Week 2: Literature Review

While reading the existing research on nanopore basecalling, three papers stood out as most relevant to what I am trying to do. The first is a 2018 paper by Teng et al., which introduced Chiron, one of the first basecallers to use deep learning to directly convert raw nanopore signals into nucleotide sequences. Before this, most pipelines relied on Hidden Markov Models, which required many manual signal-processing steps in between. Chiron showed that an end-to-end neural network approach could beat those older methods. What is relevant to my project is that the authors never considered how computationally expensive the model was, and it was built assuming you had powerful hardware to run the model on.

The second paper, from Frensel, Al-Ars, and Hofstee in 2024, is the most directly related to my project. They applied a technique called structured sparsity to a basecalling model, which essentially means training the model to get rid of weights that are not contributing much, shrinking the model without killing the accuracy. Their results were promising and suggest that compression can work in this domain. The issue is that all their testing was done on GPUs, so the efficiency gains they measured apply only to high-end hardware. Nobody has seriously tested whether these kinds of optimizations actually help when you are running on a low-power CPU.

The third paper is the 2024 BaseNet paper by Li et al., which proposes using a transformer architecture for basecalling instead of the LSTM-based models that most production basecallers use. The argument is that transformers handle long-range dependencies in the signal better. That might be true, but transformers are also expensive to run, and again, the paper only tests on powerful hardware. Looking at these three sources together, the gap becomes obvious. The field has gotten much better at making accurate models, but no one has built something that actually runs well on a small device like a Raspberry Pi. My project aims to build a low-resource model that maintains accuracy even with limited compute power.