Biology subjects

Kehoe, M.

Publications and source records attributed to Kehoe, M..

5 recordsLinked to original sources

bioRxiv2024

sangerFlow, a Sanger sequencing-based bioinformatics pipeline for pests and pathogens identification

Sequencing of a Polymerase Chain Reaction product (amplicon) is called amplicon sequencing. Amplicon sequencing allows for reliable identification of an organism by amplifying, sequencing, and analysing a single conserved marker gene or DNA barcode. As this approach generally involves a single gene, it is a light-weight protocol compared to multi-locus or whole genome sequencing for diagnostic purposes; yet considerably reliable. Therefore, Sanger-based high-quality amplicon sequencing is widely deployed for species identification and high-throughput biosecurity surveillance. However, keeping up with the data analysis in a large-scale surveillance or diagnostic settings could be a limiting factor because it involves manual quality control of the raw sequencing data, alignment of the forward and reverse reads, and finally web-based Blastn search of all the amplicons. Here, we present a bioinformatics pipeline that automates the entire analysis. As a result, the pipeline is scalable with high-volume of samples and reproducible. Furthermore, the pipeline leverages the modern open-source Nextflow and Singularity concept, thus it does not require software installation except Nextflow and Singularity, software subscription, or programming expertise from the end users making it widely adaptable. Availability and implementationsangerFlow source code and documentation are freely available for download at GitHub, implemented in Nextflow and Singularity.

bioinformatics↗

bioRxiv2024

Mating-compatibility genes employed as diagnostic markers to identify novel incursions of the myrtle rust pathogen Austropuccinia psidii

Austropuccinia psidii is the causal agent of myrtle rust in over 480 species within the family Myrtaceae. Lineages of A. psidii are structured by host in its native range, and some have success on new-encounter hosts. For example, the pandemic biotype has spread beyond South America, and proliferation of other lineages is an additional risk to biodiversity and industries. Efforts to manage A. psidii incursions, including lineage differentiation, relies on variable microsatellite markers. Testing these markers is time-consuming and complex, particularly on a large scale. We designed a novel diagnostic approach targeting the fungal mating-type HD (homeodomain) transcription factor locus to address these limitations. The HD locus (bW1/2-HD1 and bE1/2-HD2) is highly polymorphic, facilitating clear biological predictions about its inheritance from founding populations. To be considered the same lineage, all four HD alleles must be identical. Our lineage diagnostics relies on PCR amplification of the HD locus in different genotypes of A. psidii followed by amplicon sequencing using Oxford Nanopore Technologies (ONT) and comparative analysis. The lineage-specific assay was validated on four isolates with existing genomes, uncharacterized isolates, and directly from infected leaf material. We reconstructed HD alleles from amplicons and confirmed their sequence identity relative to their reference. Genealogies using HD alleles confirmed the variations at the HD loci among lineages/isolates. Our study establishes a robust diagnostic tool, for differentiating known lineages of A. psidii based biological predictions. This tool holds promise for detecting new pathogen incursions and can be refined for broader applications, including air-sample detection and mixed-isolate infections.

plant biology↗

bioRxiv2019

Tree Lab: Portable genomics for early detection of plant viruses and pests in Sub-Saharan Africa

In this case study we successfully teamed the PDQeX DNA purification technology developed by MicroGEM, New Zealand, with the MinION and MinIT mobile sequencing devices developed by Oxford Nanopore Technologies to produce an effective point-of-need field diagnostic system. The PDQeX extracts DNA using a cocktail of thermophilic proteinases and cell wall degrading enzymes, thermo-responsive extractor cartridges and a temperature control unit. This single-step closed system delivers purified DNA with no cross contamination. The MinIT is a newly released data processing unit that converts MinION raw signal output into base called data locally in real time, removing the need for high specification computers and large file transfers from the field. All three devices are battery powered with an exceptionally small footprint that facilitates transport and set up.\n\nTo evaluate and validate capability of the system for unbiased pathogen identification by realtime sequencing in a farmers field setting, we analysed samples collected from cassava plants grown by subsistence farmers in three sub-Sahara African countries (Tanzania, Uganda and Kenya). A range of viral pathogens, all with similar symptoms, greatly reduce yield or completely destroy cassava crops. 800 million people worldwide depend on cassava for food and yearly income, and viral diseases are a significant constraint on its production (https://cassavavirusactionproject.com). Early pathogen detection at a molecular level has great potential to rescue crops within a single growing season by providing results that inform decisions on disease management, use of appropriate virus resistant or replacement planting.\n\nThis case study presented conditions of working in-field with limited or no access to mains power, laboratory infrastructure, internet connectivity and highly variable ambient temperature. An additional challenge is that, generally, plant material contains inhibitors of downstream molecular processes making effective DNA purification critical. We successfully undertook real-time on-farm genome sequencing of samples collected from cassava plants on three farms, one in each country. Cassava mosaic begomoviruses were detected by sequencing leaf, stem, tuber and insect samples. The entire process, from arrival on farm to diagnosis including sample collection, processing and provisional sequencing results was complete in under 4 hours. The need for accurate, rapid and on-site diagnosis grows as globalized human activity accelerates. This technical breakthrough has applications that are relevant to human and animal health, environmental management and conservation.

genomics↗

bioRxiv2019

Successful forecasting of harmful cyanobacteria blooms with high frequency lake data

Cyanobacterial blooms are causing increasing issues across the globe. Bloom forecasting can facilitate adaptation to blooms. Most bloom forecasting models depend on weekly or fortnightly sampling, but these sparse measurements can miss important dynamics. Here we develop forecasting models from five years of high frequency summer monitoring in a shallow lake (which serves as an important regional water supply). A suite of models were calibrated to predict cyanobacterial fluorescence (a biomass proxy) using measurements of: cyanobacterial fluorescence, water temperature, light, and wind speed. High temporal autocorrelation contributed to relatively strong predictive power over 1, 4 and 7 day intervals. Higher order derivatives of water temperature helped improve forecasting accuracy. While traditional monitoring and modelling have supported forecasting on longer timescales, we show high frequency monitoring combined with telemetry allows forecasting over timescales of 1 day to 1 week, supporting early warning, enhanced monitoring, and adaptation of water treatment processes.

ecology↗

bioRxiv2017

The predictability of a lake phytoplankton community, from hours to years

Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

ecology↗