DeepSelectNet: deep neural network based selective sequencing for oxford nanopore sequencing

Abstract

Abstract Background Nanopore sequencing allows selective sequencing, the ability to programmatically reject unwanted reads in a sample. Selective sequencing has many present and future applications in genomics research and the classification of species from a pool of species is an example. Existing methods for selective sequencing for species classification are still immature and the accuracy highly varies depending on the datasets. For the five datasets we tested, the accuracy of existing methods varied in the range of $$\sim$$ ∼ 77 to 97% (average accuracy 89% (average $$\sim$$ ∼ 95%). In terms of execution performance, DeepSelectNet outperformed SquiggleNet by $$\sim$$ ∼ 13% on average. Thus, DeepSelectNet is a practically viable method to improve the effectiveness of selective sequencing. Conclusions Compared to base alignment and deep learning predecessors, DeepSelectNet can significantly improve the accuracy to enable real-time species classification using selective sequencing. The source code of DeepSelectNet is available at https://github.com/AnjanaSenanayake/DeepSelectNet .

Keywords

• Selective sequencing
• Deep learning
• Nanopore