Sphae: An automated toolkit for predicting phage therapy candidates from sequencing data
Date
2025
Authors
Papudeshi, B.
Roach, M.J.
Mallawaarachchi, V.
Bouras, G.
Grigson, S.R.
Giles, S.K.
Harker, C.M.
Hutton, A.L.K.
Tarasenko, A.
Inglis, L.K.
Editors
Mulder, N.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Bioinformatics Advances, 2025; 5(1):vbaf004-1-vbaf004-12
Statement of Responsibility
Bhavya Papudeshi, Michael J Roach, Vijini Mallawaarachchi, George Bouras, Susanna R Grigson, Sarah K Giles, Clarice M Harker, Abbey L K Hutton, Anita Tarasenko, Laura K Inglis, Alejandro A Vega, Cole Souza, Lance Boling, Hamza Hajama, Ana Georgina Cobián Güemes, Anca M Segall, Elizabeth A Dinsdale, Robert A Edwards
Conference Name
Abstract
Motivation: Phage therapy offers a viable alternative for bacterial infections amid rising antimicrobial resistance. Its success relies on selecting safe and effective phage candidates that require comprehensive genomic screening to identify potential risks. However, this process is often labor intensive and time-consuming, hindering rapid clinical deployment. Results: We developed Sphae, an automated bioinformatics pipeline designed to streamline the therapeutic potential of a phage in under 10 minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators such as integrase, recombinase, and transposase, which could preclude therapeutic use. Among the 65 phage sequences analyzed, 28 showed therapeutic potential, 8 failed due to low sequencing depth, 22 contained prophage or virulent markers, and 23 had multiple phage genomes. This workf low produces a report to assess phage safety and therapy suitability quickly. Sphae is scalable and portable, facilitating efficient deployment across most high-performance computing and cloud platforms, accelerating the genomic evaluation process.
School/Discipline
Dissertation Note
Provenance
Description
Advance Access Publication Date: 17 January 2025
Access Status
Rights
© The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.