# SWGTS—a platform for stream-based host DNA depletion

## TL;DR

SWGTS is a new platform that removes human DNA sequences from microbial data in real-time during transfer, reducing privacy risks.

## Contribution

SWGTS introduces a stream-based host DNA depletion system that limits stored human data and ensures privacy during data transfer.

## Key findings

- SWGTS can process data at up to 1.65 megabases per second while maintaining high filtering accuracy.
- The platform limits the number of SNPs in the buffer to mitigate re-identification risks, with thresholds as low as 10 or 100.
- SWGTS is implemented with Docker, Redis, and Traefik, requiring less than 8 Gb of RAM for deployment.

## Abstract

Microbial sequencing data from clinical samples is often contaminated with human sequences, which have to be removed prior to sharing. Existing methods for human read removal, however, are applicable only after the target dataset has been retrieved in its entirety, putting the recipient at least temporarily in control of a potentially identifiable genetic dataset with potential implications under regulatory frameworks such as the GDPR. In some instances, the ability to carry out stream-based host depletion as part of the data transfer process may be preferable.

We present SWGTS, a client–server application for the transfer and stream-based host depletion of sequencing reads. SWGTS enforces a robust upper bound on the maximum amount of human genetic data from any one client held in memory at any point in time by storing all incoming sequencing data in a limited-size, client-specific intermediate processing buffer, and by throttling the rate of incoming data if it exceeds the speed of host depletion carried out on the SWGTS server in the background. SWGTS exposes a HTTP–REST interface, is implemented using docker-compose, Redis and traefik, and requires less than 8 Gb of RAM for deployment. We demonstrate high filtering accuracy of SWGTS; incoming data transfer rates of up to 1.65 megabases per second in a conservative configuration; and mitigation of re-identification risks by the ability to limit the number of SNPs present on a popular population-scale genotyping array covered by reads in the SWGTS buffer to a low user-defined number, such as 10 or 100.

SWGTS is available on GitHub: https://github.com/AlBi-HHU/swgts (https://doi.org/10.5281/zenodo.10891052). The repository also contains a jupyter notebook that can be used to reproduce all the benchmarks used in this article. All datasets used for benchmarking are publicly available.