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Protecting human genetic data is crucial. The nf-core/detaxizer pipeline effectively identifies and filters human sequences in metagenomic datasets, enhancing data privacy and security during sharing.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Privacy

Background:

  • Human genetic data privacy is critical, especially in large datasets like metagenomics.
  • Metagenomic, environmental, and contaminated lab samples may contain human DNA, posing privacy risks.
  • Complete removal of human DNA while preserving non-human data is challenging.

Purpose of the Study:

  • To develop and evaluate a robust pipeline for identifying and filtering human sequences in biological datasets.
  • To enhance the security and privacy of metagenomic and other genetic data for sharing and publication.
  • To provide a flexible tool for taxonomic classification and filtering beyond just human DNA.

Main Methods:

  • Development of the nf-core/detaxizer pipeline using Nextflow.
  • Utilized Kraken2 for taxonomic classification and bbmap/bbduk for read filtering.
  • Benchmarking against Hostile and CLEAN for human read filtering efficacy.

Main Results:

  • nf-core/detaxizer successfully identifies and can filter *Homo sapiens* reads.
  • Pipeline performance was benchmarked, showing significant differences in human DNA removal and microbial data loss based on tool and database choices.
  • The choice of tool and database can impact results by an order of magnitude.

Conclusions:

  • nf-core/detaxizer offers a valuable solution for secure preprocessing of genetic data.
  • The pipeline adheres to best practices, including containerized dependencies for easy installation.
  • Open-source availability under MIT license promotes wider adoption and collaboration.