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WorMachine: machine learning-based phenotypic analysis tool for worms

BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elega...

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Autores principales: Hakim, Adam, Mor, Yael, Toker, Itai Antoine, Levine, Amir, Neuhof, Moran, Markovitz, Yishai, Rechavi, Oded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769209/
https://www.ncbi.nlm.nih.gov/pubmed/29338709
http://dx.doi.org/10.1186/s12915-017-0477-0
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author Hakim, Adam
Mor, Yael
Toker, Itai Antoine
Levine, Amir
Neuhof, Moran
Markovitz, Yishai
Rechavi, Oded
author_facet Hakim, Adam
Mor, Yael
Toker, Itai Antoine
Levine, Amir
Neuhof, Moran
Markovitz, Yishai
Rechavi, Oded
author_sort Hakim, Adam
collection PubMed
description BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. RESULTS: We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation. CONCLUSIONS: WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0477-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-57692092018-01-25 WorMachine: machine learning-based phenotypic analysis tool for worms Hakim, Adam Mor, Yael Toker, Itai Antoine Levine, Amir Neuhof, Moran Markovitz, Yishai Rechavi, Oded BMC Biol Methodology Article BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. RESULTS: We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation. CONCLUSIONS: WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0477-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-16 /pmc/articles/PMC5769209/ /pubmed/29338709 http://dx.doi.org/10.1186/s12915-017-0477-0 Text en © Hakim et al. 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Hakim, Adam
Mor, Yael
Toker, Itai Antoine
Levine, Amir
Neuhof, Moran
Markovitz, Yishai
Rechavi, Oded
WorMachine: machine learning-based phenotypic analysis tool for worms
title WorMachine: machine learning-based phenotypic analysis tool for worms
title_full WorMachine: machine learning-based phenotypic analysis tool for worms
title_fullStr WorMachine: machine learning-based phenotypic analysis tool for worms
title_full_unstemmed WorMachine: machine learning-based phenotypic analysis tool for worms
title_short WorMachine: machine learning-based phenotypic analysis tool for worms
title_sort wormachine: machine learning-based phenotypic analysis tool for worms
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769209/
https://www.ncbi.nlm.nih.gov/pubmed/29338709
http://dx.doi.org/10.1186/s12915-017-0477-0
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