Biology subjects

Leese, F.

Publications and source records attributed to Leese, F..

27 records · Page 2Linked to original sources

bioRxiv2020

TaxonTableTools - A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data

DNA metabarcoding is increasingly used in research and application to assess biodiversity. Powerful analysis software exists to process raw data. However, when it comes to the translation of sequence read data into biological information many end users with limited bioinformatic expertise struggle with the downstream analysis and explore data only to a minor extent. Thus, there is a growing need for easy-to-use, graphical user interface (GUI) analysis software to analyse and visualise DNA metabarcoding data. We here present TaxonTableTools (TTT), a new platform independent GUI software that aims to fill this gap by providing simple and reproducible analysis and visualisation workflows. TTT uses a so-called "TaXon table" as input. This format can easily be generated within TTT from two input files: a read table and a taxonomy table that can be obtained by various published metabarcoding pipelines. TTT analysis and visualisation modules include e.g. Venn diagrams to compare taxon overlap among replicates, samples or among different analysis methods. It analyses and visualises basic statistics such as read proportion per taxon as well as more sophisticated visualisation such as interactive Krona charts for taxonomic data exploration. Various ecological analyses such as alpha or beta diversity estimates, and rarefaction analysis ordination plots can be produced directly. Data can be explored also in formats required by traditional taxonomy-based analyses of regulatory bioassessment programs. TTT comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) and the Python package index (https://pypi.org/project/taxontabletools/).

bioinformatics↗

bioRxiv2020

Evaluating geographic variation within molecular operational taxonomic units (OTUs) using network analyses in Scandinavian lakes

Operational taxonomic units (OTUs) are usually treated as if they are internally uniform in environmental metabarcoding studies of microbial and macrobial eukaryotes, even when the OTUs are being used to infer biogeographic patterns. The OTUs constructed by the program Swarm have underlying network topologies in which nodes represent amplicons and edges represent 1 nucleotide differences between nodes. Such networks can be exploited to search for biogeographic patterns within each OTU. To do this, here we used an available protistan metabarcoding dataset consisting of the hypervariable V4 region of the 18S rRNA locus amplified from 77 lakes collected across Norway and Sweden. The 82 most abundant and wide-spread OTUs constructed by Swarm were evaluated using shortest path, assortativity, and geographical analyses. We found that while pairs of amplicons from the same lake were usually connected directly to each other within the OTUs, these pairs of amplicons from the same lake did not form assortative clusters within the OTUs, and amplicons were not more connected with other amplicons occurring in neighboring lakes than expected by chance. This new approach to looking at within-OTU is applicable to other metabarcoding datasets and we provide code to perform these analyses.

microbiology↗

bioRxiv2020

Deep learning and computer vision will transform entomology

ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statement Insect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.Competing Interest StatementThe authors have declared no competing interest.GlossaryBin pickingan industrial term for robots that pick up one of many objects randomly placed in a container.Convolutional Neural Network (CNN)a deep learning algorithm in the family of neural networks with serval different layers commonly applied for image recognition and classification. A CNN can be trained to recognize various objects and patterns in an image. There are four main different operations in a CNN: convolution, activation functions, sub sampling, and fully connected layer. During training the learnable parameters of each convolutional and fully connected layer are adjusted so the CNN is able to recognize different patterns of the training data and used for final image classification.Data augmentationa technique that can be used to artificially expand the size of a training dataset by creating modified images with objects of interest for classification.Machine learninga subset of artificial intelligence associated with creating algorithms that can change themselves without human intervention to get the desired result – by feeding themselves through structured data.Deep learninga subset of machine learning where algorithms are created and function similarly to machine learning, but where there are many levels of these algorithms, each providing a different interpretation of the data it conveys.DNA barcodingIdentification of a species using a short, standardised gene fragment.Initializationdescription of an object to be tracked.Training dataclassified images (e.g. images of known species identified by experts) that are recorded to train a deep learning model.Precisionthe number of true positives divided by the sum of true positives and false positivesRecallalso called the true positive rate, is the number of true positives divided by the sum of true positives and false negatives.Classification accuracythe sum of true positives and true negatives divided by the total number of specimens.View Full Text

ecology↗

bioRxiv2020

Improved freshwater macroinvertebrate detection from eDNA through minimized non-target amplification

