Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

ABSTRACT: With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challenging task. Recently deep learning-based methods were shown to have the ability to predict gene regulatory regions from genome sequences, promising to aid the interpretation of genomic sequence data. Here, we report an improvement of the prediction accuracy for gene regulatory regions by using the design of convolution layers that efficiently process genomic sequence information, and developed a software, DeepGMAP, to train and compare different deep learning-based models (https://github.com/koonimaru/DeepGMAP). First, we demonstrate that our co

SUBMITTER: Onimaru K 

PROVIDER: S-EPMC7377372 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature