Biology subjects

Grassly, N. C.

Publications and source records attributed to Grassly, N. C..

4 recordsLinked to original sources

bioRxiv2025

AmpliconTyper tool for analysing ONT multiplex PCR data from environmental and other mixed sources

Amplicon sequencing is a popular method for understanding the diversity of bacterial communities in mixed samples as exemplified by 16S rRNA metagenome sequencing. This approach has been extended into multiplex amplicon sequencing in which multiple targets are amplified in the same polymerase chain reaction (PCR). Multiple tools exist to process the sequencing data produced via the short-read Illumina platform, but there are fewer options for long-read Oxford Nanopore Technologies (ONT) sequencing, or for processing data from environmental surveillance or other sources with many different organisms. We have developed AmpliconTyper (v0.1.28, DOI: 10.5281/zenodo.14621928) for analysing multiplex amplicon sequencing data from environmental (e.g. wastewater) or similarly contaminated samples, generated using ONT devices. The software tool uses machine learning to classify sequencing reads into target and non-target organisms with very high specificity and sensitivity. The user can train models using public and/or user-generated data, which can subsequently be applied to analyse new data. The tool can also generate amplicon consensus sequences, as well as identify single nucleotide polymorphisms (SNPs) and report their genotype implications, such as association with lineages or antimicrobial resistance (AMR). The tool is freely available via Bioconda and GitHub (https://github.com/AntonS-bio/AmpliconTyper). AmpliconTyper allows robust identification of target organism reads in ONT sequenced environmental samples, and can identify user-specified lineage or AMR markers. Impact statementAmpliconTyper (v0.1.28) is a software package that enables users to analyse amplicon sequences generated by targeted amplification followed by ONT sequencing, for environmental or other similarly contaminated samples. The analysis includes mapping of reads to target amplicon sequences, classification of each sequenced read as either originating from a target or non-target organism, followed by identification of user-specified SNPs and generation of an interactive report summarising the findings. The strength of AmpliconTyper lies in its ability to train a machine learning model using public data to create sequencing read classification models tailored to a users application. AmpliconTyper is designed specifically to work with extremely noisy data that includes a large share of off-target amplification reads such as those encountered in environmental surveillance applications. Data SummaryFor the purpose of designing and testing AmpliconTyper we have used two datasets. The first consisted of 69 Salmonella enterica serovar. Typhi (S. Typhi) and 10,303 other Enterobacteriaceae WGS ONT nanopore sequencing libraries (Supp. Data 1) from NCBI Sequence Read Archive (SRA) (1). We used these data to evaluate the performance of different classifier models and to train a model for our use-case, i.e. amplicon-based detection of S. Typhi from environmental surveillance samples (2). In addition, to further evaluate the performance of AmpliconTyper in our use case, we have applied an amplicon sequencing protocol (2) to generate amplicon data for S. Typhi using two S. Typhi isolates (NCBI accessions SRR5949979 and SRR7165748) provided by Satheesh Nair (UKHSA) (3). We also used same protocol to the pooled sample of American Type Culture Collection (ATCC) consisting of S. Paratyphi A (ATCC 9150D), S. Paratyphi B (ATCC-BAA-1250D), S. Paratyphi C (ATCC-BAA-1715D), Aeromonas hydrophila (ATCC-7965D), Klebsiella pneumoniae (ATCC-BAA-1706D), and Citrobacter freundii (ATCC-8090D) chosen for their close relationship to S. Typhi. The test data for classification is available at https://github.com/AntonS-bio/AmpliconTyper/tree/main/test_data. Newly generated data was deposited in European Nucleotide Archive project PRJEB81565.

genomics↗

bioRxiv2023

A simple correction to adjust for sampling biases in phylogeographic discrete trait analysis