DNA metabarcoding of freshwater communities typically relies on PCR amplification of a fragment of the mitochondrial cytochrome c oxidase (COI) gene with degenerate primers. The advantage of COI is its taxonomic resolution and the availability of an extensive reference database. However, when universal primers are used on environmental DNA (eDNA) isolated from stream water, macroinvertebrate read and OTU numbers are typically "watered down", i.e. diluted, compared to whole specimen bulk samples due to greater co-amplification of abundant non-target taxa such as algae and bacteria. Because stream macroinvertebrate taxa are of prime importance for regulatory biomonitoring, more effective ways to capture their diversity via eDNA isolated from water are important. In this study, we aimed to improve macroinvertebrate assessment from eDNA by minimizing non-target amplification. Therefore, we generated data using universal primers BF2/BR2 throughout 15 months from a German Long-Term Ecological Research (LTER) site, the River Kinzig, to identify most abundant non-target taxa. Based on these data, we designed a new reverse primer (EPTDr2n) with 3-specificity towards macrozoobenthic taxa and validated its specificity in silico together with universal forward primer fwhF2 using available data from GenBank and BOLD. We then performed in vitro tests using 20 eDNA samples taken in the Kinzig catchment. We found that the percentage of target reads was much higher for the new primer combination compared to two universal macrozoobenthic primer pairs, BF2/BR2 and fwhF2/fwhR2n (>99 % vs. 21.4 % and 41.25 %, respectively). Likewise, number of detected macroinvertebrate taxa was substantially higher (351 vs. 46 and 170, respectively) and exceeded the number of 257 taxa identified by expert taxonomists at nearby sites across two decades of sampling. While few taxa such as Turbellaria were not detected, we show that the optimized primer avoids the dilution problem and thus significantly improves macroinvertebrate detection for bioassessment and -monitoring.

ecology↗

bioRxiv2020

Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos

Genetic diversity is the most basal level of biodiversity and determines the evolutionary capacity of species to adapt to changing environments, yet it is typically neglected in routine biomonitoring and stressor impact assessment. For a comprehensive analysis of stressor impacts on genetic diversity, it is necessary to assess genetic variants simultaneously in many individuals and species. Such an assessment is not as straight-forward and usually limited to one or few individual species. However, nowadays species diversity can be assessed by analysing thousands of individuals of a community simultaneously with DNA metabarcoding. Recent bioinformatic advances also allow for the extraction of exact sequence variants (ESVs or haplotypes) in addition to Operational Taxonomic Units (OTUs). By using this new capability, we here evaluated if the analysis of mitochondrial genetic diversity in addition to species diversity can provide insights into responses of stream macrozoobenthic communities to environmental stressors. For this purpose, we analysed macroinvertebrate bulk samples of three German river systems with different stressor levels using DNA metabarcoding. While OTU and haplotype number were negatively correlated with stressor impact, this association was not as clear when looking at haplotype diversity. Here, stressor responses were only found for sensitive EPT (Ephemeroptera, Plecoptera, Trichoptera) taxa, and those exceedingly resistant to organic stress. An increase in haplotype number per OTU and haplotype diversity of sensitive taxa was observed with an increase in ecosystem quality and stability, while the opposite pattern was detected for pollution resistant taxa. However, this pattern was less prominent than expected based on the strong differences in stressor intensity between sites. To compare genetic diversity among river systems, only OTUs could be used, which were present in all systems. As OTU composition differed strongly between the rivers, this led to the exclusion of a high number of OTUs, especially in diverse river systems of good quality, which potentially diminished the genetic diversity patterns. To better understand responses of genetic diversity to environmental stressors for example in river ecosystems, it would be important to increase OTU overlap between sites of comparisons, e.g. by sampling a narrower stressor gradient, and to perform calibrated studies controlling for the number and individual genotypes. However, this pioneer study shows that the extraction of haplotypes from DNA metabarcoding datasets is a promising tool to simultaneously assess mitochondrial genetic diversity changes in response to environmental impacts for a metacommunity.

molecular biology↗

bioRxiv2019

DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work

Effective identification of species using short DNA fragments (DNA barcoding and DNA metabarcoding) requires reliable sequence reference libraries of known taxa. Both taxonomically comprehensive coverage and content quality are important for sufficient accuracy. For aquatic ecosystems in Europe, reliable barcode reference libraries are particularly important if molecular identification tools are to be implemented in biomonitoring and reports in the context of the EU Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD). We analysed gaps in the two most important reference databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a focus on the taxa most frequently used in WFD and MSFD. Our analyses show that coverage varies strongly among taxonomic groups, and among geographic regions. In general, groups that were actively targeted in barcode projects (e.g. fish, true bugs, caddisflies and vascular plants) are well represented in the barcode libraries, while others have fewer records (e.g. marine molluscs, ascidians, and freshwater diatoms). We also found that species monitored in several countries often are represented by barcodes in reference libraries, while species monitored in a single country frequently lack sequence records. A large proportion of species (up to 50%) in several taxonomic groups are only represented by private data in BOLD. Our results have implications for the future strategy to fill existing gaps in barcode libraries, especially if DNA metabarcoding is to be used in the monitoring of European aquatic biota under the WFD and MSFD. For example, missing species relevant to monitoring in multiple countries should be prioritized. We also discuss why a strategy for quality control and quality assurance of barcode reference libraries is needed and recommend future steps to ensure full utilization of metabarcoding in aquatic biomonitoring.

