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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/1639
Ver los metadatos del registro completo
id |
CONICETDig_407202b33ded3ec243059d6f61043230 |
---|---|
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 Técnicas2025-09-29T09:34:16Zoai: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:34982025-09-29 09:34:16.404CONICET 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_ |
1844613059531767808 |
score |
13.070432 |