DevStaR: high-throughput quantification of C. elegans developmental stages.

Autores
White, Amelia G.; Lees, Brandon; Kao, Huey-Ling; Cipriani, P. Giselle; Munarriz, Eliana Rosa; Paaby, Annalyse B.; Erickson, Katherine; Guzman, Sherly; Rattanakorn, Kirk; Sontag, Eduardo; Geiger, Davi; Gunsalus, Kristin C.; Piano, Fabio
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.
Fil: White, Amelia G.. Rutgers University. Department of Computational Biology and Molecular Biophysics; Estados Unidos de América; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Lees, Brandon. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Kao, Huey-Ling. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Cipriani, P. Giselle. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Munarriz, Eliana Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Paaby, Annalyse B.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Erickson, Katherine. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Guzman, Sherly. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Rattanakorn, Kirk. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Sontag, Eduardo. Rutgers University. Department of Mathematics; Estados Unidos de América;
Fil: Geiger, Davi. New York University. Courant Institute of Mathematical Sciences. Department of Computer Science; Estados Unidos de América;
Fil: Gunsalus, Kristin C.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Piano, Fabio. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Materia
C. Elegans
High-Throughput Phenotyping
Developmental Stage
Object Recognition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/1639
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/1639
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling DevStaR: high-throughput quantification of C. elegans developmental stages.White, Amelia G.Lees, BrandonKao, Huey-LingCipriani, P. GiselleMunarriz, Eliana RosaPaaby, Annalyse B.Erickson, KatherineGuzman, SherlyRattanakorn, KirkSontag, EduardoGeiger, DaviGunsalus, Kristin C.Piano, FabioC. ElegansHigh-Throughput PhenotypingDevelopmental StageObject Recognitionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.Fil: White, Amelia G.. Rutgers University. Department of Computational Biology and Molecular Biophysics; Estados Unidos de América; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Lees, Brandon. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Kao, Huey-Ling. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Cipriani, P. Giselle. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Munarriz, Eliana Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Paaby, Annalyse B.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Erickson, Katherine. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Guzman, Sherly. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Rattanakorn, Kirk. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Sontag, Eduardo. Rutgers University. Department of Mathematics; Estados Unidos de América;Fil: Geiger, Davi. New York University. Courant Institute of Mathematical Sciences. Department of Computer Science; Estados Unidos de América;Fil: Gunsalus, Kristin C.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Fil: Piano, Fabio. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;Institute of Electrical and Electronics Engineers2013-10-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/1639White, Amelia G.; Lees, Brandon; Kao, Huey-Ling; Cipriani, P. Giselle; Munarriz, Eliana Rosa; et al.; DevStaR: high-throughput quantification of C. elegans developmental stages.; Institute of Electrical and Electronics Engineers; IEEE Transaction on Medical Imaging; 32; 10; 2-10-2013; 1791-18030278-0062http://dx.doi.org/doi:10.1109/TMI.2013.2265092enginfo:eu-repo/semantics/altIdentifier/url/http://www.ncbi.nlm.nih.gov/pubmed/23722463info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:02:18Zoai:ri.conicet.gov.ar:11336/1639instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:02:18.654CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv DevStaR: high-throughput quantification of C. elegans developmental stages.
title DevStaR: high-throughput quantification of C. elegans developmental stages.
spellingShingle DevStaR: high-throughput quantification of C. elegans developmental stages.
White, Amelia G.
C. Elegans
High-Throughput Phenotyping
Developmental Stage
Object Recognition
title_short DevStaR: high-throughput quantification of C. elegans developmental stages.
title_full DevStaR: high-throughput quantification of C. elegans developmental stages.
title_fullStr DevStaR: high-throughput quantification of C. elegans developmental stages.
title_full_unstemmed DevStaR: high-throughput quantification of C. elegans developmental stages.
title_sort DevStaR: high-throughput quantification of C. elegans developmental stages.
dc.creator.none.fl_str_mv White, Amelia G.
Lees, Brandon
Kao, Huey-Ling
Cipriani, P. Giselle
Munarriz, Eliana Rosa
Paaby, Annalyse B.
Erickson, Katherine
Guzman, Sherly
Rattanakorn, Kirk
Sontag, Eduardo
Geiger, Davi
Gunsalus, Kristin C.
Piano, Fabio
author White, Amelia G.
author_facet White, Amelia G.
Lees, Brandon
Kao, Huey-Ling
Cipriani, P. Giselle
Munarriz, Eliana Rosa
Paaby, Annalyse B.
Erickson, Katherine
Guzman, Sherly
Rattanakorn, Kirk
Sontag, Eduardo
Geiger, Davi
Gunsalus, Kristin C.
Piano, Fabio
author_role author
author2 Lees, Brandon
Kao, Huey-Ling
Cipriani, P. Giselle
Munarriz, Eliana Rosa
Paaby, Annalyse B.
Erickson, Katherine
Guzman, Sherly
Rattanakorn, Kirk
Sontag, Eduardo
Geiger, Davi
Gunsalus, Kristin C.
Piano, Fabio
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv C. Elegans
High-Throughput Phenotyping
Developmental Stage
Object Recognition
topic C. Elegans
High-Throughput Phenotyping
Developmental Stage
Object Recognition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.
Fil: White, Amelia G.. Rutgers University. Department of Computational Biology and Molecular Biophysics; Estados Unidos de América; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Lees, Brandon. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Kao, Huey-Ling. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Cipriani, P. Giselle. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Munarriz, Eliana Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Paaby, Annalyse B.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Erickson, Katherine. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Guzman, Sherly. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Rattanakorn, Kirk. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Sontag, Eduardo. Rutgers University. Department of Mathematics; Estados Unidos de América;
Fil: Geiger, Davi. New York University. Courant Institute of Mathematical Sciences. Department of Computer Science; Estados Unidos de América;
Fil: Gunsalus, Kristin C.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
Fil: Piano, Fabio. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América;
description We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.
publishDate 2013
dc.date.none.fl_str_mv 2013-10-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/1639
White, Amelia G.; Lees, Brandon; Kao, Huey-Ling; Cipriani, P. Giselle; Munarriz, Eliana Rosa; et al.; DevStaR: high-throughput quantification of C. elegans developmental stages.; Institute of Electrical and Electronics Engineers; IEEE Transaction on Medical Imaging; 32; 10; 2-10-2013; 1791-1803
0278-0062
http://dx.doi.org/doi:10.1109/TMI.2013.2265092
url http://hdl.handle.net/11336/1639
http://dx.doi.org/doi:10.1109/TMI.2013.2265092
identifier_str_mv White, Amelia G.; Lees, Brandon; Kao, Huey-Ling; Cipriani, P. Giselle; Munarriz, Eliana Rosa; et al.; DevStaR: high-throughput quantification of C. elegans developmental stages.; Institute of Electrical and Electronics Engineers; IEEE Transaction on Medical Imaging; 32; 10; 2-10-2013; 1791-1803
0278-0062
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.ncbi.nlm.nih.gov/pubmed/23722463
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781227894308864
score 12.982451

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