PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

PhageLeads : Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach. / Yukgehnaish, Kumarasan; Rajandas, Heera; Parimannan, Sivachandran; Manickam, Ravichandran; Marimuthu, Kasi; Petersen, Bent; Clokie, Martha R. J.; Millard, Andrew; Sicheritz-Pontén, Thomas.

In: Viruses, Vol. 14, No. 2, 342, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Yukgehnaish, K, Rajandas, H, Parimannan, S, Manickam, R, Marimuthu, K, Petersen, B, Clokie, MRJ, Millard, A & Sicheritz-Pontén, T 2022, 'PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach', Viruses, vol. 14, no. 2, 342. https://doi.org/10.3390/v14020342

APA

Yukgehnaish, K., Rajandas, H., Parimannan, S., Manickam, R., Marimuthu, K., Petersen, B., Clokie, M. R. J., Millard, A., & Sicheritz-Pontén, T. (2022). PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach. Viruses, 14(2), [342]. https://doi.org/10.3390/v14020342

Vancouver

Yukgehnaish K, Rajandas H, Parimannan S, Manickam R, Marimuthu K, Petersen B et al. PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach. Viruses. 2022;14(2). 342. https://doi.org/10.3390/v14020342

Author

Yukgehnaish, Kumarasan ; Rajandas, Heera ; Parimannan, Sivachandran ; Manickam, Ravichandran ; Marimuthu, Kasi ; Petersen, Bent ; Clokie, Martha R. J. ; Millard, Andrew ; Sicheritz-Pontén, Thomas. / PhageLeads : Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach. In: Viruses. 2022 ; Vol. 14, No. 2.

Bibtex

@article{1ffc821f96304c709f01f69a45afb479,
title = "PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach",
abstract = "The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.",
keywords = "AMR, Genomics, Lysogeny, Machine learning, Phage therapy",
author = "Kumarasan Yukgehnaish and Heera Rajandas and Sivachandran Parimannan and Ravichandran Manickam and Kasi Marimuthu and Bent Petersen and Clokie, {Martha R. J.} and Andrew Millard and Thomas Sicheritz-Pont{\'e}n",
note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2022",
doi = "10.3390/v14020342",
language = "English",
volume = "14",
journal = "Viruses",
issn = "1999-4915",
publisher = "M D P I AG",
number = "2",

}

RIS

TY - JOUR

T1 - PhageLeads

T2 - Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach

AU - Yukgehnaish, Kumarasan

AU - Rajandas, Heera

AU - Parimannan, Sivachandran

AU - Manickam, Ravichandran

AU - Marimuthu, Kasi

AU - Petersen, Bent

AU - Clokie, Martha R. J.

AU - Millard, Andrew

AU - Sicheritz-Pontén, Thomas

N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022

Y1 - 2022

N2 - The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.

AB - The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.

KW - AMR

KW - Genomics

KW - Lysogeny

KW - Machine learning

KW - Phage therapy

U2 - 10.3390/v14020342

DO - 10.3390/v14020342

M3 - Journal article

C2 - 35215934

AN - SCOPUS:85124545392

VL - 14

JO - Viruses

JF - Viruses

SN - 1999-4915

IS - 2

M1 - 342

ER -

ID: 300064357