Literature DB >> 33575651

Rapid discovery of novel prophages using biological feature engineering and machine learning.

Kimmo Sirén1, Andrew Millard2, Bent Petersen1, M Thomas P Gilbert1, Martha R J Clokie2, Thomas Sicheritz-Pontén1.   

Abstract

Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

Entities:  

Year:  2021        PMID: 33575651      PMCID: PMC7787355          DOI: 10.1093/nargab/lqaa109

Source DB:  PubMed          Journal:  NAR Genom Bioinform        ISSN: 2631-9268


  7 in total

1.  Unifying the known and unknown microbial coding sequence space.

Authors:  Chiara Vanni; Matthew S Schechter; Silvia G Acinas; Albert Barberán; Pier Luigi Buttigieg; Emilio O Casamayor; Tom O Delmont; Carlos M Duarte; A Murat Eren; Robert D Finn; Renzo Kottmann; Alex Mitchell; Pablo Sánchez; Kimmo Siren; Martin Steinegger; Frank Oliver Gloeckner; Antonio Fernàndez-Guerra
Journal:  Elife       Date:  2022-03-31       Impact factor: 8.713

2.  DEPhT: a novel approach for efficient prophage discovery and precise extraction.

Authors:  Christian H Gauthier; Lawrence Abad; Ananya K Venbakkam; Julia Malnak; Daniel A Russell; Graham F Hatfull
Journal:  Nucleic Acids Res       Date:  2022-07-22       Impact factor: 19.160

3.  From Trees to Clouds: PhageClouds for Fast Comparison of ∼640,000 Phage Genomic Sequences and Host-Centric Visualization Using Genomic Network Graphs.

Authors:  Guillermo Rangel-Pineros; Andrew Millard; Slawomir Michniewski; David Scanlan; Kimmo Sirén; Alejandro Reyes; Bent Petersen; Martha R J Clokie; Thomas Sicheritz-Pontén
Journal:  Phage (New Rochelle)       Date:  2021-12-16

4.  Phenotypic characterization and analysis of complete genomes of two distinct strains of the proposed species "L. swaminathanii".

Authors:  Lauren K Hudson; Harleen K Chaggar; Claire N Schamp; Michelle L Claxton; Daniel W Bryan; Tracey L Peters; Yaxiong Song; Catharine R Carlin; Henk C den Bakker; Thomas G Denes
Journal:  Sci Rep       Date:  2022-06-01       Impact factor: 4.996

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

Authors:  Kumarasan Yukgehnaish; Heera Rajandas; Sivachandran Parimannan; Ravichandran Manickam; Kasi Marimuthu; Bent Petersen; Martha R J Clokie; Andrew Millard; Thomas Sicheritz-Pontén
Journal:  Viruses       Date:  2022-02-08       Impact factor: 5.048

6.  Evaluating Plant Gene Models Using Machine Learning.

Authors:  Shriprabha R Upadhyaya; Philipp E Bayer; Cassandria G Tay Fernandez; Jakob Petereit; Jacqueline Batley; Mohammed Bennamoun; Farid Boussaid; David Edwards
Journal:  Plants (Basel)       Date:  2022-06-20

7.  Isolation and characterization of a novel Lambda-like phage infecting the bloom-forming cyanobacteria Cylindrospermopsis raciborskii.

Authors:  Emmanuelle Laloum; Esther Cattan-Tsaushu; Daniel A Schwartz; Hanaa Shaalan; Hagay Enav; Dikla Kolan; Sarit Avrani
Journal:  Environ Microbiol       Date:  2022-02-15       Impact factor: 5.476

  7 in total

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