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BAMboozle removes genetic variation from human sequence data for open data sharing

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  • Christoph Ziegenhain

    (Karolinska Institute)

  • Rickard Sandberg

    (Karolinska Institute)

Abstract

The risks associated with re-identification of human genetic data are severely limiting open data sharing in life sciences, even in studies where donor-related genetic variant information is not of primary interest. Here, we developed BAMboozle, a versatile tool to eliminate critical types of sensitive genetic information in human sequence data by reverting aligned reads to the genome reference sequence. Applying BAMboozle to functional genomics data, such as single-cell RNA-seq (scRNA-seq) and scATAC-seq datasets, confirmed the removal of donor-related single nucleotide polymorphisms (SNPs) and indels in a manner that did not disclose the altered positions. Importantly, BAMboozle only removes the genetic sequence variants of the sample (i.e., donor) while preserving other important aspects of the raw sequence data. For example, BAMboozled scRNA-seq data contained accurate cell-type associated gene expression signatures, splice kinetic information, and can be used for methods benchmarking. Altogether, BAMboozle efficiently removes genetic variation in aligned sequence data, which represents a step forward towards open data sharing in many areas of genomics where the genetic variant information is not of primary interest.

Suggested Citation

  • Christoph Ziegenhain & Rickard Sandberg, 2021. "BAMboozle removes genetic variation from human sequence data for open data sharing," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26152-8
    DOI: 10.1038/s41467-021-26152-8
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    References listed on IDEAS

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    Cited by:

    1. Alexander Bernier & Hanshi Liu & Bartha Maria Knoppers, 2021. "Computational tools for genomic data de-identification: facilitating data protection law compliance," Nature Communications, Nature, vol. 12(1), pages 1-3, December.
    2. Tao Qi & Fangzhao Wu & Chuhan Wu & Liang He & Yongfeng Huang & Xing Xie, 2023. "Differentially private knowledge transfer for federated learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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