MOTIVATION: Unique molecular identifiers (UMIs) enable efficient error correction in amplicon sequencing but UMI-aware analysis workflows for long-read sequencing and particularly for nanopore data are still sparse. Existing approaches lack portability, real-time sequencing support, GPU acceleration, and efficient use of resources. RESULTS: We present umi-pipeline-nf, a portable, fully containerized, modular and scalable workflow to create single-molecule consensus sequences from UMI-tagged long-read nanopore amplicon data. Umi-pipeline-nf supports flexible UMI-designs and is built in Nextflow DSL2 for seamless deployment across computing platforms and a high degree of parallelization, allowing analysis of several targets at once. It scales linearly from single samples to large cohorts, outperforming existing tools in efficiency and flexibility. Additionally, we integrated real-time read processing, robust UMI clustering, and GPU-accelerated consensus polishing. Umi-pipeline-nf supports two different polishing strategies [reference sequence-based and partial order alignment (POA)-based]. Implementation of GPU-accelerated, reference sequence-based polishing results in up to 100-fold runtime improvements and reduced usage of computational resources, compared to other UMI analysis pipelines and POA-based polishing. AVAILABILITY AND IMPLEMENTATION: The umi-pipeline-nf analysis pipeline and test data are available at https://github.com/genepi/umi-pipeline-nf, and a frozen snapshot is available at DOI: 10.5281/zenodo.18607956. Scripts and configuration files for the analyses in the present manuscript can be found at https://github.com/AmstlerStephan/umi-pipeline-nf_Paper.

BACKGROUND: High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects. DESCRIPTION: We developed a database system for an efficient combination and management of phenotypes and genotypes (eCOMPAGT) deriving from genetic epidemiological studies. eCOMPAGT securely stores and manages genotype and phenotype data and enables different user modes with different rights. Special attention was drawn on the import of data deriving from TaqMan and SNPlex genotyping assays. However, the database solution is adjustable to other genotyping systems by programming additional interfaces. Further important features are the scalability of the database and an export interface to statistical software. CONCLUSION: eCOMPAGT can store, administer and connect phenotype data with all kinds of genotype data and is available as a downloadable version at (http://dbis-informatik.uibk.ac.at/ecompagt).