GI-Cluster: Detecting genomic islands via consensus clustering on multiple features

Abstract

The accurate detection of genomic islands (GIs) in microbial genomes is important for both evolutionary study and medical research, because GIs may promote genome evolution and contain genes involved in pathogenesis. Various computational methods have been developed to predict GIs over the years. However, most of them cannot make full use of GI-associated features to achieve desirable performance. Additionally, many methods cannot be directly applied to newly sequenced genomes. We develop a new method called GI-Cluster, which provides an effective way to integrate multiple GI-related features via consensus clustering. GI-Cluster does not require training datasets or existing genome annotations, but it can still achieve comparable or better performance than supervised learning methods in comprehensive evaluations. Moreover, GI-Cluster is widely applicable, either to complete and incomplete genomes or to initial GI predictions from other programs. GI-Cluster also provides plots to visualize the distribution of predicted GIs and related features. GI-Cluster is available at https://github.com/icelu/GI_Cluster.

Keywords: Genomic islands; consensus clustering; unsupervised learning.