Biology subjects

Vogler, A. P.

Publications and source records attributed to Vogler, A. P..

7 recordsLinked to original sources

bioRxiv2024

Bioinformatics of combined nuclear and mitochondrial phylogenomics to define key nodes for the classification of Coleoptera

Nuclear genome sequencing is resource-intensive and not practical for building densely sampled phylogenetic trees of the most species rich lineages of animals, while mitochondrial genomes can be sequenced and analysed with relative ease. Here, we develop a conceptual approach and bioinformatics workflow for combining nuclear single-copy orthologs with less informative but densely sampled mitochondrial genomes, for a detailed tree of Coleoptera (beetles). Basal relationships of Coleoptera were first inferred from >2,000 BUSCO loci mined from GenBanks Short Read Archive for 119 exemplars of all major lineages under various substitution models and levels of matrix completion, to reveal universally supported nodes. Second, the corresponding mitogenomes were extracted and combined with an additional 373 species selected for broad taxonomic and biogeographic coverage, roughly in proportion to the known global species diversity of Coleoptera. Bioinformatic processing of mitogenomes was conducted with a novel pipeline for rapid, accurate annotation of protein-coding genes. Finally, phylogenetic trees from all 492 mitogenomes were generated under a backbone constraint from the universal basal nodes, which produced a well-supported tree of the major lineages at family and superfamily level. Being genetically unlinked and showing unique character variation, mitogenomes provide a unique perspective of the phylogeny. Comparison with three recent nuclear phylogenomic studies resulted in the recognition of >80 nodes universally present across all analyses. These may now support the higher classification of Coleoptera and serve as backbone of further studies, as numerous full mitogenomes and mitochondrial DNA barcodes are added to an increasingly complete phylogenetic tree of this super-diverse insect order.

evolutionary biology↗

bioRxiv2021

Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation

Complex bulk samples of invertebrates from biodiversity surveys present a great challenge for taxonomic identification, especially if obtained from unexplored ecosystems. High-throughput imaging combined with machine learning for rapid classification could overcome this bottleneck. Developing such procedures requires that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. Yet the feasibility of transfer learning for the classification of unknown samples remains to be tested. Here, we assess the efficiency of deep learning and domain transfer algorithms for family-level classification of below-ground bulk samples of Coleoptera from understudied forests of Cyprus. We trained neural network models with images from local surveys versus global databases of above-ground samples from tropical forests and evaluated how prediction accuracy was affected by: (a) the quality and resolution of images, (b) the size and complexity of the training set and (c) the transferability of identifications across very disparate source-target pairs that do not share any species or genera. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions was reduced to a maximum of 82% and depended greatly on the standardisation of the imaging procedure. When the source and target images were of similar quality and resolution, albeit from different faunas, the reduction of accuracy was minimal. Application of algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, when the imaging conditions and classification algorithms are carefully considered. Also, our results provide guidelines for data acquisition and algorithmic development for high-throughput image-based biodiversity surveys.

zoology↗

bioRxiv2021

Quantitative prey species detection in predator guts across multiple trophic levels by DNA shotgun sequencing

O_LIMetabarcoding has revolutionized the study of ecological communities, but PCR bias hampers quantitative analyses, as required in studies of trophic interactions. Direct DNA shotgun sequencing can avoid the amplification step when unassembled reads are mapped to a reference database, enabling identification and quantification of prey items. C_LIO_LITwo feeding bioassays tested the precision and accuracy of quantitative assessments with the coccinellids Harmonia axyridis and Hippodamia convergens, the chrysopid Chrysoperla externa, and the aphid Myzus persicae. Guts were dissected and the total DNA extracted was directly sent to sequence by Illumina HiSeq2500 (insert 350 bp, PE 250). Predator gut content reads were blasted against an arthropod mitochondrial DNA reference database for taxonomic assignment and the matches curated for false positive prey identification using a series of bioinformatics pipelines, mostly in R. Taxonomic assignment through KrakenUnique, which is based on the counts of unique k-mers, was compared. C_LIO_LIIn a Prey Quantity bioassay, the number of prey reads was correlated to the amount of prey consumed and the elapsed time since consumption. In a Direct and Indirect Predation bioassay, prey was detectable in the predator 6 h after feeding (direct predation) and the preys prior prey was detectable 3 h after feeding (indirect predation). Detection of indirect predation was related to the species-specific decay rates of the two predators, but not to the order of predation. C_LIO_LIWe demonstrate that degraded prey DNA was quantifiable across trophic levels with high accuracy (98.4% positive predictive value) and taxonomic resolution. C_LI

ecology↗

bioRxiv2020

The limited spatial scale of dispersal in soil arthropods revealed with whole-community haplotype-level metabarcoding

Soil mesofauna communities are hyperdiverse and critical for ecosystem functioning. However, our knowledge on spatial structure and underlying processes of community assembly for soil arthropods is scarce, hampered by limited empirical data on species diversity and turnover. We implement a high-throughput-sequencing approach to generate comparative data for thousands of arthropods at three hierarchical levels: genetic, species and supra-specific lineages. A joint analysis of the spatial arrangement across these levels can reveal the predominant processes driving the variation in biological assemblages at the local scale. This multi-hierarchical approach was performed using haplotype-level-COI metabarcoding of entire communities of mites, springtails and beetles from three Iberian mountain regions. Tens of thousands of specimens were extracted from deep and superficial soil layers and produced comparative phylogeographic data for >1000 co-distributed species and nearly 3000 haplotypes. Local assemblages were highly distinctive between grasslands and forests, and within each of them showed strong spatial structures and high endemicity at the scale of a few kilometres or less. The local distance-decay patterns were self-similar for the haplotypes and higher hierarchical entities, and this fractal structure was very similar in all three regions, pointing to a significant role of dispersal limitation driving the local-scale community assembly. Our results from whole-community metabarcoding provide unprecedented insight into how dispersal limitations constrain mesofauna community structure within local spatial settings over evolutionary timescales. If generalized across wider areas, the high turnover and endemicity in the soil locally may indicate extremely high richness globally, challenging our current estimations of total arthropod-diversity on Earth.

