Dataset Information

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.

ABSTRACT: The Global Polio Eradication Initiative uses an outbreak response protocol that defines type 2 Sabin or Sabin-like virus as those with 0-5 nucleotides diverging from their parental strain in the complete VP1 genomic region. Sabin or Sabin-like viruses share highly similar genome sequences, regardless of their origin. Thus, it is challenging to distinguish viruses at a higher resolution to detect polio clusters or trace sources for local transmissions of viruses at an early stage. To identify type 2 Sabin or Sabin-like sources and improve our ability to map viral sources to campaigns during the polio endgame, we investigated the feasibility of a new method for genetic sequence analysis. We named the method Major Minor Variation Clustering (MMVC), which uses a network model to simultaneously incorporate sequence similarity in major and minor variants in addition to onset dates to detect fine-scale polio clusters. Each identified cluster represents a collection of sequences that are highly similar in both major and minor variants, enabling the discovery of new links between viruses. By applying the method to a published data set collected in Nigeria during 2009-2012, we found that clusters identified using this method have several improvements over clusters derived from a phylogenetic tree approach. Integrative data analysis reveals that sequences in the same cluster have greater genomic similarities and better agreement with onset dates. As a complement to current phylogenetic tree approaches, MMVC has the potential to improve epidemiological surveillance and investigation precision to guide polio eradication.

SUBMITTER: Tan J

PROVIDER: S-EPMC11299649 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.

Tan Jiahui J Zhao Yutong Y Burns Cara C CC Tian Dechao D Zhao Kun K

Journal of computational biology : a journal of computational molecular cell biology 20220916 4

The Global Polio Eradication Initiative uses an outbreak response protocol that defines type 2 Sabin or Sabin-like virus as those with 0-5 nucleotides diverging from their parental strain in the complete VP1 genomic region. Sabin or Sabin-like viruses share highly similar genome sequences, regardless of their origin. Thus, it is challenging to distinguish viruses at a higher resolution to detect polio clusters or trace sources for local transmissions of viruses at an early stage. To identify typ ...[more]

PMID: 36112351

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Project description:Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Dataset Information

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.

Publications

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.

Similar Datasets

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