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Ford, C. T.

Publications and source records attributed to Ford, C. T..

2 recordsLinked to original sources
bioRxiv2019

StrainHub: A phylogenetic tool to construct pathogen transmission networks

In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g., host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in meta-data, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree, and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.\n\nAvailabilityhttps://github.com/abschneider/StrainHub and strainhub.io

bioinformatics↗
bioRxiv2019

Prediction of the Effect of Naturally Occurring Missense Mutations on Cellular N-Acetyl-Glucosaminidase Enzymatic Activity

In 2015, the Critical Assessment of Genome Interpretation (CAGI) proposed a challenge to devise a computational method for predicting the phenotypic consequences of genetic variants of a lysosomal hydrolase enzyme known as -N-acetylglucosaminidase (NAGLU). In 2014, the Human Gene Mutation Database released that 153 NAGLU mutations associated with MPS IIIB and 90 of them are missense mutations. The ExAC dataset catalogued 189 missense mutations NAGLU based on exome sequence data from about 60,000 individual and 24 of them are known to be disease associated. Biotechnology company, BioMarin, has quantified the relative functionality of NAGLU for the remaining subset of 165 missense mutations. For this particular challenge, we examined the subset missense mutations within the ExAC dataset and predicted the probability of a given mutation being deleterious and relating this measure to the capacity of enzymatic activity. In doing so, we hoped to learn the degree to which changes in amino acid physicochemical properties are tolerable for NAGLU function.\n\nAmino acid substitution (AAS) prediction methods are mainly based on the sequence and structure information. Simple comparisons between different AAS methods are not only difficult, but also irrational because each method was tested on various datasets and based on varied versions of databases. Currently, several AAS prediction methods have been introduced. PolyPhen-2, an updated version of PolyPhen, is a tool used to predict possible impacts of an amino acid substitution on the structure and function. Users are required to provide protein or SNP identifiers, protein sequences, substitution positions, etc. A score is provided, ranging from 0 to 1, corresponding to the probability of a mutation resulting in no functionality for the enzyme\n\nOnce the probability scores were generated, the dataset was then run through multiple machine learning algorithms to generate an applicable model for predicting the enzymatic activity of MPS IIIB-related mutations. This prediction was generated using the PolyPhen-2 probability score and other information about the mutation (amino acid type, location, allele frequency, etc.) as input feature variables. This generated a predicted aggregate score for each mutation, which was then reported back to CAGI. The results of the analysis are significant enough to hold confidence that the scores are decent predictors of enzymatic activity given a mutation in the NAGLU amino acid sequence.

bioinformatics↗