vHULK, a New Tool for Bacteriophage Host Prediction Based on Annotated Genomic Features and Neural Networks

Autor(es)

Amgarten, Deyvid ; Iha, Bruno Koshin Vazquez ; Piroupo, Carlos Morais ; da Silva, Aline Maria ; Setubal, Joao Carlos

• Tipo de documento: Artigo Científico
• Fonte: PHAGE-THERAPY APPLICATIONS AND RESEARCH; v. 3, n. 4, p. 9-pg., 2022-12-01.

Resumo

Background: The experimental determination of a bacteriophage host is a laborious procedure. Thus, there is a pressing need for reliable computational predictions of bacteriophage hosts.Materials and Methods: We developed the program vHULK for phage host prediction based on 9504 phage genome features, which consider alignment significance scores between predicted proteins and a curated database of viral protein families. The features were fed to a neural network, and two models were trained to predict 77 host genera and 118 host species.Results: In controlled random test sets with 90% redundancy reduction in terms of protein similarity, vHULK obtained on average 83% precision and 79% recall at the genus level, and 71% precision and 67% recall at the species level. The performance of vHULK was compared against three other tools on a test data set with 2153 phage genomes. On this data set, vHULK achieved better performance at both the genus and the species levels than the other tools.Conclusions: Our results suggest that vHULK represents an advance on the state of art in phage host prediction. (AU)

• Processo FAPESP: 11/50870-6 - Estudos da diversidade microbiana no Parque Zoológico do Estado de São Paulo
• Beneficiário: João Carlos Setubal
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOTA - Temático