Abstract
Cryptosporidium, a protozoan parasite of significant public health concern, is responsible for severe diarrhoeal disease, particularly in immunocompromised individuals and young children in resource-limited settings. Analysis of whole-genome next-generation sequencing (NGS) data is critical in improving our understanding of Cryptosporidium epidemiology, transmission and diversity. However, effective analysis of NGS data in a public health context necessitates the development of robust, validated computational tools. We present Parapipe, an ISO-accreditable bioinformatic pipeline for high-throughput analysis of NGS data from Cryptosporidium and related taxa. Built using Nextflow DSL2 and containerized with Singularity, Parapipe is modular, portable, scalable and designed for use by public health laboratories. Using both simulated and real Cryptosporidium datasets, we demonstrate the power of Parapipe's genomic analysis for generating epidemiological insights. We highlight how whole-genome analysis yields substantially greater phylogenetic resolution than conventional gp60 molecular typing in Cryptosporidium parvum. Uniquely, Parapipe facilitates the integration of mixed infection analysis and phylogenomic clustering with epidemiological metadata, representing a powerful tool in the investigation of complex transmission pathways and identification of outbreak sources. Parapipe significantly advances genomic surveillance of Cryptosporidium, offering a streamlined, reproducible analytical framework. By automating a complex workflow and delivering detailed genomic characterization, Parapipe provides a valuable tool for public health agencies and researchers, supporting efforts to mitigate the global burden of cryptosporidiosis.