Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
This work addresses the bottleneck of matching DNA sequences to genomes in metagenomic profiling, which is critical for fields like healthcare and environmental science, though it is incremental as it builds on existing basecalling and classification methods.
The paper tackled the problem of identifying microbial species in genome sequencing by developing a method that performs basecalling and species classification simultaneously, achieving state-of-the-art basecalling accuracies and classification accuracies of 92.5% for top-1 and 98.9% for top-3 species on a dataset of 17 genomes.
The ability to quickly and accurately identify microbial species in a sample, known as metagenomic profiling, is critical across various fields, from healthcare to environmental science. This paper introduces a novel method to profile signals coming from sequencing devices in parallel with determining their nucleotide sequences, a process known as basecalling, via a multi-objective deep neural network for simultaneous basecalling and multi-class genome classification. We introduce a new loss strategy where losses for basecalling and classification are back-propagated separately, with model weights combined for the shared layers, and a pre-configured ranking strategy allowing top-K species accuracy, giving users flexibility to choose between higher accuracy or higher speed at identifying the species. We achieve state-of-the-art basecalling accuracies, while classification accuracies meet and exceed the results of state-of-the-art binary classifiers, attaining an average of 92.5%/98.9% accuracy at identifying the top-1/3 species among a total of 17 genomes in the Wick bacterial dataset. The work presented here has implications for future studies in metagenomic profiling by accelerating the bottleneck step of matching the DNA sequence to the correct genome.