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

ABSTRACT:

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

SUBMITTER: Du N 

PROVIDER: S-EPMC8033682 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature