This research presents using Hidden Markov Models to resolve DNA basecalling problems.
This paper proposes Hidden Markov Models (HMMs) as an approach to the DNA basecalling problem. The authors model the state emission densities using Artificial Neural Networks, and provide a modified Baum-Welch re-estimation procedure to perform training. Moreover, the authors develop a method that exploits consensus sequences to label training data, thus minimizing the need for hand-labeling. The results demonstrate the potential of these models and suggest further research. The authors also perform a careful study of the basecalling errors and propose alternative HMM topologies that might further improve performance. The authors conclude by suggesting further research directions.
Similar Publications
- Look Twice as Much as You Say: Scene Graph Contrastive Learning for Self-Supervised Image Caption Generation
- Deciphering Dismemberment Cuts: Statistical Relationships Between Incomplete Kerf Morphology and Saw Class Characteristics
- Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates Through Understanding and Predicting Difficulty