Affiliation:
1. Department of Mathematics and Computer Science Tougaloo College Jackson MS 39174 USA
2. Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children’s Hospital of Philadelphia Philadelphia PA 19104 USA
3. Department of Pathology and Laboratory Medicine University of Pennsylvania Philadelphia PA 19104 USA
Abstract
Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. We evaluated this idea using real data sets (Escherichia coli data and the human genome NA12878 sequenced by Simpson et al.) and demonstrated the ability of Transformers to detect methylation on ionic signal data.BackgroundOxford Nanopore long‐read sequencing technology addresses current limitations for DNA methylation detection that are inherent in short‐read bisulfite sequencing or methylation microarrays. A number of analytical tools, such as Nanopolish, Guppy/Tombo and DeepMod, have been developed to detect DNA methylation on Nanopore data. However, additional improvements can be made in computational efficiency, prediction accuracy, and contextual interpretation on complex genomics regions (such as repetitive regions, low GC density regions).MethodIn the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing.ResultsCompared to traditional deep‐learning method such as convolutional neural network (CNN) and recurrent neural network (RNN), Transformer may have specific advantages in DNA methylation detection, because the self‐attention mechanism can assist the relationship detection between bases that are far from each other and pay more attention to important bases that carry characteristic methylation‐specific signals within a specific sequence context.ConclusionWe demonstrated the ability of Transformers to detect methylation on ionic signal data.
Subject
Applied Mathematics,Computer Science Applications,Biochemistry, Genetics and Molecular Biology (miscellaneous),Modeling and Simulation