DeepHost : phage host prediction with convolutional neural network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article numberbbab385
Journal / PublicationBriefings in Bioinformatics
Online published22 Sep 2021
Publication statusOnline published - 22 Sep 2021

Abstract

Next-generation sequencing expands the known phage genomes rapidly. Unlike culture-based methods, the hosts of phages discovered from next-generation sequencing data remain uncharacterized. The high diversity of the phage genomes makes the host assignment task challenging. To solve the issue, we proposed a phage host prediction tool—DeepHost. To encode the phage genomes into matrices, we design a genome encoding method that applied various spaced k-mer pairs to tolerate sequence variations, including insertion, deletions, and mutations. DeepHost applies a convolutional neural network to predict host taxonomies. DeepHost achieves the prediction accuracy of 96.05% at the genus level (72 taxonomies) and 90.78% at the species level (118 taxonomies), which outperforms the existing phage host prediction tools by 10.16–30.48% and achieves comparable results to BLAST. For the genomes without hits in BLAST, DeepHost obtains the accuracy of 38.00% at the genus level and 26.47% at the species level, making it suitable for genomes of less homologous sequences with the existing datasets. DeepHost is alignment-free, and it is faster than BLAST, especially for large datasets. DeepHost is available at https://github.com/deepomicslab/DeepHost.

Research Area(s)

  • phage–host relationship, convolutional neural network, genome encoding