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WorMachine: machine learning-based phenotypic analysis tool for worms.


ABSTRACT: 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.

SUBMITTER: Hakim A 

PROVIDER: S-EPMC5769209 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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WorMachine: machine learning-based phenotypic analysis tool for worms.

Hakim Adam A   Mor Yael Y   Toker Itai Antoine IA   Levine Amir A   Neuhof Moran M   Markovitz Yishai Y   Rechavi Oded O  

BMC biology 20180116 1


<h4>Background</h4>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.<h4>Results</h4>We examined the power o  ...[more]

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