Optimization Methods for Efficient Nanopore DNA Basecalling

Abstract

Genomics, the study of an organism's complete set of DNA, including all of its genes, has revolutionized our understanding of biological processes and disease mechanisms. The field's rapid advancements have paved the way for personalized medicine, offering targeted therapies and improved healthcare outcomes. These advancements are a result of significant improvements in sequencing technology, bioinformatics, and computational power. Next-generation or long-read sequencing has reduced the cost and time required to sequence entire genomes, and Oxford Nanopore Technologies (ONT) sequencers provide 100–1000× longer contiguous reads, simplifying genome assembly. However, bioinformatics-driven advances in accuracy have come at the cost of high computational requirements because of the dependency on large deep neural networks (DNNs), and the basecalling step now takes 43% of the time in the nanopore sequencing pipeline.

This thesis addresses the large computational demands for high accuracy nanopore basecalling of nanopore reads. Bonito, ONT's research basecaller, and other basecallers use DNNs at their core. The five Long Short-Term Memory (LSTM) layers used by the basecaller are the primary bottleneck to more efficient basecalling, taking almost 90% of the whole model's execution time when basecalling a single read. To alleviate this bottleneck, three approaches are investigated: pruning, model architecture, and quantization. Preliminary results show that pruning is the most impactful approach and has not successfully been used in previous work.

We propose learning structured sparsity using a delayed masking penalty scheduler. By adapting and improving on previous work, each LSTM layer is able to learn its optimal size during training, simultaneously with learning to basecall accurately. The method is optimized for the basecalling application and can be generalized to other tasks. We find that the required number of computations in the LSTM layers can be significantly reduced by up to 21 times with a reduction in match rate of just 1.3% compared to the high accuracy Bonito model. Furthermore, the newly introduced penalty parameter can be tuned to find the optimal trade-off between compute and accuracy for users' requirements.

The results indicate that state-of-the-art basecalling models are overparameterized and that their size can be reduced drastically without significantly affecting accuracy. Future work is suggested to investigate the benefits of pruning the whole model, and to assess the feasibility of combining pruning with advanced quantization methods. This work helps increase the accessibility of nanopore DNA sequencing, broadening the reach and impact of this technology.