Issue 31, 2024

Machine learning empowered next generation DNA sequencing: perspective and prospectus

Abstract

The pursuit of ultra-rapid, cost-effective, and accurate DNA sequencing is a highly sought after aspect of personalized medicine development. With recent advancements, mainstream machine learning (ML) algorithms hold immense promise for high throughput DNA sequencing at the single nucleotide level. While ML has revolutionized multiple domains of nanoscience and nanotechnology, its implementation in DNA sequencing is still in its preliminary stages. ML-aided DNA sequencing is especially appealing, as ML has the potential to decipher complex patterns and extract knowledge from complex datasets. Herein, we present a holistic framework of ML-aided next-generation DNA sequencing with domain knowledge to set directions toward the development of artificially intelligent DNA sequencers. This perspective focuses on the current state-of-the-art ML-aided DNA sequencing, exploring the opportunities as well as the future challenges in this field. In addition, we provide our personal viewpoints on the critical issues that require attention in the context of ML-aided DNA sequencing.

Graphical abstract: Machine learning empowered next generation DNA sequencing: perspective and prospectus

Article information

Article type
Perspective
Submitted
13 3 2024
Accepted
07 7 2024
First published
08 7 2024
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2024,15, 12169-12188

Machine learning empowered next generation DNA sequencing: perspective and prospectus

S. Mittal, M. K. Jena and B. Pathak, Chem. Sci., 2024, 15, 12169 DOI: 10.1039/D4SC01714E

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