Jody Phelan

Projects 30

• CompMutTB View project →
• Genomic-surveillance of-Anopheles-mosquitoes-on-the-Bijagós-Archipelago View project →
• Global Drug resistance study View project →
• New reference genomes for Plasmodium ovale spp will assist large-scale genomic studies of these neglected malaria parasites. View project →
• Next generation targeted amplicon sequencing for insecticide resistance mutations in Aedes aegypti View project →
• Barcoding method for the genetic surveillance of insecticide resistance and species identification in Anopheles gambiae View project →
• A molecular barcode to inform the geographical origin and transmission dynamics of Plasmodium vivax View project →
• COVID-profiler View project →
• COVID-19 serological and vaccine targets View project →
• Using deep learning to identify recent positive selection in malaria parasite sequence data View project →
• Environmental detection, quantification, and molecular characterization of Anopheles stephensi and Aedes aegypti View project →
• Distinctive genetic structure and selection patterns in Plasmodium vivax from South Asia and East Africa View project →
• Population dynamics and drug resistance mutations in Guinea-Bissau View project →
• Drug resistance surveillance of Plasmodium falciparum in Ouélessébougou, Mali View project →
• Flavivirus integrations in Aedes aegypti View project →
• Geographical classification of malaria parasites through applying machine learning View project →
• Identification of two insecticide resistance markers in Ethiopian Anopheles stephensi mosquitoes View project →
• Analysis of Klebsiella pneumoniae from Portuguese hospitals reveals insights into circulating amr View project →
• A phylogenomic approach for the analysis of colistin resistance-associated genes in Klebsiella pneumoniae View project →
• Extended-spectrum beta-lactam and carbapenem-resistant Klebsiella pneumoniae polyclonal outbreak View project →
• Genomic Epidemiology of Carbapenemase Producing Klebsiella pneumoniae Strains Enables the Detection of a Misidentified Klebsiella variicola KPC-3 View project →
• Mixed infections in genotypic drug-resistant Mycobacterium tuberculosis (GMM4TB) View project →
• Malaria-MOI, A flexible and scalable tool for predicting multiplicity of infection in malaria parasites. View project →
• Malaria-Profiler View project →
• MTB Sequencing Variability View project →
• PE/PPE Genes View project →
• primedRPA, primer design for recombinase polymerase amplification assays View project →
• TB-ML Read summary →

  TB-ML

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  Motivation: Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test or reproduce published models.

  Results: We packaged a number of published and unpublished ML models for predicting AMR of M. tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard, we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic.

• TB-Profiler View project →
• Using an ecological study approach to investigate potential co-acting risk factors for an unusual pattern of microcephaly in Brazil View project →