Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACTThere is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent co...

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Bibliographic Details
Main Authors: Andrew J. Borchert, Alissa C. Bleem, Hyun Gyu Lim, Kevin Rychel, Keven D. Dooley, Zoe A. Kellermyer, Tracy L. Hodges, Bernhard O. Palsson, Gregg T. Beckham
Format: Article
Language:English
Published: American Society for Microbiology 2024-03-01
Series:mSystems
Subjects:
transposon insertion sequencing
RB-TnSeq
independent component analysis
machine learning
Pseudomonas putida
aromatic catabolism
Online Access:https://journals.asm.org/doi/10.1128/msystems.00942-23

Internet

https://journals.asm.org/doi/10.1128/msystems.00942-23

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