Biology subjects

Chorlton, S. D.

Publications and source records attributed to Chorlton, S. D..

6 recordsLinked to original sources

bioRxiv2023

refMLST: Reference-based Multilocus Sequence Typing Enables Universal Bacterial Typing

SummaryCommonly used approaches for genomic investigation of bacterial outbreaks, including SNP and gene-by-gene approaches, are limited by the requirement for curated allele schemes. As a result, they only work on a select subset of known organisms, and fail on novel or less studied pathogens. We introduce refMLST, a gene-by-gene approach using the reference genome of a bacterium to form a scalable, reproducible and robust method to perform outbreak investigation. When applied to 1263 Salmonella enterica genomes, refMLST enabled consistent clustering, improved resolution and faster processing in comparison to chewieSnake. refMLST is applicable to any bacterial species with a public genome, does not require a curated scheme, and automatically accounts for genetic recombination. Availability and ImplementationrefMLST is freely available for academic use at https://bugseq.com/academic.

bioinformatics↗

bioRxiv2023

No isolate, no problem: Using a novel insertion sequence PCR to link rats to human shigellosis cases in an underserved urban community

IntroductionDuring an investigation into a cluster of Shigella flexneri serotype 2a cases in an underserved community, we assessed the relatedness of human and rat S. flexneri isolates utilizing a novel PCR targeting insertion sites (IS-PCR) of mobile elements in the Shigella genome characteristic of the cluster strain. MethodsWhole genome sequences of S. flexneri (n=50) associated with the cluster were analyzed. de novo genome assemblies were analyzed by a Geneious V10.2.6 motif search, and 2 unique IS were identified in all human Shigella sequences of the local cluster. Hydrolysis probe PCR assays were designed to detect these sequences consisting of forward and reverse primers to amplify across each insertion site, and a hydrolysis probe spanning the insertion site. IS-PCR was performed for three Shigella PCR-positive culture-negative rat intestine specimens from this community. ResultsBoth insertion sites were detected in the de novo genome assemblies of all clinical S. flexneri isolates (n=50). Two of the three PCR-positive culture-negative rat samples were positive for both unique IS identified in the human S. flexneri isolates, suggesting that the rat Shigella spp. strains were closely related to the human strains in the cluster. The cycle threshold (Ct) values were >35, indicating that the bacterial load was very low in the rat samples. ConclusionsTwo unique IS were identified in clinical isolates from a community S. flexneri cluster. Both IS targets were identified in PCR-positive (Shigella spp.), culture-negative rat tissue and clinical isolates from humans, indicating relatedness.

microbiology↗

bioRxiv2021

BugSplit: highly accurate taxonomic binning of metagenomic assemblies enables genome-resolved metagenomics

A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplits taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae and Neisseria gonorrhoeae infection, BugSplits taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic.

bioinformatics↗

bioRxiv2021

BugSeq 16S: NanoCLUST with Improved Consensus Sequence Classification

NanoCLUST has enabled species-level taxonomic classification from noisy nanopore 16S sequencing data for BugSeqs users and the broader nanopore sequencing community. We noticed a high misclassification rate of NanoCLUST-derived consensus 16S sequences due to its use of BLAST top hit taxonomy assignment. We replaced the consensus sequence classifier of NanoCLUST with QIIME2s VSEARCH-based classifier to enable greater accuracy. We use mock microbial community and clinical 16S sequencing data to show that this replacement results in significantly improved nanopore 16S accuracy (over 5% recall and 19% precision), and make this new tool (BugSeq 16S) freely available for academic use at BugSeq.com/free.

bioinformatics↗

bioRxiv2020

BugSeq: a highly accurate cloud platform for long-read metagenomic analyses

BackgroundAs the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge) or take days to analyse modern sequencer outputs (eg. MetaMaps). ResultsWe present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. BugSeq (F1=0.91-0.95) offers better read classification than MetaMaps (F1=0.89-0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1=0.79-0.93) while offering competitive run times. We apply BugSeq to metagenomic sequencing of 41 samples from patients with lower respiratory tract infections and show that it produces greater concordance with microbiological culture and qPCR compared with "Whats In My Pot" analysis. ConclusionBugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free. Contactsam@bugseq.com

bioinformatics↗

bioRxiv2019

Reanalysis of Alzheimer's Brain Sequencing Data Reveals Absence of Purported HHV6A and HHV7

Readhead et al. recently reported in Neuron the detection and association of Human Herpesviruses 6A (HHV6A) and 7 (HHV7) with Alzheimers disease by shotgun sequencing. I was skeptical of the specificity of their modified Viromescan bioinformatics method and subsequent analysis for numerous reasons. In their supplementary data, they report the detection of Variola virus, the etiological agent of the eradicated disease smallpox, in 97.5% of their Mount Sinai Brain Bank dataset. I reanlyzed Readhead et als data using highly sensitive and specific alternative methods and find no HHV7 reads in their samples; HHV6A reads were found in only 2 out of their top 15 samples sorted by reported HHV6A abundance. Finally, I recreate Readhead et als modified Viromescan method and identify reasons for its low specificity.

bioinformatics↗