Abstract
Privacy is paramount in health data, particularly in human genetics, where information extends beyond individuals to their relatives. Metagenomic datasets contain substantial human genetic material, necessitating careful handling to mitigate data leakage risks when sharing or publishing. The same applies to genetic datasets from the environment or datasets from contaminated laboratory samples, although to a lesser extent. Completely removing human sequence data while retaining unbiased nonhuman reads is not achievable currently, but several tools exist. To address these topics, we developed nf-core/detaxizer, a nextflow-based pipeline that employs Kraken2 and bbmap/bbduk for taxonomic classification, identifying and optionally filtering Homo sapiens reads. Due to its generalized design, other taxa can also be classified and filtered. We benchmark its filtering efficacy for human reads against Hostile and CLEAN, demonstrating its utility for secure data preprocessing. The comparison showed that the choice of tool and database can result in differences of up to an order of magnitude in both the amount of human data not removed and the amount of microbial data mistakenly removed. As part of the nf-core initiative, nf-core/detaxizer adheres to best practices, leveraging containerized dependencies for streamlined installation. The source code is openly available under the MIT license: https://github.com/nf-core/detaxizer.