Biological sequence analysis relies on the ability to denoise the imprecise
output of sequencing platforms. We consider a common setting where a short
sequence is read out repeatedly using a high-throughput long-read platform to
generate multiple subreads, or noisy observations of the same sequence.
Denoising these subreads with alignment-based approaches often fails when too
few subreads are available or error rates are too high. In this paper, we
propose a novel method for blindly denoising sets of sequences without directly
observing clean source sequence labels. Our method, Self-Supervised Set
Learning (SSSL), gathers subreads together in an embedding space and estimates
a single set embedding as the midpoint of the subreads in both the latent and
sequence spaces. This set embedding represents the "average" of the subreads
and can be decoded into a prediction of the clean sequence. In experiments on
simulated long-read DNA data, SSSL methods denoise small reads of $\leq 6$
subreads with 17% fewer errors and large reads of $>6$ subreads with 8% fewer
errors compared to the best baseline. On a real dataset of antibody sequences,
SSSL improves over baselines on two self-supervised metrics, with a significant
improvement on difficult small reads that comprise over 60% of the test set. By
accurately denoising these reads, SSSL promises to better realize the potential
of high-throughput DNA sequencing data for downstream scientific applications.

无监督集合学习（SSSL）方法能够在没有直接观察到干净的源序列标签的情况下，将亚读物聚集在嵌入空间中并估计单个集合嵌入作为亚读物在潜在空间和序列空间中的中点，从而实现对生物序列进行准确去噪。该方法在模拟的长读长 DNA 数据和真实的抗体序列数据集上表现出优于基准方法的去噪性能，为高通量 DNA 测序数据在下游科学应用中的潜力提供更好的实现。