Publication-ready single nucleotide polymorphism visualisation with snipit.

SUMMARY: Snipit is an analysis and visualisation tool designed for summarising single nucleotide polymorphisms (SNPs) in sequences in comparison to a reference sequence. This tool efficiently catalogues nucleotide and amino acid differences, enabling clear comparisons through customizable, publication-ready figures. With features such as configurable colour palettes, customizable record sorting, and the ability to output figures in multiple formats, snipit offers a user-friendly interface for researchers across diverse disciplines. Additionally, snipit includes a specialised recombi-mode for illustrating recombination patterns, which can highlight otherwise often difficult-to-detect relationships between sequences. AVAILABILITY AND IMPLEMENTATION: Snipit is an open-source python-based tool that is hosted on GitHub under a GNU-GPL 3.0 licence (https://github.com/aineniamh/snipit). It can be installed from PyPi using pip. Source code and additional documentation can be found on the GitHub repository.

Molecular epidemiology of recurrent zoonotic transmission of mpox virus in West Africa.

Nigeria and Cameroon reported their first mpox cases in over three decades in 2017 and 2018 respectively. The outbreak in Nigeria is recognised as an ongoing human epidemic. However, owing to sparse surveillance and genomic data, it is not known whether the increase in cases in Cameroon is driven by zoonotic or sustained human transmission. Notably, the frequency of zoonotic transmission remains unknown in both Cameroon and Nigeria. To address these uncertainties, we investigated the zoonotic transmission dynamics of the mpox virus (MPXV) in Cameroon and Nigeria, with a particular focus on the border regions. We show that in these regions mpox cases are still driven by zoonotic transmission of a newly identified Clade IIb.1. We identify two distinct zoonotic lineages that circulate across the Nigeria-Cameroon border, with evidence of recent and historic cross border dissemination. Our findings support that the complex cross-border forest ecosystems likely hosts shared animal populations that drive cross-border viral spread, which is likely where extant Clade IIb originated. We identify that the closest zoonotic outgroup to the human epidemic circulated in southern Nigeria in October 2013. We also show that the zoonotic precursor lineage circulated in an animal population in southern Nigeria for more than 45 years. This supports findings that southern Nigeria was the origin of the human epidemic. Our study highlights the ongoing MPXV zoonotic transmission in Cameroon and Nigeria, underscoring the continuous risk of MPXV (re)emergence.

Genomic epidemiology uncovers the timing and origin of the emergence of mpox in humans.

Five years before the 2022-2023 global mpox outbreak Nigeria reported its first cases in nearly 40 years, with the ongoing epidemic since driven by sustained human-to-human transmission. However, limited genomic data has left questions about the timing and origin of the mpox virus' (MPXV) emergence. Here we generated 112 MPXV genomes from Nigeria from 2021-2023. We identify the closest zoonotic outgroup to the human epidemic in southern Nigeria, and estimate that the lineage transmitting from human-to-human emerged around July 2014, circulating cryptically until detected in September 2017. The epidemic originated in Southern Nigeria, particularly Rivers State, which also acted as a persistent and dominant source of viral dissemination to other states. We show that APOBEC3 activity increased MPXV's evolutionary rate twenty-fold during human-to-human transmission. We also show how Delphy, a tool for near-real-time Bayesian phylogenetics, can aid rapid outbreak analytics. Our study sheds light on MPXV's establishment in West Africa before the 2022-2023 global outbreak and highlights the need for improved pathogen surveillance and response.

Sustained human outbreak of a new MPXV clade I lineage in eastern Democratic Republic of the Congo.

Outbreaks of monkeypox (mpox) have historically resulted from zoonotic spillover of clade I monkeypox virus (MPXV) in Central Africa and clade II MPXV in West Africa. In 2022, subclade IIb caused a global epidemic linked to transmission through sexual contact. Here we describe the epidemiological and genomic features of an mpox outbreak in a mining region in eastern Democratic Republic of the Congo, caused by clade I MPXV. Surveillance data collected between September 2023 and January 2024 identified 241 suspected cases. Genomic analysis demonstrates a distinct clade I lineage divergent from previously circulating strains in the Democratic Republic of the Congo. Of the 108 polymerase chain reaction-confirmed mpox cases, the median age of individuals was 22 years, 51.9% were female and 29% were sex workers, suggesting a potential role for sexual transmission. The predominance of APOBEC3-type mutations and the estimated emergence time around mid-September 2023 imply recent sustained human-to-human transmission.

Epigenetic age estimation of wild mice using faecal samples.

Age is a key parameter in population ecology, with a myriad of biological processes changing with age as organisms develop in early life then later senesce. As age is often hard to accurately measure with non-lethal methods, epigenetic methods of age estimation (epigenetic clocks) have become a popular tool in animal ecology and are often developed or calibrated using captive animals of known age. However, studies typically rely on invasive blood or tissue samples, which limit their application in more sensitive or elusive species. Moreover, few studies have directly assessed how methylation patterns and epigenetic age estimates compare across environmental contexts (e.g. captive or laboratory-based vs. wild animals). Here, we built a targeted epigenetic clock from laboratory house mice (strain C57BL/6, Mus musculus) using DNA from non-invasive faecal samples, and then used it to estimate age in a population of wild mice (Mus musculus domesticus) of unknown age. This laboratory mouse-derived epigenetic clock accurately predicted adult wild mice to be older than juveniles and showed that wild mice typically increased in epigenetic age over time, but with wide variation in epigenetic ageing rate among individuals. Our results also suggested that, for a given body mass, wild mice had higher methylation across targeted CpG sites than laboratory mice (and consistently higher epigenetic age estimates as a result), even among the smallest, juvenile mice. This suggests wild and laboratory mice may display different CpG methylation levels from very early in life and indicates caution is needed when developing epigenetic clocks on laboratory animals and applying them in the wild.

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. Using 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), a 'sequencing reads-to-report' solution to aid routine poliovirus testing of both stool and environmental samples and alleviate the bioinformatic bottleneck that often exists for laboratories 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 reports, as well as summary comma-separated values files and consensus poliovirus FASTA sequences. Piranha optionally provides phylogenetic analysis, with the ability to incorporate a local database, processing from raw sequencing reads to an interactive, annotated phylogeny in a single step. 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. Poliovirus eradication efforts are hindered by the lack of real-time detection and reporting, and piranha can be used to complement direct detection sequencing approaches.

A framework for automated scalable designation of viral pathogen lineages from genomic data.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.

SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method.

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.