Biology subjects

Hegarty, B.

Publications and source records attributed to Hegarty, B..

3 recordsLinked to original sources

bioRxiv2026

Validation of shoe sole dust as a microbial sampler reveals distinct fungal and bacterial responses to nearby vegetation

Urbanization-driven environmental change has significant implications for human health and well-being. However, studies have found differing patterns in microbial diversity along urbanization gradients; and it remains unknown whether this reflects methodological limitations or genuine ecological complexities. Resolving these inconsistencies requires innovative, reproducible methods that accurately reflect human contact with environmental microbiota. In this study, we have validated a new method for assessing environmental microbial exposure by measuring microbiota from particulate matter collected from shoe soles and studied the influence of vegetation at different proximities. Through repeated walks on routes along an urbanization gradient in Finland, we show that left and right shoe sole dust from the same walk and same route represent more similar microbial communities compared to different walks and routes. We found that bacterial biomass and diversity were best predicted by Normalized Difference Vegetation Index (NDVI, as a measure of greenness) immediately surrounding the walking path, whereas fungal communities responded to broader landscape-scale greenness (100m-1km), suggesting that bacteria and fungi are governed by different dispersal processes. Importantly, NDVI explained these differences in diversity more effectively than simple classifications of the path based on its substrate and whether it was in a rural or urban setting. Shoe sole dust sampling offers a simple, effective, and reliable approach for evaluating microbial exposures, capturing scale-dependent microbial responses to vegetation, and enabling more robust epidemiological studies on the health effects of greenness and environmental biodiversity.

microbiology↗

bioRxiv2023

Benchmarking combined informatics approaches for virus discovery: Caution is needed when combining in silico identification methods

BackgroundThe identification of viruses from environmental metagenomic samples using informatics tools has offered critical insights in microbiome studies. However, it remains difficult for researchers to know for their specific study which tool(s) and settings are best suited to maximize capture of viruses while minimizing false positives. Studies are increasingly combining multiple tool outputs attempting to recover more viruses, but no combined approach has been benchmarked for accuracy. Here, we benchmarked 63 viral identification rulesets against mock metagenomes composed of publicly available viral, bacterial, archaeal, fungal, and protist sequences. These rulesets are based on combinations of four single-tool rules and two multi-tool tuning rules. We applied these rulesets to various aquatic metagenomes and filtering strategies to evaluate the impact of habitat and viral enrichment on individual and combined tool performance. We provide a packaged pipeline for researchers that want to replicate our process. ResultsWe found that combining rules increased viral recall, but at the expense of increased false positives. Six of the 63 combinations tested had equivalent accuracies to the highest one (MCC=0.77, padj [≥] 0.05). All of the six high accuracy rulesets included VirSorter2, five included our "tuning removal" rule, and no high performing rulesets used more than four of our six rules. DeepVirFinder, VIBRANT, and VirSorter were each found once in these high accuracy rulesets, but never in combination with each other. Our validation suggests that the MCC plateau at 0.77 is caused by inaccurate labeling of the data that viral identification tools rely on for training and validation. In the aquatic metagenomes, our "highest MCC" ruleset identified a higher proportion of viral sequences in the virus-enriched samples (44-46%) than the non-enriched, cellular metagenomes (7-19%). ConclusionWhile improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with curating accurately labeled viral gene and sequence databases. For most applications, we recommend the use of the ruleset that uses VirSorter2 and our empirically derived tuning removal rule. By providing a rigorous overview of the behavior of in silico viral identification strategies, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

bioinformatics↗

bioRxiv2022

Evaluating limitations of quantitative metagenomics with synthetic dsDNA and ssDNA standards

Quantitative metagenomic methods are maturing but continue to lack clearly-defined analytical limits. Here, we developed a computational tool, QuantMeta, to determine the absolute abundance of targets in metagenomes spiked with synthetic DNA standards. The tool establishes (1) entropy-based detection thresholds to confidently determine the presence of targets, and (2) an approach to identify and correct read mapping or assembly errors and thus improve the quantification accuracy. Together this allows for an approach to confidently quantify absolute abundance of targets, be they microbial populations, genes, contigs, or metagenome assembled genomes (MAGs). We applied the approach to quantify single- and double-stranded DNA viruses in wastewater viral metagenomes, including pathogens and bacteriophages. Concentrations of total DNA viruses in wastewater influent and effluent were greater than 108 copies/mL using QuantMeta. Human-associated DNA viruses were detected and quantifiable with QuantMeta thresholds, including polyomavirus, papillomavirus, and crAss-like phages, at concentrations similar to previous reports that utilized quantitative PCR-based assays. Our results highlight the higher detection thresholds of quantitative metagenomics ([~]500 copies/L) as compared to PCR-based quantification ([~]10 copies/L) despite a sequencing depth of 200 million reads per sample. The QuantMeta approach, applicable to both viral and cellular metagenomes, advances quantitative metagenomics by improving the accuracy of measured target absolute abundances.

genomics↗