025) present RepeatOBserver—a tool to visualise repeat organisation through direct analysis of chromosome-scale assemblies. This tool facilitates the summary and visualisation of large- and fine-scale patterns of repetitive DNA sequence structure across assemblies. Their approach borrows metrics from information theory, which have found uses in ecology (i.e., the Shannon Diversity Index), to help infer functional regions within repetitive sequences including putative centromeres. Importantly, RepeatOBserver does not require annotations, repeat libraries or functional genomic data—just a high-quality assembly. This type of tool addresses ongoing challenges in mapping the structure and functions of repeat-rich chromosomal regions, which remain the least well-understood components of genomes.

The availability of chromosome-scale genome assemblies is growing rapidly, as advances in long-read sequencing technology make assembly-based approaches accessible to more taxa. These genome assemblies can reveal important insights into genome biology, biomedicine and biodiversity. Our ability to extract these insights from assemblies is built on decades of hard-won work in early genomic model organisms. For example, early work on gene structure, regulation and evolution provided a knowledge base for ab initio gene prediction from nothing more than a DNA sequence. While annotation tools for non-coding sequences like the abundant repetitive DNAs found in most eukaryotic genomes are now accessible, the methods to extract insights from these regions are less mature. Repetitive DNAs evolve rapidly: their composition, organisation and abundance varies across species (Yunis and Yasmineh 1971), making predictions based on sequence conservation difficult. Many insights require functional genomic data (e.g., ChIP-seq, methylation and ATAC-seq), which may be challenging to access in non-model systems. Despite recent progress in resolving repeats in chromosome-scale assemblies, their assembly and annotation remain non-trivial problems (Lower et al. 2018).

Some genome regions with critical functions are enriched in, or entirely composed of, repeated DNA sequences. Centromeres—the essential structures that guide chromosome segr