Biology subjects

Cheney, L.

Publications and source records attributed to Cheney, L..

4 recordsLinked to original sources

bioRxiv2026

SpikeLab: Agentic tools for spike data analysis

Large language models have the potential to transform scientific research and analysis, but without domain-specific structure they produce silent methodological errors, unreported decisions, and irreproducible results. Here we present SpikeLab, a text-to-analysis framework for neural spike data that combines composable data structures with a skill-based agentic system enforcing bounded autonomy: mandatory use of expert-vetted methods, correctness over efficiency, and clarification-seeking on ambiguous requests. In a controlled benchmark on electrophysiology data, Sonnet 4.6 with SpikeLab produced correct and reproducible results across all tasks, outperforming both the unassisted Sonnet and the more capable Opus 4.6, which exhibited deterministic failures including ad hoc method invention, silent data reduction, and inconsistent experimental designs. We demonstrate versatility across in vivo mouse, human, and in vitro brain organoid recordings, and apply the framework to a pharmacological dose-response study spanning single-unit dynamics, pairwise network structure, burst-level temporal sequences, and latent population states, all through natural language prompts without writing analysis code.

neuroscience↗

bioRxiv2024

Characterising Staphylococcus aureus genomic epidemiology with Multilevel Genome Typing.

Staphylococcus aureus is a major source of both hospital and community acquired infections, and is the leading source of skin and soft tissue infections worldwide. Advances in whole genome sequencing (WGS) technologies have recently generated large volumes of S. aureus WGS data. The timely classification of S. aureus WGS data with genomic typing technologies has the potential to describe detailed genomic epidemiology at large and small scales. In this study, a multilevel genome typing (MGT) scheme comprised of 8 levels of multilocus sequence typing schemes of increasing resolution was developed for S. aureus and used to analyse 50,481 publicly available genomes. Application of MGT to S. aureus epidemiology was showcased in three case studies. Firstly, the population structure of the globally disseminated sequence type ST8 were described by MGT2, which was compared with Spa typing. Secondly, MGT was used to characterise MLST ST8 - PFGE USA300 isolates that colonised multiple body sites of the same patient. Unique STs from multiple MGT levels were able to group isolates of the same patient, and the highest resolution MGT8 separated isolates within a patient that varied in predicted antimicrobial resistance. Lastly, MGT was used to describe the transmission of MLST ST239 - SCCmec III throughout a single hospital. MGT STs were able to describe both isolates that had spread between wards and also isolates that had colonised different reservoirs within a ward. The S. aureus MGT describes large- and small-scale S. aureus genomic epidemiology with scalable resolutions using stable and standardised ST assignments. The S. aureus MGT database is online (https://mgtdb.unsw.edu.au/staphylococcus) and is capable of tracking new and existing clones to facilitate the design of new strategies to reduce the global burden of S. aureus related diseases.

microbiology↗

bioRxiv2023

SaLTy: a novel Staphylococcus aureus Lineage Typer

Staphylococcus aureus asymptomatically colonises 30% of humans and in 2017 was associated with 20,000 deaths in the USA alone. Dividing S. aureus into smaller sub-groups can reveal the emergence of distinct sub-populations with varying potential to cause infections. Despite multiple molecular typing methods categorising such sub-groups, they do not take full advantage of S. aureus WGS when describing the fundamental population structure of the species. In this study, we developed Staphylococcus aureus Lineage Typing (SaLTy), which rapidly divides the species into 61 phylogenetically congruent lineages. Alleles of three core genes were identified that uniquely define the 61 lineages and were used for SaLTy typing. SaLTy was validated on 5,000 genomes and 99.12% (4,956/5,000) of isolates were assigned the correct lineage. We compared SaLTy lineages to previously calculated clonal complexes (CCs) from BIGSdb (n=21,173). SALTy improves on CCs by grouping isolates congruently with phylogenetic structure. SaLTy lineages were further used to describe the carriage of Staphylococcal chromosomal cassette containing mecA (SCCmec) which is carried by methicillin-resistant S. aureus (MRSA). Most lineages had isolates lacking SCCmec and the four largest lineages varied in SCCmec over time. Classifying isolates into SaLTy lineages, which were further SCCmec typed, allowed SaLTy to describe high-level MRSA epidemiology We provide SALTy as a simple typing method that defines phylogenetic lineages (https://github.com/LanLab/SaLTy). SALTy is highly accurate and can quickly analyse large amounts of S. aureus WGS. SALTy will aid the characterisation of S. aureus populations and the ongoing surveillance of sub-groups that threaten human health.

microbiology↗

bioRxiv2020

Elucidation of global and local epidemiology of Salmonella Enteritidis through multilevel genome typing

Salmonella enterica serovar Enteritidis is a major cause of foodborne Salmonella infections and outbreaks in humans. Effective surveillance and timely outbreak detection are essential for public health control. Multilevel genome typing (MGT) with multiple levels of resolution has been previously demonstrated as a promising tool for this purpose. In this study, we developed MGT with nine levels for S. Enteritidis and characterised the genomic epidemiology of S. Enteritidis in detail. We examined 26,670 publicly available S. Enteritidis genome sequences from isolates spanning 101 years from 86 countries to reveal their spatial and temporal distributions. Using the lower resolution MGT levels, globally prevalent and regionally restricted sequence types (STs) were identified; avian associated MGT4-STs were found that were common in human cases in the USA were identified; temporal trends were observed in the UK with MGT5-STs from 2014 to 2018, revealing both long lived endemic STs and the rapid expansion of new STs. Using MGT3 to MGT6, we identified MDR associated STs at various MGT levels, which improves precision of detection and global tracking of MDR clones. We also found that the majority of the global S. Enteritidis population fell within two predominant lineages, which had significantly different propensity of causing large scale outbreaks. An online open MGT database has been established for unified international surveillance of S. Enteritidis. We demonstrated that MGT provides a flexible and high-resolution genome typing tool for S. Enteritidis surveillance and outbreak detection. Impact statementSalmonella enterica serovar Enteritidis is a common foodborne pathogen that can cause large outbreaks. Surveillance and high-resolution typing are essential for outbreak prevention and control. Genome sequencing offers unprecedented power for these purposes and a standardised method or platform for the interpretation, comparison and communication of genomic typing data is highly desirable. In this work, we developed a genomic typing scheme called Multilevel Genome Typing (MGT) for S. Enteritidis. We analysed 26,670 publicly available genomes of S. Enteritidis using MGT. We characterised the geographic and temporal distribution of S. Enteritidis MGT types as well as their association with multidrug resistance (MDR) and virulence genes. A publicly available MGT database for S. Enteritidis was established, which has the potential facilitate the unified global public health surveillance for this pathogen. Data SummaryO_LIThe MGT database for S. Enteritidis is available at https://mgtdb.unsw.edu.au/enteritidis/. C_LIO_LIAll accession numbers of the public available genomes were available in the MGT database and Data Set S1, Tab 1. And there were no newly sequenced data in this study. C_LIO_LISupplementary material: Supplementary Fig. S1 to S7, supplementary methods and supporting results about the evaluation of potential repeat sequencing bias. C_LIO_LIData Set S1: Supporting tables of the main results. C_LIO_LIData Set S2. Supporting tables of the repeat sequencing bias evaluation by removing the potential repeat sequencing isolates. Note outbreak isolates may also be removed. C_LI

genomics↗