Improving protein domain classification for third-generation sequencing reads using deep learning

Background With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads. Results In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification. Conclusions In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

Automated summary

ProDOMA is a deep learning model that performs protein domain classification for TGS reads, outperforming HMMER and DeepFam. The model uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations and formulates the problem as an open-set problem, allowing it to reject reads not containing the targeted domains.