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Growth Score: a single metric to define growth in 96-well phenotype assays

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we pre...

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Detalles Bibliográficos
Autores principales: Cuevas, Daniel A., Edwards, Robert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911387/
https://www.ncbi.nlm.nih.gov/pubmed/29686949
http://dx.doi.org/10.7717/peerj.4681
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author Cuevas, Daniel A.
Edwards, Robert A.
author_facet Cuevas, Daniel A.
Edwards, Robert A.
author_sort Cuevas, Daniel A.
collection PubMed
description High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.
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spelling pubmed-59113872018-04-23 Growth Score: a single metric to define growth in 96-well phenotype assays Cuevas, Daniel A. Edwards, Robert A. PeerJ Computational Biology High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline. PeerJ Inc. 2018-04-19 /pmc/articles/PMC5911387/ /pubmed/29686949 http://dx.doi.org/10.7717/peerj.4681 Text en ©2018 Cuevas and Edwards http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Computational Biology
Cuevas, Daniel A.
Edwards, Robert A.
Growth Score: a single metric to define growth in 96-well phenotype assays
title Growth Score: a single metric to define growth in 96-well phenotype assays
title_full Growth Score: a single metric to define growth in 96-well phenotype assays
title_fullStr Growth Score: a single metric to define growth in 96-well phenotype assays
title_full_unstemmed Growth Score: a single metric to define growth in 96-well phenotype assays
title_short Growth Score: a single metric to define growth in 96-well phenotype assays
title_sort growth score: a single metric to define growth in 96-well phenotype assays
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911387/
https://www.ncbi.nlm.nih.gov/pubmed/29686949
http://dx.doi.org/10.7717/peerj.4681
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