Discrete trait analysis (DTA) methods are widely used to infer patterns of movement between discrete geographic locations from genetic sequence data as they are simple and fast to implement. With the increasing interest in phylogeography for pathogen genomic epidemiology, accounting for and understanding the limitations of the methods used is becoming increasingly important. Applying DTA to location makes several strong simplifying assumptions, including the assumption of equal sampling probability across locations. This may lead to biased estimates of migration rates and ancestral locations where sampling rates vary. Here we investigate these biases using simulated migration and transmission data. We propose a simple correction to DTA for unequal sampling probability across locations when sampling rates are known or can be estimated. This correction to the migration rate matrix can be applied only to phylogenetic branches leading to sampled tips or to the whole phylogeny. We assess the performance of this correction using simulated data for a range of endemic transmission scenarios with varied sampling and migration rates. In a simple, two-location, case the correction performs significantly better than a standard DTA method in three of the four sampling rate distributions investigated. Applying the correction across only the tips of the phylogeny and over the full depth of the tree gives similar results, with the correction only at the tips performing marginally better in this simple two-location simulation. As migration is a key driver of infectious disease spread, improving estimates of migration with simple phylogenetic methods is a valuable development in infectious disease phylogeography.

evolutionary biology↗

bioRxiv2023

Automated detection and classification of polioviruses from nanopore sequencing reads using piranha

Widespread surveillance, rapid detection and appropriate intervention will be critical for successful eradication of poliovirus. With deployable next-generation sequencing (NGS) approaches, such as Oxford Nanopore Technologies MinION, the time from sample to result can be significantly reduced compared to cell culture and Sanger sequencing. We developed piranha (poliovirus investigation resource automating nanopore haplotype analysis) to aid routine poliovirus testing of both stool and environmental samples and alleviate the bioinformatic bottleneck that often exists for labs adopting novel NGS approaches. Piranha can be used for efficient intratypic differentiation of poliovirus serotypes, for classification of Sabin-like polioviruses and for detection of wild-type and vaccine derived polioviruses. It produces interactive, distributable html reports, as well as summary csv files and consensus poliovirus FASTA sequences. The reports describe each nanopore sequencing run with interpretable plots enabling researchers to easily detect the presence of poliovirus in samples and quickly disseminate their results.

bioinformatics↗

bioRxiv2020

Rapid and sensitive direct detection and identification of poliovirus from stool and environmental surveillance samples using nanopore sequencing

Global poliovirus surveillance involves virus isolation from stool and environmental samples, intratypic differential (ITD) by PCR and sequencing of the VP1 region to distinguish vaccine (Sabin), vaccine-derived and wild-type polioviruses and ensure an appropriate response. This cell-culture algorithm takes 2-3 weeks on average between sample receipt and sequencing. Direct detection of viral RNA using PCR allows faster detection but has traditionally faced challenges related to poor sensitivity and difficulties in sequencing common samples containing poliovirus and enterovirus mixtures. We present a nested PCR and nanopore sequencing protocol that allows rapid (<3 days) and sensitive direct detection and sequencing of polioviruses in stool and environmental samples. We developed barcoded primers and a real-time analysis platform that generate accurate VP1 consensus sequences from multiplexed samples. The sensitivity and specificity compared with cell-culture were 90.9% (95% Confidence Interval: 75.7-98.1%) and 99.2% (95.5-100.0%) for wild-type 1 poliovirus, 92.5% (79.6-98.4%) and 98.7% (95.4-99.8%) for vaccine and vaccine-derived serotype 2 poliovirus, and 88.3% (81.2-93.5%) and 93.2% (88.6-96.3%) for Sabin 1 and 3 poliovirus alone or in mixtures when tested on 155 stool samples in Pakistan. Variant analysis of sequencing reads also allowed identification of polioviruses and enteroviruses in artificial mixtures and was able to distinguish complex mixtures of polioviruses in environmental samples. The median identity of consensus nanopore sequences with Sanger or Illumina sequences from the same samples was >99.9%. This novel method shows promise as a faster and safer alternative to cell-culture for the detection and real-time sequencing of polioviruses in stool and environmental samples.

microbiology↗