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- Implementation of a data analysis pipeline for the genetic characterization of non-seasonal influenza A WGS samples in the context of laboratory surveillance of viral outbreaksPublication . Pereira, João Luís Gomes; Sobral, Daniel Vieira Noro e Silva; Couto, Francisco José MoreiraBackground: Influenza A viruses (IAV) are rapidly evolving pathogens with high zoonotic and pandemic potential. Their segmented genome allows antigenic drift and reassortment, key drivers of adaptation and cross-species transmission. The ongoing H5Nx panzootic underscores the need for timely genomic surveillance to detect adaptive mutations, reassortments, and antiviral resistance. Existing frameworks such as the INSaFLU-TELEVIR platform, work well for seasonal strains but face challenges with non-seasonal IAV due to reference selection, representation bias, and database redundancies. Objectives: This project aimed to develop an automated pipeline for the genetic characterization of non-seasonal IAV whole-genome sequencing (WGS) samples. Goals were: (1) accurate identification of genomic segments, subtypes/genotypes, host origins and closely-related reference sequences per segment; (2) characterization of mutations of biological relevance, including host adaptation and antiviral resistance; (3) integration of results into user-friendly and machine-readable outputs. Methods: The pipeline, named AFluID (Automatic Influenza Identification pipeline), was implemented in Python and combined clustering (cd-hit), similarity search (BLAST), clade assignment (Nextclade), and mutation screening (FluMut). It was validated on curated datasets from NCBI, GISAID, EQA panels, and Portuguese outbreak samples resulting from an INSA-INIAV-IP cooperation. Results: AFluID rapidly identified IAV segments and subtypes across datasets, while its multi-feature design further streamlined the identification of closely-related references (and detection of reassortment events), along with clade classification, host/geographic inference, and identification of mutations potentially linked to adaptation, virulence, and resistance. Proof-of-concept analysis of outbreak samples confirmed applicability in real surveillance scenarios. Conclusions: AFluID addresses major limitations of current pipelines by offering an automated, scalable, and reproducible framework tailored for non-seasonal IAV. Although reassortment detection requires further refinement, the pipeline strengthens laboratory surveillance capacity and represents a step toward integration with global frameworks such as INSaFLU.