Biology subjects

Conwill, A.

Publications and source records attributed to Conwill, A..

5 recordsLinked to original sources

bioRxiv2025

High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV

Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstructing transmission events, and identifying antimicrobial resistance mutations. While many tools have been developed to identify single nucleotide variants (SNVs) from whole-genome sequencing, they often suffer from high false positive rates due to the complexity of bacterial genomes and the need for different filtering cutoffs across sample types and sequencing depths. As datasets increase in size, the manual filtering required for high accuracy presents a significant obstacle. Here, we present AccuSNV, a novel deep learning-based tool for high-precision and automated bacterial SNV calling. Unlike traditional methods that process one sample at a time, AccuSNV leverages a convolutional neural network (CNN) that integrates alignment information across multiple samples, enhancing precision through learned across-sample patterns. We evaluated AccuSNV against seven popular SNV calling tools using simulated data from six bacterial species with varied sequencing depths, numbers of isolates, mutations, and divergence levels. To further validate its real-world utility, we tested AccuSNV on multiple curated bacterial datasets containing reported SNVs. In both simulated and real-world scenarios, AccuSNV consistently achieved the best performance. Moreover, AccuSNV provides comprehensive user-friendly downstream analysis modules and outputs, including mutation annotation information, phylogenetic inference, dN/dS calculations, and optional manual filtering. Together with the automated deep learning-based calling, these features make AccuSNV broadly accessible to users with different levels of computational expertise.

bioinformatics↗

bioRxiv2024

Highly-resolved within-species dynamics in the human facial skin microbiome

Adult human facial skin microbiomes are remarkably similar at the species-level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 360 metagenomes, we reconstruct on-person evolutionary history to reveal on and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

microbiology↗

bioRxiv2021

Anatomy promotes neutral coexistence of strains in the human skin microbiome

What enables strains of the same species to coexist in a microbiome? Here, we investigate if host anatomy can explain strain co-residence of Cutibacterium acnes, the most abundant species on human skin. We reconstruct on-person evolution and migration using 947 C. acnes colony genomes acquired from 16 subjects, including from individual skin pores, and find that pores maintain diversity by limiting competition. Although strains with substantial fitness differences coexist within centimeter-scale regions, each pore is dominated by a single strain. Moreover, colonies from a pore typically have identical genomes. An absence of adaptive signatures suggests a genotype-independent source of low within-pore diversity. We therefore propose that pore anatomy imposes random single-cell bottlenecks during migration into pores and subsequently blocks new migrants; the resulting population fragmentation reduces competition and promotes coexistence. Our findings imply that therapeutic interventions involving pore-dwelling species should focus on removing resident populations over optimizing probiotic fitness.

microbiology↗

bioRxiv2018

Rapid, inexpensive measurement of synthetic bacterial community composition by Sanger sequencing

Simple synthetic bacterial communities are powerful tools for studying microbial ecology and evolution, as they enable rapid iteration between controlled laboratory experiments and theoretical modeling. However, their utility is hampered by the lack of fast, inexpensive, and accurate methods for quantifying bacterial community composition. For instance, while next-generation amplicon sequencing can be very accurate, high costs (>$30 per sample) and turnaround times (>1 month) limit the nature and pace of experiments. Here, we introduce a new approach for quantifying composition in synthetic bacterial communities based on Sanger sequencing. First, for a given community, we PCR-amplify a universal marker gene (here, the 16S rRNA gene), which yields a mixture of amplicons. Second, we sequence this amplicon mixture in a single Sanger sequencing reaction, which produces a \"mixed\" electropherogram with contributions from each community member. We also sequence each community members marker gene individually to generate \"individual\" electropherograms. Third, we fit the mixed electropherogram as a linear combination of time-warped individual electropherograms, thereby allowing us to estimate the fractional amplicon abundance of each strain within the community. Importantly, our approach accounts for retention-time variability in electrophoretic signals, which is crucial for accurate compositional estimates. Using synthetic communities of marine bacterial isolates, we show that this approach yields accurate and reproducible abundance estimates for two-, four-, and seven-strain bacterial communities. Furthermore, this approach can provide results within one day and costs ~$5 USD per sample. We envision this approach will enable new insights in microbial ecology by increasing the number of samples that can be analyzed and enabling faster iteration between experiments and theory. We have implemented our method in a free and open-source R package called CASEU (\"Compositional Analysis by Sanger Electropherogram Unmixing\"), available at https://bitbucket.org/DattaManoshi/caseu.

bioinformatics↗

bioRxiv2017

Migration alters oscillatory dynamics and promotes survival in connected bacterial populations

Migration influences population dynamics on networks, thereby playing a vital role in scenarios ranging from species extinction to epidemic propagation. While low migration rates prevent local populations from becoming extinct, high migration rates enhance the risk of global extinction by synchronizing the dynamics of connected populations. Here, we investigate this trade-off using two mutualistic strains of E. coli that exhibit population oscillations when co-cultured. In experiments, as well as in simulations using a mechanistic model, we observe that high migration rates lead to in-phase synchronization whereas intermediate migration rates perturb the oscillations and change their period. Further, our simulations predict, and experiments show, that connected populations subjected to more challenging antibiotic concentrations have the highest probability of survival at intermediate migration rates. Finally, we identify altered population dynamics, rather than recolonization, as the primary cause of extended survival.

ecology↗