High-Throughput, High-Precision Colony Phenotyping with Pyphe.
Cell viability
Colony
Fitness
Functional genomics
Growth curve
Large-scale phenotyping
Microbiology
Phenomics
Python software
Screen
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
6
5
2022
pubmed:
7
5
2022
medline:
11
5
2022
Statut:
ppublish
Résumé
Colony fitness screens are powerful approaches for functional genomics and genetics. This protocol describes experimental and computational procedures for assaying the fitness of thousands of microbial strains in numerous conditions in parallel. Data analysis is based on pyphe, an all-in-one bioinformatics toolbox for scanning, image analysis, data normalization, and interpretation. We describe a standard protocol where endpoint colony areas are used as fitness proxy and two variations on this, one using colony growth curves and one using colony viability staining with phloxine B. Different strategies for experimental design, normalization and quality control are discussed. Using these approaches, it is possible to collect hundreds of thousands of data points, with low technical noise levels around 5%, in an experiment typically lasting 2 weeks or less.
Identifiants
pubmed: 35524128
doi: 10.1007/978-1-0716-2257-5_21
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
381-397Subventions
Organisme : Wellcome Trust
ID : FC001134
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 200829/Z/16/Z
Pays : United Kingdom
Organisme : Cancer Research UK
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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