ecology↗

bioRxiv2020

NUMT dumping: validated removal of nuclear pseudogenes from mitochondrial metabarcode data

Metabarcoding of Metazoa using mitochondrial genes may be confounded by both the accumulation of PCR and sequencing artefacts and the co-amplification of nuclear mitochondrial pseudogenes (NUMTs). The application of read abundance thresholds and denoising methods is efficient in reducing noise accompanying authentic mitochondrial amplicon sequence variants (ASVs). However, these procedures do not fully account for the complex nature of concomitant sequences and the highly variable DNA contribution of individuals in a metabarcoding sample. We propose, as a complement to denoising, the metabarcoding Multidimensional Abundance Threshold Evaluation (metaMATE) framework, a novel approach that allows comprehensive examination of multiple dimensions of abundance filtering and the evaluation of the prevalence of unwanted concomitant sequences in denoised metabarcoding datasets. metaMATE requires a denoised set of ASVs as input, and designates a subset of ASVs as being either authentic (mtDNA haplotypes) or non-authentic ASVs (NUMTs and erroneous sequences) by comparison to external reference data and by analysing nucleotide substitution patterns. metaMATE (i) facilitates the application of read abundance filtering strategies, which are structured with regard to sequence library and phylogeny and applied for a range of increasing abundance threshold values, and (ii) evaluates their performance by quantifying the prevalence of non-authentic ASVs and the collateral effects on the removal of authentic ASVs. The output from metaMATE facilitates decision-making about required filtering stringency and can be used to improve the reliability of intraspecific genetic information derived from metabarcode data. The framework is implemented in the metaMATE software, available at https://github.com/tjcreedy/metamate).

evolutionary biology↗

bioRxiv2019

A validated workflow for rapid taxonomic assignment and monitoring of a national fauna of bees (Apiformes) using high throughput barcoding

Improved taxonomic methods are needed to quantify declining populations of insect pollinators. This study devises a high-throughput DNA barcoding protocol for a regional fauna (United Kingdom) of bees (Apiformes), consisting of reference library construction, a proof-of-concept monitoring scheme, and the deep barcoding of individuals to assess potential artefacts and organismal associations. A reference database of Cytochrome Oxidase subunit 1 (cox1) sequences including 92.4% of 278 bee species known from the UK showed high congruence with morphological taxon concepts, but molecular species delimitations resulted in numerous split and (fewer) lumped entities within the Linnaean species. Double tagging permitted deep illumina sequencing of 762 separate individuals of bees from a UK-wide survey. Extracting the target barcode from the amplicon mix required a new protocol employing read abundance and phylogenetic position, which revealed 180 molecular entities of Apiformes identifiable to species. An additional 72 entities were ascribed to mitochondrial pseudogenes based on patterns of read abundance and phylogenetic relatedness to the reference set. Clustering of reads revealed a range of secondary Operational Taxonomic Units (OTUs) in almost all samples, resulting from traces of insect species caught in the same traps, organisms associated with the insects including a known mite parasite of bees, and the common detection of human DNA, besides evidence for low-level cross-contamination in pan traps and laboratory steps. Custom scripts were generated to conduct critical steps of the bioinformatics protocol. The resources built here will greatly aid DNA-based monitoring to inform management and conservation policies for the protection of pollinators.

molecular biology↗

bioRxiv2018

The contribution of mitochondrial metagenomics to largescale data mining and phylogenetic analysis of Coleoptera

A phylogenetic tree at the species level is still far off for highly diverse insect orders, including the Coleoptera, but the taxonomic breadth of public sequence databases is growing. In addition, new types of data may contribute to increasing taxon coverage, such as metagenomic shotgun sequencing for assembly of mitogenomes from bulk specimen samples. The current study explores the application of these techniques for large-scale efforts to build the tree of Coleoptera. We used shotgun data from 17 different ecological and taxonomic datasets (5 unpublished) to assemble a total of 1942 mitogenome contigs of >3000 bp. These sequences were combined into a single dataset together with all mitochondrial data available at GenBank, in addition to nuclear markers widely used in molecular phylogenetics. The resulting matrix of nearly 16000 species with two or more loci produced trees (RAxML) showing overall congruence with the Linnaean taxonomy at hierarchical levels from suborders to genera. We tested the role of full-length mitogenomes in stabilizing the tree from GenBank data, as mitogenomes might link terminals with non-overlapping gene representation. However, the mitogenome data were only partly useful in this respect, presumably because of the purely automated approach to assembly and gene delimitation, but improvements in future may be possible by using multiple assemblers and manual curation. In conclusion, the combination of data mining and metagenomic sequencing of bulk samples provided the largest phylogenetic tree of Coleoptera to date, which represents a summary of existing phylogenetic knowledge and a defensible tree of great utility, in particular for studies at the intra-familial level, despite some shortcomings for resolving basal nodes.

evolutionary biology↗