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Title: Transformer‐based DNA methylation detection on ionic signals from Oxford Nanopore sequencing data
Background

Oxford 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).

Method

In 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.

Results

Compared 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.

Conclusion

We demonstrated the ability of Transformers to detect methylation on ionic signal data.

 
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Award ID(s):
1912191
NSF-PAR ID:
10502800
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
NSF-PAR
Date Published:
Journal Name:
Quantitative Biology
Volume:
11
Issue:
3
ISSN:
2095-4689
Page Range / eLocation ID:
287 to 296
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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