BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains

PeerJ

Claus O. Wilke¹  Adam J. Hockenberry¹ 

[1] Department of Integrative Biology, The University of Texas, Austin, TX, United States of America;

关键词: Bacteriophage;    Machine learning;    Genomics;   

DOI  :  10.7717/peerj.11396

来源: DOAJ

【 摘 要 】

Bacteriophages are broadly classified into two distinct lifestyles: temperate and virulent. Temperate phages are capable of a latent phase of infection within a host cell (lysogenic cycle), whereas virulent phages directly replicate and lyse host cells upon infection (lytic cycle). Accurate lifestyle identification is critical for determining the role of individual phage species within ecosystems and their effect on host evolution. Here, we present BACPHLIP, a BACterioPHage LIfestyle Predictor. BACPHLIP detects the presence of a set of conserved protein domains within an input genome and uses this data to predict lifestyle via a Random Forest classifier that was trained on a dataset of 634 phage genomes. On an independent test set of 423 phages, BACPHLIP has an accuracy of 98% greatly exceeding that of the previously existing tools (79%). BACPHLIP is freely available on GitHub (https://github.com/adamhockenberry/bacphlip) and the code used to build and test the classifier is provided in a separate repository (https://github.com/adamhockenberry/bacphlip-model-dev) for users wishing to interrogate and re-train the underlying classification model.

【 授权许可】

Unknown