Molecular Ecology Resources最新文献

Differential gene expression (DGE) analysis enables researchers to investigate the link between gene expression and the phenotypic responses observed in organisms across time, experimental, or field conditions. Accurate quantification of gene expression is essential when performing DGE experiments, with a range of methods having been developed to enable the study of gene expression within a species. Quantifying differences in expression not just within but across multiple species can also be used to reveal the genetic mechanisms underlying phenotypic differences observed between species. Accurate quantification of gene expression across multiple species requires a suitable reference; it should include each species' own expressed transcripts to mitigate reference bias, with the orthology relationships of transcripts being used to facilitate comparison of expression at the gene level. Production of such a reference remains a challenge, despite its necessity for minimising bias during multispecies DGE analysis. Our software BINge specifically aims to address this need through use of a novel approach to modelling orthology which results in multispecies transcript clusters that accurately reflect their locus orthology. Evaluation experiments demonstrate the effectiveness of this approach over existing clustering methods which have not been designed for producing a reference suitable for multispecies DGE analysis. Source code and documentation for BINge are available from the GitHub repository at https://github.com/zkstewart/BINge.

Many useful quantities in conservation genomics are point estimates that summarise genetic variation at the population or species level. This presents a challenge to the application of null hypothesis testing frameworks, which require some measure of uncertainty to test the authenticity of measured group differences. The jackknife is a convenient statistical resampling tool which sees regular use to this end: producing confidence intervals for point estimates. Unfortunately, the slightly obscure nature of the jackknife coupled with its superficial resemblance to the bootstrap has led to its misuse, specifically in the analysis of R_XY statistics, a recently formulated quantity designed to measure relative mutation load. In this review, I summarise the application of the jackknife as a method to produce confidence intervals and review its uses in the conservation/population genomics literature. I outline how it is similar to and different from the bootstrap, and how the treatment of jackknife pseudo-values as bootstrap pseudo-values produces inflated confidence in otherwise negligible group differences. I then provide suggestions for the future use of the jackknife in conservation genomics.

Advancements in historical genomics increasingly leverage museum collections to study past ecosystems, species interactions and biodiversity. Formalin-fixed, ethanol-preserved specimens, once thought inaccessible to molecular analyses due to DNA degradation, are emerging as valuable genomic resources. If recoverable and reliably attributable, DNA within preservation media could provide a non-destructive alternative to conventional tissue sampling, with the potential to expand molecular access to valuable or irreplaceable specimens. We tested whether preservation media contains recoverable DNA suitable for taxonomic inference. We coupled passive adsorption and active filtration of specimen media with hot alkaline lysis DNA extraction followed by metabarcoding and shotgun metagenomics. DNA was recoverable across samples, including 41 of 61 (~67%) targets in a composite sample. However, detections were dominated by non-target taxa, indicating that preservation media retain a layered mixture of specimen-derived DNA and broader collection-level background. Detection success tracked with preservation chemistry (near-neutral pH and low residual formaldehyde) rather than specimen age. Method choice influenced detections: active filtration increased target detections but admitted more background; passive capture was sparser but more selective; shotgun sequencing retrieved broader vertebrate signals, including reptiles, but was heavily enriched for non-targets. Because both target and non-target taxa were often abundant, read-abundance cut-offs were unreliable for attribution. Spirit-media DNA is therefore best interpreted as a collection-level signal and a screening tool to identify jars with molecular potential (e.g., taxa of conservation or biosecurity interest), rather than as a definitive non-destructive proxy for specimen identity. Prioritising chemically favourable jars and implementing rigorous contamination controls should improve signal interpretability and help unlock the value of preservation media for historical genomics.

Metabarcoding sequence data from environmental DNA (eDNA) is rapidly expanding as a powerful method for biodiversity surveys. In order to interpret these data, tools are needed that account for the uncertainty associated with eDNA sampling, sequencing and analysis. The data resulting from eDNA marker gene analysis differ from many traditional methods of biodiversity surveys because they are highly complex, sparse and compositional. Methodological biases produce uncertainty at every step of the sampling and sequencing process. Thus, it is critical that users have a way of interpreting eDNA results that accounts for their compositional nature and models the uncertainty resulting from factors like patchy sampling, PCR amplification biases and variable sequencing depth. Here, we introduce MAMBO: Metabarcoding Analysis using Modeled Bayesian Occurrences. MAMBO simulates in silico replication and models the uncertainty surrounding the sequencing and analysis process. Further, it uses these modelled sequence count data to correlate two sets of marker genes with a Bayesian regression, facilitating the linkage of different groups targeted by these assays. Compared with correlational network analyses, MAMBO overcomes many of the limitations to robust statistical analyses of eDNA marker gene data and provides an opportunity for new insight into ecological patterns over space and time.

Spiders are renowned for their ecological versatility and silk-based innovations in materials science, yet marine environments remain virtually uncolonized by this predominantly terrestrial lineage. A striking exception is the obligate intertidal spider genus Desis, whose members have evolved extraordinary physiological and behavioural adaptations to persist in wave-swept, saline habitats that oscillate between land and sea. However, the molecular basis of these adaptations has remained largely unexplored. Here, we present a high-quality, chromosome-scale genome of the intertidal spider Desis jiaxiangi, together with a reference genome of the water spider Argyroneta aquatica, integrated with transcriptomic and proteomic data. This multi-omics framework reveals the genomic architecture underlying adaptation to life at the ocean's edge. We uncover expansions of gene families linked to hormone biosynthesis and DNA repair, alongside signatures of adaptive evolution in genes involved osmoregulation, the rate-limiting step of glycolysis, mitochondrial regulation, epithelial tube morphogenesis and circadian rhythm. Notably, we characterize a novel silk spidroin enriched with a unique GVGAKV motif, which may enhance silk hydrophobicity, and detect the duplication burst of hemocyanin genes likely supporting oxygen transport during submersion. Together, these findings reveal convergent molecular strategies for coping with extreme and fluctuating environments, and demonstrate how genomic innovation enables terrestrial lineages to invade marine-influenced ecosystems. Our study establishes Desis as powerful model for understanding adaptation at terrestrial-marine interface.