ecology↗

bioRxiv2018

DNA metabarcoding from sample fixative as a quick and voucher preserving biodiversity assessment method

Metabarcoding is a powerful tool for biodiversity assessment and has become increasingly popular in recent years. Although its reliability and applicability have been proven in numerous scientific studies, metabarcoding still suffers from some drawbacks. One is the usually mandatory destruction of specimens before DNA extraction, which is problematic because it does not allow a later taxonomic evaluation of the results. Additionally, metabarcoding often implements a time-consuming step, where specimens need to be separated from substrate or sorted in different size classes. A non-destructive protocol, excluding any sorting step, where the extraction of DNA is conducted from a samples fixative (ethanol) could serve as an alternative. We test an innovative protocol, where the sample preserving ethanol is filtered and DNA extracted from the filter for subsequent DNA metabarcoding. We first tested the general functionality of this approach on 15 mock communities comprising one individual of eight different macroinvertebrate taxa each and tried to increase DNA yield through different treatments (ultrasonic irradiation, shaking, freezing). Application of the method was successful for most of the samples and taxa, but showed weaknesses in detecting mollusc taxa. In a second step, the community composition detected in DNA from ethanol was compared to conventional bulk sample metabarcoding of complex environmental samples. We found that especially taxa with pronounced exoskeleton or shells (Coleoptera, Isopoda) and small taxa (Trombidiformes) were underrepresented in ethanol samples regarding taxa diversity and read numbers. However, read numbers of Diptera (mainly chironomids) and Haplotaxida were higher in ethanol derived DNA samples, which might indicate the detection of stomach content, which would be an additional advantage of this approach. Concerning EPT (Ephemeroptera, Plecoptera, Trichoptera) taxa which are decisive for the determination of ecological statuses, both methods had 46 OTUs in common with 4 unique to the ethanol samples and 10 to the bulk samples. Results indicate that fixative-based metabarcoding is a non-destructive, time-saving alternative for biodiversity assessments focussing on taxa used for ecological status determination. For a comprehensive picture on total biodiversity, the method might however not be sufficient and conventional bulk sample metabarcoding should be applied.

molecular biology↗

bioRxiv2017

Environmental DNA metabarcoding of rivers: Not all eDNA is everywhere, and not all the time

Environmental DNA metabarcoding has become a popular tool for the assessment of freshwater biodiversity, but it is largely unclear how sampling time and location influence the assessment of communities. Abiotic factors in rivers can change on small spatial and temporal scale and might greatly influence eDNA metabarcoding results. In this study, we sampled three German rivers at four locations per sampling site: 1. Left river side, surface water 2. Right river side, surface water, 3. Left side, close to the riverbed, 4. Right side, close to the riverbed. For the rivers Ruhr and Mohne, sampling was conducted three times in spring, each sampling one week apart. The Ruhr was again sampled in autumn and the Gillbach was sampled in winter. Sequencing on an Illumina MiSeq with COI primers Bf2/BR2 revealed diverse communities (6493 Operational taxonomic units, OTUs), which largely differed between rivers. Communities changed significantly over time in the Ruhr, but not in the Mohne. Sampling location influenced recovered communities in the Mohne and in the Ruhr in autumn. Our results have important implications for future eDNA studies, which should take into account that not all eDNA in rivers is everywhere and not at all times.

molecular biology↗

bioRxiv2017

A simple centrifugation protocol leads to a 55-fold mitochondrial DNA enrichment and paves the way for future mitogenomic research

DNA (meta)barcoding is used to study biodiversity and is available for standardised assessments. However, it suffers from PCR bias, which can lead to the loss of specific taxa. PCR-free techniques such as shotgun metagenomics are therefore thought to be more suited for biodiversity assessments, but are currently limited by incomplete reference libraries.\n\nThe technique of mitogenome-skimming or mitogenomics, in which complete mitochondrial genomes are sequenced, is ideal to bridge the techniques of (meta)barcoding and metagenomics. However, without the enrichment of mitochondria, roughly 99 % of all sequencing reads are of non-mitochondrial origin and mostly useless for common applications, e.g. species identification.\n\nHere, we present a simple centrifugation protocol that leads to an average 140-fold enrichment of mitochondrial DNA. By sequencing six mock- communities - comprising the freshwater taxa Corbicula fluminea, Gammarus roeselii and Hydropsyche exocellata each - we recovered whole mitochondrial genomes of these species and the acanthocephalan endoparasite Pomphorhynchus laevis.\n\nThe presented protocol will greatly speed up building reference libraries for whole mitochondrial genomes, as dozens of species could be sequenced on a single MiSeq run. Subsequently, it will also allow biodiversity assessments using mitogenomics at greatly reduced costs in comparison to mitogenomic approaches without prior enrichment for mitochondria.

molecular biology↗