Morin, K.; Hetrick, E.; Shrivastava, A.; Tran, E.; Cartee, J. C.; Hebrank, K.; Gernert, K.; Schmerer, M.; Joseph, S. J.

Antimicrobial-resistant Neisseria gonorrhoeae (Ng) poses a growing global public health threat. Existing tools available for Ng genome analysis carry notable limitations, including incomplete re-sistance marker coverage, absence of species identification, and lack of phylogenetic capability. We developed neissflow, a highly parallelized Nextflow pipeline for Ng genome analysis that integrates five subworkflows: read preprocessing, species identification, de novo assembly, antimicrobial re-sistance (AMR) profiling, and recombination-aware phylogenetic analysis with outbreak detection. neissflow performs extensive quality control on reads, assemblies, variant calls, and phylogenetic results. Validation was performed on two datasets: a mixed-species dataset (n=158; 105 Ng and 53 non-gonococcal species) for sensitivity/specificity assessment, and a reproducibility dataset (n=283 replicate sequences from 17 reference strains) for consistency and phylogenetic validation. neiss-flow achieved 100% sensitivity and specificity for Ng species identification compared with MALDI-TOF and PubMLST methods. All nine AMR and typing analytes demonstrated [≥]98.1% concordance with PubMLST genotype calls. Genotype-phenotype validation confirmed perfect concordance for key resistance determinants including gyrA mutations with ciprofloxacin resistance, 23S rRNA mu-tations with high-level azithromycin resistance, and tetM plasmid gene with high-level tetracycline resistance. Reproducibility analysis demonstrated 99.97% concordance across 3,093 analyte calls. Phylogenetic validation demonstrated 100% accuracy for both strain-level and intra-MLST clustering. neissflow is a robust, accessible, and standardized pipeline, positioning it as a valuable tool for public health laboratories engaged in Ng AMR monitoring and outbreak investigations. Keywords: Neisseria gonorrhoeae, antimicrobial resistance, whole-genome sequencing, bioinfor-matics pipeline, genomic surveillance, Nextflow, public health, outbreak detection