Identifying genomic islands with deep neural networks

BMC Genomics

卷:22

Methodology

Fangfang Xia¹  Rick Stevens²  Rida Assaf³ 

[1] Computing Environment and Life Sciences Division, Argonne National Laboratory, S. Cass Ave., 60439, Lemont, USA;Data Science and Learning Division, Argonne National Laboratory, S. Cass Ave., 60439, Lemont, USA;

[2] Computing Environment and Life Sciences Division, Argonne National Laboratory, S. Cass Ave., 60439, Lemont, USA;The University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, S. Ellis Ave., 60637, Chicago, USA;

[3] Department of Computer Science, University of Chicago, S. Ellis Ave., 60637, Chicago, USA;

关键词: Genomic island;    Deep learning;    Transfer learning;    Computer vision;    Inception V3;   

DOI  :  10.1186/s12864-021-07575-5

 received in 2021-03-07, accepted in 2021-03-31,  发布年份 2021

来源: Springer

【 摘 要 】

BackgroundHorizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types.ResultsWe propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools.ConclusionsWe used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

【 授权许可】

CC BY   
© The Author(s) 2021

【 图 表 】

Fig. 4

Fig. 3

Fig. 2

Fig. 1