Molecular Ecology Resources最新文献

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

Spiders are a hyperdiverse taxon and among the most abundant predators in nearly all terrestrial habitats. Their success is often attributed to key developments in their evolution such as silk and venom production and major apomorphies such as a whole-genome duplication. Resolving deep relationships within the spider tree of life has been historically challenging, making it difficult to measure the relative importance of these novelties for spider evolution. Whole-genome data offer an essential resource in these efforts, but also for functional genomic studies. Here, we present de novo assemblies for three spider species: Ryuthela nishihirai (Liphistiidae), a representative of the ancient Mesothelae, the suborder that is sister to all other extant spiders; Uloborus plumipes (Uloboridae), a cribellate orbweaver whose phylogenetic placement is especially challenging; and Cheiracanthium punctorium (Cheiracanthiidae), which represents only the second family to be sequenced in the hyperdiverse Dionycha clade. These genomes fill critical gaps in the spider tree of life. Using these novel genomes along with 25 previously published ones, we examine the evolutionary history of spidroin gene and structural hox cluster diversity. Our assemblies provide critical genomic resources to facilitate deeper investigations into spider evolution. The near chromosome-level genome of the 'living fossil' R. nishihirai represents an especially important step forward, offering new insights into the origins of spider traits.

Current rDNA reference sequence databases are tailored towards shorter DNA markers, such as parts of the 16/18S marker or the internally transcribed spacer (ITS) region. However, due to advances in long-read DNA sequencing technologies, longer stretches of the rDNA operon are increasingly used in environmental sequencing studies to increase the phylogenetic resolution. There is, therefore, a growing need for longer rDNA reference sequences. Here, we present the ribosomal operon database (ROD), which includes eukaryotic full-length rDNA operons fished from publicly available genome assemblies. Full-length operons were detected in 34.1% of the 34,701 examined eukaryotic genome assemblies from NCBI. In most cases (53.1%), more than one operon variant was detected, which can be due to intragenomic operon copy variability, allelic variation in non-haploid genomes, or technical errors from the sequencing and assembly process. The highest copy number found was 5947 in Zea mays. In total, 453,697 unique operons were detected, with 69,480 operon variant clusters remaining after intragenomic clustering at 99% sequence identity. The operon length varied extensively across eukaryotes, ranging from 4136 to 16,463 bp, which will lead to considerable polymerase chain reaction (PCR) bias during amplification of the entire operon. Clustering the full-length operons revealed that the different parts (i.e., 18S, 28S, and the hypervariable regions V4 and V9 of 18S) provide divergent taxonomic resolution, with 18S, the V4 and V9 regions being the most conserved. The ROD will be updated regularly to provide an increasing number of full-length rDNA operons to the scientific community.

One essential initial step in the analysis of ancient DNA is to authenticate that the DNA sequencing reads are actually from ancient DNA. This is done by assessing if the reads exhibit typical characteristics of post-mortem damage (PMD), including cytosine deamination and nicks. We present a novel statistical method implemented in a fast multithreaded programme, ngsBriggs that enables rapid quantification of PMD by estimation of the Briggs ancient damage model parameters (Briggs parameters). 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 ngsBriggs 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.

Chironomidae, so-called non-biting midges, are considered key bioindicators of aquatic ecosystem variability. Data derived from morphologically identifying their chitinous remains in sediments document 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 observed 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.

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

It is a global priority to better manage the biosphere, but action must be informed by comprehensive data on the abundance and distribution of species. The acquisition of such information is currently constrained by high costs. DNA barcoding can speed the registration of unknown animal species, the most diverse kingdom of eukaryotes, as the BIN system automates their recognition. However, inexpensive sequencing protocols are critical as the census of all animal species is likely to require the analysis of a billion or more specimens. Barcoding involves DNA extraction followed by PCR and sequencing with the last step dominating costs until 2017. By enabling the sequencing of highly multiplexed samples, the Sequel platforms from Pacific BioSciences slashed costs by 90%, but these instruments are only deployed in core facilities because of their expense. Sequencers from Oxford Nanopore Technologies provide an escape from high capital and service costs, but their low sequence fidelity has, until recently, constrained adoption. However, the improved performance of its latest flow cells (R10.4.1) erases this barrier. This study demonstrates that a MinION flow cell can characterise an amplicon pool derived from 100,000 specimens while a Flongle flow cell can process one derived from several thousand. At $0.01 per specimen, DNA sequencing is now the least expensive step in the barcode workflow.

Eukaryotic genomes harbour sequences derived from non-retroviral RNA viruses, known as endogenous viral elements (EVEs) or non-retroviral integrated RNA virus sequences (NIRVS). These sequences represent a record of past infections and have been implicated in host anti-viral response. We have created a program to identify viral sequences integrated in a host genome. It begins with a specimen BAM file and outputs candidate NIRVS, along with putative host insertion sites and overlapping genomic features of the host genome in XML and visual formats, with minimal intermediary intervention. We ran through this software short-read data derived from the genomes of 222 wild-caught A. aegypti mosquitoes, from a dozen geographical regions, and located putative NIRVS from seven virus families. This program is as accurate as currently available software for NIRVS detection, and represents a significant improvement in adaptability and user-friendliness. Furthermore, the flexibility of this pipeline allows the user to search for sequence integrations across the genome of any organism, as long as a query sequence database and a reference genome is provided. Potential extended applications include identification of integrated transgenic sequences used for research or vector control strategies.